RTK: A Second Great Pinpointing

Next Thursday is the 25th anniversary of a watershed moment that paved the way for a proliferation of location-precise applications and mass adaption of modern positioning and mapping services. Referred to affectionately at the time as “The Great Pinpointing”, on May 1st, 2000, President Bill Clinton announced the end of “Selective Availability,” a deliberate signal degradation in the U.S. GPS system. Practically overnight, the accuracy of civilian GPS jumped from hundreds of feet to within about 20 feet—a massive leap that laid the groundwork for the modern mapping and navigation tools we now take for granted. From Google Maps to Uber, from Strava to Pokémon Go, nearly every location-based product or service we rely on today traces its origins to that moment. It was a sonic boom for innovation in the digital world.

Today, we’re on the cusp of another leap forward—one that could be just as transformative for the physical world automation stack: the broad deployment of a reliable, accessible RTK (Real-Time Kinematic) network.

RTK is a technique used to improve the accuracy of GNSS (Global Navigation Satellite System) signals, correcting for errors in real-time using data from stationary reference stations. While standard GPS accuracy typically hovers in the range of several meters, RTK enhances that to centimeter-level precision. That might sound like a minor upgrade—but for drones, autonomous mobile robots (AMRs), and countless robotics applications, it’s the difference between fantasy and feasibility.

RTK is Essential for Autonomous Technologies

In construction, agriculture, logistics, and beyond, the potential of drones and robots has been widely touted. But in practice, many of these use cases remain stuck in R&D limbo or limited to tightly geofenced pilot projects. The missing link? Absolute, precise, and persistent localization. GPS alone doesn’t cut it when you need to reliably locate a drone relative to the corner of a building, or a farm robot within a crop row.

RTK solves this. It enables a level of positional accuracy that allows drones to land autonomously on small charging pads, construction rovers to perform high-precision site scans without manual survey markers, and delivery bots to navigate sidewalks and drop packages at specific doors. It provides a globally understandable, centimeter-accurate positioning layer that’s essential when robots are expected to operate in open environments with minimal human oversight.

A Shared Infrastructure Layer, Like GPS

Just as GPS became a foundational utility for the digital world, a robust RTK network can become foundational for the physical automation economy. But unlike GPS, which is satellite-based and globally available, RTK requires ground-based infrastructure: fixed base stations that constantly stream corrections over a network to receivers in the field.

Until recently, RTK networks were either highly localized (e.g., private installations on a farm or jobsite) or prohibitively expensive. That’s changing. A number of companies and governments are now working to deploy broad, affordable RTK networks—either as a service or as open infrastructure. As this layer becomes widely available and easy to integrate with commercial-grade GNSS receivers, it will unlock a wave of autonomy use cases previously deemed too complex or unreliable to commercialize. There are also a variety of entrepreneurial efforts to build DePIN distributed RTK networks on blockchains as well as retrofit existing real-world infrastructure with RTK functionality.

Towards a Cambrian Explosion

When GPS-enabled smartphones hit critical mass, location-aware apps didn’t just become better—they multiplied. Similarly, we can expect a Cambrian explosion of real-world automation the moment RTK infrastructure crosses a threshold of coverage, reliability, and accessibility.

Autonomous agtech machines can plant and harvest with surgical precision. Drone-based inspection and delivery can scale across urban and rural environments. Warehouse robots can operate with consistent accuracy outdoors, not just inside tightly controlled facilities. Even consumer robotics—think lawn mowers or home delivery bots—stand to benefit.

RTK isn’t just a technical upgrade—it’s an infrastructure revolution. And like GPS before it, once it’s in place, it will seem impossible to imagine the modern robotics stack without it. For startups and enterprises building in autonomy, now is the time to pay attention. The pieces are falling into place, and those who understand the significance of robust RTK coverage will be best positioned to lead in this next frontier.

As we’ve seen with GPS, a persistent, robust, and open location-mapping network can transform industries and daily life. With RTK, we’re looking at the same kind of inflection point—only this time, it’s not only about where we are on a screen. It may well have the most significant impact on where our machines are in the physical world, and what they can do when they know it with centimeter-level confidence.

Open Source > Open Source

“There’s more to Michael Jordan than basketball; there’s more to basketball than basketball.”

- Michael Jordan

This is one of my favorite quotes. My AmLit professor in college introduced it on the first day of class to encourage us to pursue planes of abstraction beyond, beneath, and in between the written words of the stories we’d read towards unearthing and wrestling with meaning and comprehension apart from the explicit or literal. I’m reminded of this quote often and have been returning to it recently in thinking about the value of open source technology models and the intent of openness as the rapid proliferation of artificial intelligence continues. Open source, at its best, is much more than just a publicly available, free source code base. The traits, functions, influences, and effects that together make up that much more of open source, may have a distinct and essential role in shaping our AI future.

If the past three decades of internet history have taught us anything, it’s this: open ecosystems—not walled gardens—catalyze explosive growth, innovation, and global access.

In the early 2000s, the decline of AOL’s tightly controlled web experience coincided with the rise of open browsers like Firefox and, eventually, Google Chrome. This shift allowed users to explore the internet on their own terms—outside of AOL’s curated portal—and marked a turning point in internet freedom and adoption.

Fast forward a few years, and the launch of Android OS—built on open-source Linux—ignited an unprecedented surge in global mobile internet access. Its open architecture enabled manufacturers around the world to produce affordable smartphones, connecting billions and accelerating the internet’s reach far beyond the desktop.

And during the rise of cloud computing, open-source tools like Kubernetes redefined enterprise software infrastructure. Kubernetes, originally developed by Google and released as open source, became the backbone of modern cloud-native applications—powering everything from startups to Fortune 500s.

Each of these milestones illustrates a clear pattern: the internet grows, thrives, and reaches new heights when its building blocks are open. Today, as we stand at the threshold of a new AI era, the same lesson applies.

AI systems—especially large foundation models—are poised to reshape economies, governance, creativity, and daily life. But much like the early internet, their long-term societal value hinges on openness. This isn’t just a technical debate—it’s a foundational choice about who gets to shape the future. Openness in AI offers a cascade of extraordinary benefits. These include:

• Transparency and Accountability: Closed models hinder scrutiny and amplify concerns about bias, discrimination, and opacity. Open models allow public researchers, watchdogs, and everyday users to better understand how decisions are made—and who made them.

• Democratic Access to Knowledge: When model weights, training data, and code are open, students, educators, and developers anywhere in the world can learn, experiment, and innovate. This lowers barriers to entry, decentralizes expertise, and broadens participation.

• Unexpected Innovation and Public Interest Use Cases: Just as open internet protocols gave rise to Wikipedia, peer-to-peer networks, and WhatsApp, open AI models will enable “out-of-left-field” applications—from rural education tools to climate modeling software—that closed systems would never prioritize.

• A More Competitive Ecosystem: Open tools help level the playing field. They enable smaller startups, nonprofits, and academic teams to build and iterate without relying on exclusive access granted by dominant players. In doing so, openness actively counters market consolidation and encourages competition.

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None of this is speculative—it’s historical precedent. Open-source software powers the web servers that host our sites, the operating systems on our phones, and the frameworks our apps are built with. Companies like Google, Meta, Amazon, and even Microsoft (once the emblem of closed systems) all rely on and contribute to open-source communities. The AI stack should follow suit.

Of course, openness isn’t a silver bullet nor just a given. Openness must be pursued thoughtfully, with safeguards against misuse, attention to deployment environments, and proper governance. But dismissing open AI models as inherently “dangerous” misses the mark. As with any transformative technology, the marginal risks of openness must be weighed against its structural benefits—including transparency, innovation, and equitable access. We don’t yet know what the most transformative AI applications of the next decade will look like. But if history is any guide, they’ll come not from behind closed doors, but from the edges—from open systems that invite experimentation, learning, and remixing.

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The more that I think about it, I tend to believe that the strongest case for open-source AI isn’t just technical or economic—it’s human.

First, open-source projects thrive not only because they are accessible, but because they attract dynamic communities. These communities form in layers—core contributors, frequent users, evangelists, fans. That network of affiliation drives a kind of cultural flywheel. When the product is strong, this layered engagement doesn’t just sustain it—it propels it. The popularity and loyalty that grows around open-source tools isn’t just a byproduct of utility; it’s a function of taste, trust, and shared ownership. These affinities can be the fuel which propels a company to breakaway velocity. And in a world where pace of adoption and community momentum often predict market success, that’s an advantage no proprietary tool can easily manufacture.

Second, open source enables real-time, collective learning from mistakes. Contributors build on each other’s work, flag issues publicly, and iterate fast. This capacity for shared error correction and the gains therein is essential—especially in AI, where models must be not just powerful, but appropriate, accurate, and reliable. This open-source advantage in particular has been a bit of a blind spot for certain current closed AI systems, where mistakes are just eliminations; the priority is to be correct — to give the right answer, make the optimal move, or find the best outcome. Yet, as the level of abstraction increases — especially with messy, real-world decisions — what is “correct” often becomes contextual, subjective, and even debatable. Within the complexities of nuance and ambiguity and shades of grey, there’s intrinsic value in being wrong — making mistakes, reflecting on them, and learning through the process. Historically, that’s been a very human advantage: our ability to grow through trial and error.

What’s most exciting about emerging multi-agent AI system architectures is their potential to engage more like collaborators — not just as oracles of correctness, but as systems that can learn, adapt, and evolve through interaction, feedback, and even failure. This shift — from optimization to exploration, from perfection to progress — could mark a fundamental turning point in how AI systems develop and how we work alongside them.

Cleave to Chaos

At a time when artificial intelligence evolves by the hour and societal systems are strained, it’s easy to fall into the trap of binary thinking—progress vs. peril, order vs. chaos, human vs. machine. It’s also tempting to spin out in thinking that we’re just fodder for overwhelming bedlam. But what if the truth—and the path forward—lies in accepting and engaging disorder itself?

Few thinkers captured the spiritual and philosophical tension of technology better than John Perry Barlow, who stood at the demiurgic intersection of counterculture idealism and digital frontierism. He reminds us that the internet is not just a tool; it’s a movement, an idea, a space for radical freedom. As AI begins to occupy a similar space in our cultural consciousness, it’s worth revisiting Barlow’s vision to help us make sense of this new frontier. Barlow believed that chaos was not to be feared, but rather to be invited. Decentralized systems and self-organizing networks are not bugs of the digital age, but features—manifestations of a more organic, democratic, and ultimately human order.

Curiosity of intent over correctness of outcome is an algorithm for which fascinating, bizarre, and unpredictable are inherent. Where the patterns, hunches, and ordering dynamics that emerge may seed the systems of our future’s future. These are the traits of chaos, in theory and in demonstrable practice. The venomous saliva of the North American Gila monster was passing factoid from David Attenborough in Desert episodes of nature programs until the GLP-1 hormone in that saliva delivered one of the most significant breakthroughs in biochemistry and human healthcare in decades.

The progress of AI today echoes that same unpredictability. We now build models we can’t fully explain. We deploy them in open systems where outcomes can’t be forecasted. While this raises real ethical and safety concerns, it also challenges our assumptions about control, authorship, and intelligence itself. Such challenges aren’t signs of doom—they are marks of evolution.

To embrace AI through this lens is to reject rigid hierarchies in favor of permissionless experimentation. It is to question centralized power, whether in the form of massive language models trained behind closed doors or governments scrambling to write the rules of a game they barely understand. It is to ask: Who gets to decide what intelligence means? And who owns the outputs of a machine that has learned from all of us?

Barlow’s optimism wasn’t naive —it stemmed first from a deep belief in human agency and collective intelligence And it endured despite deep skepticism of surrendering organization and control to any entrenched 3rd party actors, especially in the form of governmental regulatory protection. That same suspicion could be leveled at today’s tech monopolies who promise to keep us safe from the very tools they are racing to build. There is a comforting delusion of existing structures which obscures the opportunity to begin relying on that which will emerge.

Of course, chaos can also be dangerous. There is a fine line between open exploration and reckless annihilation. We don’t cede our responsibilities — as an individual, a collective, a firm, or a corporation — of participation and enabled involvement with artificial intelligence or any other technologies. But if we view AI as a force of entropy alone, we miss the deeper opportunity: to build new institutions, norms, and narratives that reflect the complexity of the world we actually live in.

Crucially, chaos isn’t a formless void. It is rich with nascent principles and ordering elements. Such points and patterns — odd vessels and thickets of magnetic draw — come to the fore in that space of possibility, potential, and fervent activity. Think: fibonacci sequence. Now more than ever, artificial intelligence and machine learning enhance and advance the power of distributed information networks and bottoms up structure, access and involvement. Individuals and firms, as smaller actors loosely meshed, engage with fluidity through which they can come together for one instance, spin off, and then reform for another.

Maybe the goal isn’t to tame AI into perfect alignment, but to co-evolve with it—curious, alert, and a little off-balance. That posture of humility and openness may be precisely and absolutely essential to the purposes of AI and our access to and involvement with these technologies. It creates a zone of experimentation, not one of perfect outcomes, but of shared authorship. AI, too, could be essential towards that zone—if we let it.

The future won’t be shaped by those trying to nail down final answers, but by those willing to dance with uncertainty. Maybe we should ask not only what AI can do, but what it allows us to become—as thinkers, as builders, and as a society. That may require letting go of some control, welcoming chaos, and trusting that from the friction of uncertainty, our strange, new world might emerge.

In every new medium, morning, meeting, and maelstrom, there lies wild possibility. It’s up to us—imperfect, poetic, merry pranksters —to shape it.

The Inventor’s Antidote

“To me there is no creativity without boundaries. If you're going to write a sonnet, it's 14 lines, so it's about solving the problem within the container.”

- Lorne Michaels

SNL is an paramount example of the paradox that creativity (original ideas) and innovation (their real-world applications) thrive not in an expanse of unlimited possibilities but within the tension and discomfort of constraint.

Two sacrosanct mandates of the show’s original design propel it. 1). Every show is created and produced from scratch each week and 2). it goes live at 11:30pm Saturday night. No matter what. Within the six-day window, the cast, writers, designers, directors, and crews have freedom and agency to experiment and adapt as sketch brainstorming transitions to producing a 90-minute live show. The mammoth deliverable looming when the clock runs out is an adversarial means of demanding collaborative focus to succeed.

Several other examples have stuck with me from creative fields:

Tierra Whack turned her self-described “scatter-brain” and the 60-second time for Instagram videos in 2016 into a wildly singular and successful debut album made up of fifteen, 1-minute tracks. For his directorial debut, Jordan Peele embraced a tiny budget and fewer resource inputs, developing a process which repositioned funding limitations and conventional inputs as levers for new creative possibilities, which wouldn’t emerge without them.

At the heart of every startup is a problem—the galvanizing reason for its existence. The problem is itself a form of constraint. It sets the contours and guardrails of founders’ journey to solve it, from which much creativity and innovation may flourish. Along the way, further limitations — time, funding, bandwidth, competition, market dynamics, shifting incentives, personal sacrifices — sharpen thinking, compel unconventional problem-solving, and drive urgency. In a first principles framing, it could be said that creativity and innovation born out of constraint is the self-evident purpose of startups and entrepreneurship.

The power of this dynamic may be most clear if it’s removed, when an early-stage company’s original constraints begin to shrink or disappear altogether. Abundance is a burden. Largesse invites distraction. For startups, this is commonly a profusion of funding that’s multiples more than the most generous runway projections. Bigger isn’t necessarily better. When everything or anything may be seen as an option or taken for granted, it’s easier to mistake possibility for necessity and motion for progress. Invention and impact become fleeting. Creativity needs friction. Innovation needs urgency. 

To embrace addition by subtraction is to encourage success because of constraints and to keep them alive long after they’ve first disappeared. For founders, this isn’t about thwarting hyper-growth or avoiding entropy, it’s about sustain a culture of bold, high-velocity innovation over time. Often the most inspired work emerges along the edges of a boundary, against the confines of a clock.

Hollywhere?

This year’s Oscars delivered a strong night for independents and Conan the best — I’m glad he’ll be back as the host next year. However, a different story has been simmering along Hollywood Boulevard recently, as film and television creators have scattered to the worldwide winds in search of, amongst other things, less expensive productions. At the dawn of the U.S. film industry, LA hosted ~ 82% of worldwide film productions, and as recently as 2018, it still represented roughly 50% of where all films were made. In 2024, that percentage stands at 26% and shrinking. What should be made of LA’s Hollywood heart starting to see other people?

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LA and by proxy Hollywood have had a brutal half-decade. A series of adversarial forces, some of their own making (SAG-AFTRA and WGA strikes) and others unavoidably external (covid-19 and wildfires), have punched a hole through the postcard perfect allure of La La Land. It’s a blow that’s laid low the area’s essential, aspirational function — in being the place where cinematic dreams are made:

While these difficulties may have served as accelerants, its clear that the diasporic spread of filming destinations was already underway before them and very likely would have continued without them. As the two charts below make clear. For Q4 2016, LA topped out at more than 10,000 shoot days. Fast forward to Q4 2024 and the total LA shoot days have declined by nearly 50%:

How did this happen? It’s cheaper to shoot outside of LA. Cheaper how? more generous tax incentives elsewhere.

In the past 10 years, many other states and entire countries have entered the competitive production tax incentives fray. What began as individual cities and jurisdictions leaning into the economic potential of movie tourism — the long-tail boost to local tourism activity that can result when a popular film features that locale — has evolved into broad and highly competitive tax incentive packages to lure productions of every size to shoot within their borders.

This map shows those in detail and the link to a series of interactive maps that drill-down to each detail for states and countries:

The breakdown of these packages, their evolution, and enormous influence deserves an entire post, but what it boils down to right now is the ability of a production to save (by taxation rebate) as much as 50% of an entire budget. That means, in relative terms, as much to a $1M indie as it does a $300M major studio film.

The devil is in the details for a specific project shipping out to the UK, Australia, Canada, or Texas, including things like budget caps and minimums and the various categories of incentive. The big picture truth though is that its not longer SoCal or nowhere for productions, as regional and international hubs continue emerge.

Clockwise from top-left: Monmouth, NJ, Yonkers, NY, Albuquerque, NM, Mexico City, Mexico, Perth, Australia, Borehamwood, UK, Long Island City, NY, Athens, GA, and Atlanta, GA.

At a glance, one could be forgiven for mistaking the above photo collage as a tour of LA’s Studio City. In fact, it represents major studio lots and sound stages far afield of LA and California, all built or having broken ground over the last two years. With studios investing major physical infrastructure in new locales, it seems this would be more permanent shift than passing trend.

An indie producer friend of mine has confirmed this shift in the day-to-day reality of pitching, producing, and completing everything from fortune 500 commercials to independent shorts:

“We’re down in Mexico City all the time now for shoots, and nearly as often in Toronto. The travel is a thing, but the infrastructure in terms of crews, spaces, and services has really built up in these locations and they matter in winning pitches for projects no doubt.”

So, this all may be a product of basic cost-benefit analysis. It proves out in a look back at LA’s past. The primary reason LA blossomed into Hollywood was the arrival at the turn of the century of eager, independent producers seeking freedom from the costly controls of Thomas Edison’s Motion Picture Patents Company. Having invented the first film cameras, Edison sought to force all subsequent productions to pay high licensing fees to use his equipment and to require his approval before beginning to shoot.

California was literally as far as they could get in country on land from the NJ-based patent offices and enforcers, as well as a place where labor was cheap, land was cheaper, and restrictions were basically non-existent. In the succeeding decades as studios, services, and talent relocated to SoCal permanently, it stayed that way, in relative terms, thanks to maintaining the most beneficial tax incentives to film and tv production. Until ten years ago, when other locales decided they could compete and their provincial governments agreed.

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I have a few loosely connected thoughts at the end that I’m just going to put here…

Diversity through expansion is a good thing. Though many of these productions are flown in and out for the tax benefit, an expansion of popular and sustainable filming centers inevitably will lead to a diversification of voices and minds involved in those productions — from PAs to award-winning directors. It may not happen overnight, but anything that improves accessibility for newer and different participants, stories, and ideas is a positive.

LA-Hollywood is not going to disappear. It’s not as if California is removing or reducing its film and TV tax incentives. In the short-term, one can certainly argue that job creation in new production hubs is zero-sum for L.A. As new job opportunities emerge in Yonkers and Mexico City, that may mean fewer in L.A. But that’s only in the immediate short-term. The entertainment industry is still reacting to the aforementioned 5-year crippling of overall productions and so revenue shortfall as a result of fewer completed projects. That lower booked revenue has led to a contraction in newly green-lit productions. This too shall pass. Zooming out to a longer view of consumers’ insatiable appetites for content and the demonstrable ability of new technologies to gradually reduce the cost of production, suggests surplus work will be available in many production hubs. As long as there are new stories to tell.

Dorothy Parker once said that, “Los Angeles is 72 suburbs in search of a city.” Maybe due to its sprawling horizontal bounds or the absence of centuries of compiled cultural heritage, L.A. has long felt like a sort of geographic cypher. For a long time, this fit Hollywood like a glove. Where a city-as-cypher could frame the many cinematic stories created there. Perhaps in parallel with the emergence of new production hubs, a diminishing of novel narrative cinema made locally has turned the cypher on itself. The cypher of a cypher has given us endless the promulgation of reality tv and the preeminent trend of famous for being famous —both elevated to high consumption status over the preceding two decades. Much of this programming originated in and around Hollywood’s hills, depicting the high-end glitz and not much depth. If we’re being honest, its more Dorian Gray vapid than So Meta cool, and hardly emblematic of Hollywood’s cinematic highs.

The truth is, there are established cultures and existing identities tied to Los Angeles. In the long-ignored immigrant and minority communities, the vast majority of the city’s multi-generational population continues to proudly identify as angelenos. Maybe then, a cohesive way to call more productions back to LA would be to empower and involve those communities, as collaborators, casts, crews, storytellers and creators. A new sound stage-studio lot — one that employs local teams and maybe even has employee equity — Altadena seems like a great place to start.

Blender Flow

“Science flourishes best when it uses freely all the tools at hand, unconstrained by preconceived notions of what science ought to be. There is great satisfaction in building good tools for other people to use. Every time we introduce a new tool, it always leads to new and unexpected discoveries.”

-  Freeman Dyson, The Scientist as Rebel

I saw the animated film Flow recently, from Latvian director Gints Zilbalodis. The movie is a visual feast for which I can’t think of any existing comparison. The last time I experienced something this fresh in theaters, this little guy hopped on screen for the first time, broke the 4th wall, and Toy Story ushered in the unprecedented era of computer animation via Pixar and DreamWorks.

In listening to and reading interviews with Gints about his creative process and the technologies he relies on, I was struck by just how thoroughly Flow is a product of two tenets recognizable across many versions of innovation on the periphery, including tech startups: powerfully open-source and creativity from constraints.  In combination on Flow, these two elements provided unbridled experimentation and open, intentional collaboration, through a designed environment that evinced breakthroughs.

Powerfully Open Source

Flow is the first feature-length animated film to completely made in Blender, a 3D computer graphics software. First and foremost, Blender is an extremely powerful product suite, it a deep-frontier tech leader, consistently releasing some of the most dynamic new capabilities at the edge of the $35B 3D computer graphics industry. With Blender, Gints could visualize and experiment in his imagined environment much earlier on in development:

“I can take the virtual camera and explore, almost like location-scouting in a live action movie. I need to go through that process and try different things. It’s a very spontaneous and kind of intuitive process. It also has a real-time render engine, EEVEE, which means you can actually see what you’re making—you don’t have to wait for the rendering to see all the lights, textures, fog, and effects.” [i]

Equipped with a creative canvas that provides frictionless trial and error of potential shots and angles in real-time with no additional cost. This is the live-action equivalent of digital cameras introducing immediate playback on set, supplanting the rote process of waiting for printed dailies in traditional filmmaking. Blender is also free and open source, which was critical in creating Flow. The technical and creative teams on the film could develop bespoke tools on top of Blender’s foundational code:

“Blender is very customizable, we were able to build tools within Blender to realize unique elements in our film, in particular water. People say to avoid water because it can be really hard to get it into the right shape that you want. With Blender we built one tool for when the ocean is very active and a completely different system for an underwater scene, or for a puddle, or just a splash of water. I think it’s amazing that there’s a feature film of this scope being made on a software that anyone can just try out for themselves. I could travel and work on the film on my laptop without a crazy workstation.” [ii]

There are many established 3D graphics modeling products, including AutoDesk Maya, Houdini, and ZBrush. The majority are closed source systems, slow to innovate and offering minimal customization. Gints switched from Maya to Blender for its superior technology, its open-source malleability, and for the emergent network of technologists and animators building with, around, and on top of Blender.

“In 2020, we secured some funding, and I moved into a co-working studio space with other artists and developers who were using Blender. That’s where I connected with Mārtiņš Upītis and Konstantīns Višņevskis. It quickly became clear Mārtiņš had a deep expertise in water. He’d had already been researching water simulations and eventually developed a Blender add-on for water effects. Konstantīns was a Blender super-user and was much more technical than me, so I could ask him for advice. He did a lot of the rigging and handed smaller simulations like splashes.” [iii]

Community is perhaps the most underappreciated value accelerator that’s commonplace in open-source offerings catching fire. Impossible to fake and earned over time, a passionate, involved community advances capability, shared understanding, and shared contribution in a step-function vortex that’s further propelled by the compounding nature of affinity for and familiarity with the brand behind the technology.

This was literally true with Flow, where core members of the movie’s creative and production team came organically from Blender’s active community. Several contributors have gone on to build powerful add-ons for purchase in the Blender marketplace. If open source can create a limitless, supercharged blank canvas to rapidly explore and experiment, a passionate and involved community can play an essential part to fan the sparks, and even guide development as direction takes shape.

Creativity from Constraints

Like many indie films, Flow had a tiny production budget: $3.5M total. For comparison, another 2024 Oscar-nominated, animated film – the studio feature sequel Inside Out 2 – had a $200M production budget. So how could something as groundbreaking as Flow result? By not just learning to do more with less, but seeing what may be less through one lens, for the more it may bear through another.

“Storytelling offers infinite possibilities, but sometimes constraints can be beneficial. For example, deciding to use only four characters and a handful of locations can lead to stronger creative choices. Having fewer resources was helpful too, because we had to focus, we couldn’t make any big changes late in the process.” [iv]

The examples of creativity from constraints are myriad in creative industries. Like the accelerative nature of open-source, creativity from constraints rings true for entrepreneurs and founders as it does for filmmakers. In fact, the dynamic could be said to be the elemental purpose of startups at their foundation. Of course, the parallels between startup founder and filmmaker are only so many. Where a movie is a temporal start-to-finish process with a bespoke end product, building a company takes place over stages of evolution and growth. Learning to embrace, seeking out, or artificially build structures of constraint in one form or another are a means of recreating early ideation environments that foster innovative sparks, once a company is in manifest motion towards growing into itself.

“I think it’s valuable for filmmakers to collaborate with tool developers early on to understand which things are challenging and which are easy. This can actually spark a continuum of creative ideas throughout production rather than feeling like an ongoing limitation.” [v]

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Just as many of Blender’s frontier innovations are products of its earned open-source community, Flow is sine qua non Blender. Without it, Flow may not have ever been made.

Go watch Flow!

Gints’ Quote Links:

[i] Gints Zilbalodis, Hollywood Reporter Interview, 10.13.24

[ii] Gints Zilbalodis, Fast Company Interview, 11.22.24

[iii] Gints Zilbalodis, Blender Blog Interview, 1.22.25

[iv] Gints Zilbalodis, Roundtable Interview, Hollywood Reporter, 1.9.25

[v] Gints Zilbalodis, Blender Blog Interview, 1.22.25

What's Best For Me?

Health and wellness for individuals, much like beauty, cosmetics, and even fashion, often operate on an inverse incentive system between suppliers and consumers. Marketers, media, and product companies don’t always benefit from making us truly informed. Instead, they thrive when we keep searching - prioritizing transactions over outcomes - and stay in a loop of trial and error. Essentially “just buy something — procedure, pill, or powder — and if that doesn’t work, come on back and buy something else.”

Take a walk through any GNC storefront to see this irl. The setup is designed to look quasi-professional—fitness-oriented branding, walls lined with glossy supplement bottles, vague claims of performance enhancement, each with a Sam’s Club-level bulk discount offer. Everything looks exactly the same. The experience can be as intimidating as going to indie movie rental stores used to be, not because your selections are being judged, actually because they aren’t. In the absence of any informed, engaged guidance, trust has eroded and left individuals adrift amongst indistinguishable, indecipherable aisles. This trust vacuum is hardly limited to preventative nutrition and wellness in healthcare industries. The systems are built to keep you patient (buyer), not necessarily to get you to a point where you may no longer need their products thanks to long-term effectiveness and healthier outcomes.

The real question many individuals may actually want help with in making these purchases isn’t just, “what should I take?” but rather, “how do I know what actually works for me?” This requires two critical capabilities: first, the ability to clearly express my own specific health, fitness, or performance needs, and second, the ability to assess—objectively and quantitatively—whether something I put into my body is delivering real, measurable benefits. Luckily, individuals are beginning to reclaim more self-determined agency in their healthcare journeys, via complimentary technological advancements from wearable health trackers to biopunk cybernetics. The user and so patient-centric nature of the open internet, its vast stockpiles of health and healthcare data, from clinical trials to group-text step counting, is now met with AI capable of transforming that data from unknown, inaccessible or obfuscated, to clear, relevant, and actionable on a individualized and personalized level in real-time and on demand.

For example, if I start taking a new supplement, how do I know if it’s working? Who else has tried this? How did it go for them? Are there noticeable changes in energy, sleep, recovery, or cognitive function? How long do I need to take it in order to experience and gauge its impact? And beyond subjective feeling, is there any data that can validate these effects? What markers should I be looking for? The same goes for diet—how can I track whether I’m actually getting the right nutrients, not just in theory, but based on how my body responds over time?

We’re starting to see companies emerge on the periphery of legacy healthcare and wellness that take a more data-driven, personalized approach. Ingredient.ai is a new company utilizing AI to analyze supplement ingredient experimental and clinical study and research data on supplement ingredients to deliver coherent, current and actionable insights for supplement R&D. Such AI-driven enabling layer development will allow providers to pass along that transparency, individualization to proactive patient individuals in the form of more effective and understood products. Such data platforms leave open the opportunity for new services and applications developed on top of them to further implement and facilitate patient-directed, personalized user interactions and experience. SuppCo is another recently launched startup that helps rebuilding trust in supplementation through community-building. Users can share their own supplement histories, experiences, and suggestions alongside clear and concise industry indicators and rating systems. Trust in shared community is extremely powerful in a social context. Fostering community around this data, where user-generated explanations, shared experiences, and real-world results can coexist alongside clear, accurate, and digestible information can transform the individual adrift at GNC into an empowered, informed, self-determined participant.

I’m curious about solutions that provide a similar level of AI-ML analysis and design-conscious accessibility for other personalized healthcare categories and in different forms and functions. For instance in diet and nutrition, something that doesn’t just count macros, but helps individuals understand nutrient intake over time in a meaningful, science-backed way. Genuine, active health communities could be especially powerful for individuals dealing with chronic conditions, autoimmune disease management or long-term, undiagnosed health issues. This mix of data-driven insights and community-driven wisdom could create a much stronger foundation for truly understanding our health and how we can feel better.

(Inter)National Football League

Hot off the three-peat that wasn’t, the NFL is letting the rest of the world know it’s coming for it. In recent weeks, Ireland, Spain, and Australia have agreed to host future games, which adds to the 4 international games hosted by the UK and Germany (3 & 1) each season. Commissioner Roger Goodell has said his goal is 16 international games every year, while also dreaming of international franchises and a Super Bowl abroad not out of the question. So, what’s going on here? Is America’s favorite sport really putting down roots outside its borders? Does the gameplay suggest a mismatch beyond novelty?

The NFL is a finely tuned machine, dominating live entertainment with its carefully designed gameplay and earned status. Its down-by-down format and rule evolution maximize excitement, making it compelling whether casually in the background or intensely analyzed for hours. With an average of 17.5 million viewers per game and $16 billion in annual broadcast revenue from virtually every major network and streamer in 2024, it commands primetime slots and weekend days for a third of the year. Often described as both Shakespearean and gladiatorial, the NFL stands unrivaled—not just in live sports, but in live U.S. broadcasts of any kind.

When you own your foundational market - and growth being ever-expected to track up and to the right - it stands to simple reason that you go looking outside of it to greener pastures for new audiences, fans, users. Look no further than the New and Upcoming original content lists from Netflix, Amazon, or HBO Max, its mostly foreign-language series and movies for markets with many more yet-subscribed potential viewers. For the NFL, new growth may only seem viable beyond U.S. borders.

But does it really translate? The NFL boasts sell-out crowds and rave reviews at international games, suggesting untapped markets worldwide. However, packed stadiums mask a key issue: these games are the least watched of the season, likely dragging 2024’s average viewership into an uncharacteristic dip. The game itself doesn’t change abroad—airtime does. The oceans flanking the U.S. add 8-9 hours of separation and multiple time zones (except for games in Central and South America), making a 9:30 AM Sunday kickoff in the UK far less appealing than a familiar afternoon slot. While full stadiums contribute to the NFL’s spectacle, ticket sales account for just 15% of league revenue—the bulk comes from massive broadcast deals. Experiments are fine when they’re purely additive, but speculative expansion that undercuts a dominant, proven model feels both risky and self-defeating.

Also buried beneath strong international turnout is the question of whether it stems from the novelty of a one-off event or signals a sustainable foreign fanbase. A cynic might argue that fans attend simply because they rarely get the chance, not because they’re future season ticket holders for an 18-game schedule. The telling fact that U.K. crowds go wild for kickoffs, field goals, and extra points—while often wearing random jerseys, regardless of the teams playing—suggests novelty plays a major role. There’s a loose but insightful parallel with U.S. soccer: American fans pack stadiums for Premier League exhibition matches here but show far less enthusiasm for midseason MLS games:

While much of the data challenges the financial case for a permanent slate of international NFL games, it also makes Roger Goodell’s long-standing push for overseas franchises even harder to justify. Talent is non-negotiable in pro sports, and football’s brutal physical toll demands recovery time—something extensive travel only worsens. Betting lines already favor home teams when opponents cross U.S. time zones; an intercontinental schedule could cripple international franchises. The NFL has floated the idea of European teams composed of U.S. players relocating for the season, but the logistical hurdles are massive. How would in-season trades, cuts, and free agency work? What about taxes, visas, families, and schooling? I’m sure sharper minds are tackling these complexities, but one reality remains: NFL players hate international games:

As entertainment, a pro sports league succeeds as a product of scarcity of talent and competition built up from broad, local familiarity with the sport. Wtf does that mean?

Scarcity of talent is what allows the NFL to credibly deliver the most elite players in the sport. Roster spots are reserved for the best of the best, with an exceptionally high bar for entry. This monopoly on an extremely limited resource has enabled the NFL to dominate while every attempt at a rival pro football league—from the AFL to the XFL and NFL Europe—has failed. It’s also why MLS, despite modest growth, still lags far behind the powerhouse soccer leagues of Europe and South America; they lack top-tier talent and struggle to attract it, especially in its prime.

The NFL, by contrast, draws from a massive domestic talent pipeline, with NCAA Division I football serving as a highly competitive, built-in farm system for decades. But with elite players already pushed to their physical limits each season—and, as Pat McAfee suggests, unwilling to buy into international expansion—developing a comparable talent pipeline abroad would be essential. And here lies the problem: American football has little to no foothold internationally. More on that shortly.

Scarcity of competition is a hot-button issue for the NFL, which has steadily added more games to team schedules—a topic that extends beyond international expansion. However, it’s worth considering here, as additional regular-season games may be necessary to make an international schedule feasible. Competition scarcity is driven by simple supply and demand: the NFL has created perpetual, pent-up demand by controlling supply, earning unprecedented compensation for its product. With only 17 regular-season weeks and games once a week (plus one Monday Night Football), the frequency slightly increases at season’s end and during the playoffs when games matter most. The NFL has perfected this balance to date, echoing P.T. Barnum’s wisdom: always leave them wanting more. If international expansion results in more games, the NFL risks disrupting this delicate equilibrium, potentially diluting its once-unassailable product across too many competitions. The struggles of the other big four sports—MLB (162 games), NBA (82), and NHL (82)—highlight the downside of oversupply, which erodes audience demand until the postseason.

Broad, local familiarity with a sport is a consideration for which the NFL, in pursuing a top-down approach to taking the NFL global, is missing the forest for the trees and stepping into a foundational void. As hyperlinked in the paragraph before last, American football participation has no meaningful presence abroad, at any level of competition, from peewee to college. Perhaps unsurprisingly, despite the launch of an international player pathway program in 2023, the NFL is made up almost entirely of U.S.-born athletes:

The degree to a switch is generally recognizable in a given country such that a significant mass of individuals have had first hand experience playing it or watching someone they know play, may be a most viable indicator of its potential to produce a sustainable fanbase of its professional level.

In the U.S., lots of kids grow up in little league, playing pickup basketball, travel hockey, club soccer, or peewee football, and their parents shuttle them to and from games. In high school, young people play and watch these sports, the same goes for college. It’s hardly surprising that baseball, basketball, ice hockey, and football are four of the five most popular sports in America. The enduring presence of a sport across age groups, life experiences, and socioeconomic categories creates a shared base from which the supremely gifted may ascend to professional level and everyone else may become a fan, from casual to diehard.

Football is big business from high school through pros in the states and nearly ubiquitous to American culture. Much like the makeup of its active rosters, NFL fans are primarily U.S.-based, compared to nascent if not de minimis real fanbases outside the U.S., where foreign citizens’ exposure to football, by and large, begins and ends with the elite NFL:

Goodell and the NFL hq may have visions of a gridiron version of the global explosion the NBA experienced in the early 1990s thanks to the star power of one Michael Jordan and the ‘92 Dream Team. That was a moment in time and there aren’t any reasonable 2025 NFL comps. However, the NFL braintrust could take a different page from the NBA’s book as far as bringing their game to a global level.

In 1990, NBA rosters were predominantly mono-national, with 95% of players from the U.S. Around the rise of Air Jordan and the Dream Team, the NBA implemented a long-term strategy to gradually diversify its players and fanbase internationally. Success didn’t rely on establishing new teams in foreign cities or moving chunks of the regular season overseas. The approach was intentionally grassroots, starting with expanding exposure to and engagement with basketball at local levels—from recreational leagues and community pick-up games to developmental programs for foreign athletes. Many of these initiatives are still thriving over 30 years later, including the NBA Academy, Basketball Without Borders, and the use of foreign-born players as global ambassadors. NBA games abroad are treated as exciting, novelty events, with one-off international exhibitions held outside the regular season.

Today, 75% of the NBA’s social media followers are international fans, global viewership has exploded, significantly outpacing the foreign audience growth of the other Big Four U.S. pro leagues, and nearly one in every 4 current NBA players was born abroad:

There are, of course, differences in what’s required to play organized football compared to basketball. Football in its full-contact, padded form requires a specialized field, expensive equipment, and huge rosters of participating players. Two hoops, a ball, a few players and a ref can make a genuine basketball game. However, unless the NFL recognizes that global legitimacy requires more from the bottom-up than just apex pro spectacle, it may well remain just the top tier of American football.

Truth Seekers

Something really wonderful happened a few days before the inauguration. Immediately following Joe Biden’s use of the term oligarchy in his final address, google searches for the definition of the word oligarchy spiked — becoming a trending search term for the first time since 2004, when they started making that data public. Rather than choosing a cynical reaction, I actually see this as such a positive, redeeming signal. Evidence that people still want to understand things, especially things they didn’t know about before.

Truth is simply the best basis from which we can, and must, continue to ask: why? Why are things this way, and what can we do, build, examine, and discover to change, evolve, improve, or further understand about it. Truth, in the most objective or at least broadly intersubjective sense, is born out of data, opinion-less information recorded from empirical measurement and primary source observation. The internet is an immense compilation of databases and data sources with communicating webs built on top. Indeed, the first iteration Tim Berners-Lee’s World Wide Web was a uniform system for locating and retrieving information from diverse, curated databases.

The information superhighway it most certainly is. Part of the challenge is the superhighway has grown to be a criss-crossing mega-maze of available avenues to travel that individual navigation can be daunting. No human is reaching the end of the internet at this point. Another, more palpable development hurdle is the established economic model underlying much of the user experienced internet, digital display advertising. Maximizing performance in this model has meant that software algorithms that decide our news feed are programmed to prioritize user attention to optimize for engagement, which means optimizing for outrage, anger and awe.

These are undeniably engaging emotions. They are also exhausting and, as can sometimes be the case, can depend on obscuring truth, which may be more mundane, expected, frustrating, embarrassing or even just a buzzkill. But shock emotions aren’t the only ones that elicit sustained engagement. Learning, truth seeking, does too. Ask anyone who’s enjoyed a fascinating journey through wikipedia rabbit holes for hours or anyone who’s pursued an advanced degree, or written a thesis, or dissertation, or high school U.S. history term paper. Learning stuff is extremely engaging. Sharing discoveries and the shared experience of new things are bedrocks of social networks.

In his new book Nexus: A Brief History of Information Networks from the Stone Age to AI, Yuval Harari argues that in the societal balance between truth and order, necessary elements of functionally open and transparent progress are self-correcting mechanisms. In the absence of existing powers that be having any interest in adjusting their powerfully profitable algorithms, where could technology provide if not self-correcting, a correcting mechanism option? A host of services which on the surface could rhyme with truth seeking emerged in startupland the wake of the 2016 election, positioned as tools to fight disinformation online. Many relied on user-installed browser extensions and human fact-checking. A long list of them is here, many no longer exist or pivoted towards reputation management for existing entities online. Human bias and the associated cost of human fact checking likely make mechanical turk models ineffective. It’s also probably a bridge to far to ask users to install browser extensions to solve for missed truths that may be subconscious or at least not as obvious as oligarchy? in and amongst the raging news feed.

We are though at a fascinating, potent moment regarding the original access point for the internet — the browser. This to me is where either through feature set or entire standalone product design, truth via data could become a UX/UI reality. There are several prominent startups building transformative, groundbreaking web browsers. Many of the most dominant internet browsers already contain automatic product features that aim to give us the most engaging and transparent manifestation of each page we click to load. The wayback machine preserves the historical record of any address, google translate resolves foreign language pages instantly, cookies connect the user dots and pinpoint user location in relation to the specifics of a site or page. Many of the most widely integrated and mass adopted AI models and agents are already doing objective data search across unrelated sources and complex analysis at the top of search engine results to give users the answer they were looking for without having to click another link. The database infrastructure and emergent AI application power are clear and present and could combine to passively scan the articles, posts, comments, tweets, headlines, and breaking news we consume to be available to answer in-scroll questions like What does this mean? Is this really happening? What’s “x” topic all about? designed in ways that aren’t introducing new friction or distraction to end users.

The truth is out there. In fact, it’s often right there. And when we seek it out, we learn stuff, like what even is an oligarchy? Sometimes, we just need a technology-enabled nudge to be aware of our blind spots and fill them in with fascinating new knowledge along the information superhighway towards the truth.

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note: the banner image used is from the short-lived, excellent and hilarious series Truth Seekers on amazon prime video.

Antitrust History Repeats

In the mid-1990s, Microsoft was embroiled in a series of high-profile antitrust lawsuits that fundamentally reshaped the technology landscape. The U.S. government accused Microsoft of using its dominance in the PC operating system market to stifle competition, particularly through the bundling of its Internet Explorer browser with Windows. This move was seen as a direct attempt to marginalize competitors like Netscape Navigator. The litigation culminated in a landmark ruling that curtailed Microsoft’s anti-competitive practices, opening the door for innovation and competition in the tech ecosystem. One of the most significant beneficiaries of this shift was Google, a nascent search engine at the time. By the early 2000s, Google had emerged as the dominant force in internet search, capitalizing on the competitive opportunities created by Microsoft’s weakened monopoly.

Today, history appears to be repeating itself, with Google now in the crosshairs of antitrust regulators worldwide. The U.S. Department of Justice and multiple state attorneys general have launched lawsuits against Google, alleging that it leverages its dominance in search and online advertising to suppress competition. At the heart of the issue are Google’s agreements with device manufacturers and browsers to make its search engine the default choice—a practice reminiscent of Microsoft’s bundling of Internet Explorer. These lawsuits aim to dismantle Google’s control over key digital markets, much as the Microsoft case sought to level the playing field in the 1990s.

This unfolding legal battle presents a unique opportunity for innovative web browser startups to emerge and thrive. Much like Google’s rise was facilitated by the antitrust litigation distraction and resulting measures taken against Microsoft, new entrants could capitalize on a Google’s current antitrust quagmire and a post-Google landscape where barriers to entry are lowered, and competition is actively encouraged. Startups with fresh ideas around privacy, user experience, or decentralized web technologies could gain traction as users and regulators alike seek alternatives to Google’s ecosystem.

Similarly, there is growing consumer interest in transparent, collaborative, and design-centric products that break from the traditional mold. Startups like Arc from The Browser Company and SigmaOS exemplify this trend, offering tools that prioritize user creativity, seamless teamwork, and aesthetically engaging interfaces. These companies are reimagining the browser as a platform for productivity and connection, appealing to an audience that values both functionality and form. As antitrust litigation forces a reevaluation of dominant browser ecosystems, such innovative approaches could capture the attention of users seeking fresh, empowering alternatives.

Another parallel lies in the growing consumer demand for privacy-focused solutions. Just as Netscape’s vision of an open web inspired early adopters, today’s internet users are increasingly drawn to platforms that prioritize transparency and data protection. Startups like Brave and DuckDuckGo have already gained footholds by offering privacy-centric alternatives to Google’s services. Should antitrust litigation force Google to alter its practices or divest certain assets, these and other emerging companies could accelerate their growth by appealing to a broader audience.

Moreover, the antitrust scrutiny could spur innovation in browser technology itself. Much of Google’s dominance is tied to its Chrome browser, which integrates seamlessly with its suite of services. New players could challenge this model by developing browsers that are interoperable with a diverse range of applications, breaking the silos that Google has carefully constructed. The rise of open-source platforms and advances in artificial intelligence will further empower startups to redefine what a web browser can be, much as Google once redefined internet search.

The lessons from the Microsoft case are clear: when monopolistic control is dismantled, innovation thrives. The current antitrust lawsuits against Google have the potential to catalyze a similar wave of disruption and creativity, particularly in the web browser market. By addressing the entrenched dominance of today’s tech giants, regulators are creating fertile ground for the next generation of innovators to flourish. If history is any guide, we may be on the brink of a transformative era in which new players rewrite the rules of the digital ecosystem.

Antediluvian Climate Data

There’s a legacy problem in the climate space that’s ripe for technology-enable solutions. Along a well-trod Web 2.0 avenue, far from the technology frontier, it’s a data sharing problem. Since we get daily weather forecasts from apps with instant updates, one might assume that atmospheric weather data flows into global climate models in near real-time too. It doesn’t. In fact, the data that informs the scope and scale of measurable climate change currently is more than a decade old.

This was a key takeaway from an article by climatologists Gavin Schmidt and Zeke Hausfather in the NYTimes at the end of last year. As the piece makes clear: “the data that went into the latest round of climate model simulations are based on observations that only run through 2014…Similarly, the forecasts are stuck with scenarios that were common in the early 2000s.” It’s now ten years and counting since 2014, when bitcoin was $310 and all our headphones still had wires. A lot has changed. There’s little analysis of data that’s ten years old through which we could find our 2025 selves, let alone project our future global circumstances. The ten hottest years ever happened in the last decade, along with 204 natural disasters declared in the United States costing $1.4 trillion.* None of this data is in current climate models. The article continues, “to fix this means more comprehensive and faster data gathering from satellites. This needs to be matched by a commitment by the 30 labs worldwide that maintain the earth climate system models to update their simulations to reflect the latest data.”

Sometimes, the existence of dynamic problem-solving technologies are taken for naturally doing so once suitable applications are known. Adoption and saturation are not automatic of course and fixes for climate data sharing may be found via “picks-and-shovels” or “plumbing” tools which successfully aided the proliferation of the SaaS solutions over the past 15 years. These supplemental services streamline interconnection, especially as conduit to data-heavy end products like global climate simulations. Fortunately, there are data infrastructure and ecosystem maps that are overflowing with startup logo solutions focused on transforming unstructured data and disparate data silos into interoperable shared data systems available and accessible to any authorized participant. Standard elements like robust APIs, cloud computing, and distributed data warehouses should all accelerate the pace of climate data retrieval and sharing.

This is not to suggest that delays in the bureaucratic exchange of information across languages, borders, and globally dispersed organizations can be fixed in technology-enabled finger snap. But communication breakdowns, prioritization challenges, and misaligned timeframe expectations are largely human-to-human problems. Problems that can be fixed with existing, out of the box technology products today — and have been in many industries populated by their own opaque, glacial, bureaucratic companies and organizations.

My friend Sanjiv, Co-Founder and CEO of Texture, recently wrote a post calling for collaborative data partnership amongst clean power sources in order to advance and strengthen renewables viability on the grid in aggregate. I believe many of his points regarding standardization and efficiency born out of ecosystem-wide collaboration could be viable and transformative for data distribution and latency status quo that hamstrings global climate analysis today.

One last thing, this gigantic climate data system will absolutely benefit from the AI-ML frontier. It just needs the data first. Without the data — or anywhere near current data — there aren’t worthwhile inputs for agentic AI to analyze or for LLMs to be trained on. With those inputs up to date, I anticipate multimodality classification will improve the accuracy of climate model forecasts, while climate model simulations will include multi-agent systems that capture complex climate interactions, including decision-making scenarios based on economic incentives, policy regulations, and social norms. But that’s for another post.

Network-working

Networking is a career concept that invites and may evoke cringe — visions of transactional glad-handing and superficial self-interest in and amongst name tags, cocktails or breakfast pastries. Nevertheless, the notion that it’s all about who you know seems to prove out. Modern digital networks tend to build from known interactions and connections. LinkedIn, for example, is a generally complete reflection of real-world professional connections and the start-to-present CV of each of those people (including you). However, the combination of the accuracy of each user’s digital record, the willingness of users to become new connections, and a given means of communicating with each connection at any time, suggests immense, underutilized data value, that’s created as the network itself transforms and expands.*

Working world networks are perpetually reshuffling. The individual connections move and change, increasing the likelihood of new, unexpected, or unknown intersections, overlaps, and alignments. Professional serendipity is inevitable, and its unique value is potent. What’s missing are the timely suggestions to act — reach out and exploring something unanticipated and perhaps valuable. This is because the new data that shifts a connection and triggers new serendipitous potential isn’t readily brought to our attention. Yet with all of the other necessary data specifics accurately mapped and openly available (the Who, What, When, and Where), it stands to reason that innovative efforts to provide the awareness of and justification for communicating, connecting, re-connecting (the Why) would be worthwhile to create.

We demonstrate the need or desire to find and engage with specific types of connections through other avenues. Twitter is often a platform megaphone for those “hoping to connect with X types of startup founders in Y sector” and many private listservs are replete with one-to-many “looking for X candidates in Y location” broadsides. The demand is often time-sensitive and resource-limited. Why not tap into at least an additional means of surfacing high potential, high relevance contacts from existing networks if not technology enabled to anticipate certain connections’ relevant potential before you need to ask via AI-ML data classification and interpretation analysis running in the background?

There are interesting companies and projects building in and around this space, including those deploying multi-agent systems to unlock the value of being more, and more genuinely, connected. While I’ve used LinkedIn as an example, multi-modal analysis of other professional networks, email providers’ Contacts databases, or potentially messaging apps in various combinations could unlock similar value in via relevant or customized models. Boardy is a cool example, which deploys NLP via telephony voice assistant to intake specifics of what a given user is working on and runs those specifics against an internal network to surface unique contact recommendations. An ambitious project is The Network Of Time, which seeks to visualize, through organic submission, the largest network of people who appear together in photos that currently exist which can be connected through peoples' recurring appearances in different photos.

I hope that there are and will be many more entrepreneurs taking cracks at turning missed connections into newly connected (or re-connected) sparks of potential communication and collaboration. We’re all better when we’re more together.

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*Note: LinkedIn’s means of communicating element is not InMail but an email address. Nearly 90% of LinkedIn profiles provide an email to 1st degree connections, most often their current work email or active personal Gmail.

Artem ex Machina

collaborative creativity and AI’s avant-garde

. . .

Reading many headlines today, you’d be forgiven for thinking that our machine overlords have finally arrived to take all that we hold dear. Artificial intelligence and machine learning have come up and showed out in powerful and public ways in 2024. The existential gasp regarding the perceived threat these technologies represent has been especially loud from the entertainment business and the creative fields that make it go.

The lightning speed and high quality of generative AI output in its current iteration displays new computational creativity that can seem overwhelming. When faced with such immense power, it is a common human response to retreat to the familiar and established, while associating the immense new technology with a potentially destructive force that threatens what came before it. Having debuted to the masses in something of a whirlwind, today’s generative AI has caused collective cognitive dissonance between wonder and worry – a reactive emotional paradox that feels particularly acute as we actively participate in its growth. The technology’s output and concerns about that output abound. AI alternate endings to classic films saturate TikTok. AI-powered Wes Anderson-style takes on the casts of Harry Potter, Game of Thrones, and the MCU pervade Instagram feeds. Guardrails and carve-outs for AI have become critical topics adding fuel to the WGA and SAG-AFTRA strikes. That the current capabilities of these technologies have provoked an existential gasp from creative industries is not surprising. These are fields of artwork and entertainment – for these purposes film and television, music, and writing – within which the human artist has always been revered as the original, singular intelligence essential to the creative process. Long gestating as deep technology R&D and on the furthest cusps of entrepreneurial innovation, this moment is an inflection point for artificial intelligence and machine learning in entertainment. For these creative fields in particular, it’s a paradigm shift in the making. To better understand the new AI-ML reality suggests an exploration of both the profound contours and unprecedented capabilities of the current creative moment as well as the interdependent history of technological innovation and the entertainment industry. In so doing, hopefully this essay can provide a more optimistic lens and clear framing through which to see and engage AI-ML present and future as collaborative accelerants distinctively equipped for the rapidly evolving creative environment emerging all around us.

Note 1: I’m not making any claim of certified qualification in these realms of technology or creativity beyond my own evolving points of view, borne out of the continued pursuit of an education in and experiences of these technologies. This essay presents an optimistic perspective focused specifically on the growing presence of AI-ML in creative fields. Even for someone whose career has centered on technology and innovation, this is all so new. Right now, the contours and concerns of an AI-ML paradigm shift are defined by abundant and consequential unknowns. Certain crucial issues regarding AI-ML in other industries and the implications for society writ large fall outside of the parameters of this essay. The concept of inflection points, for technology and creative expression, inspired this essay and helped center much of the research. I have to thank my friend Mike Dempsey for his piece a few years ago going deep On Inflection Points, which sparked said inspiration. He is a curious, brilliant, and thoughtful guy and everyone should read all of his writing.

Note 2: Even with this focused scope, forward consideration for emergent AI-ML issues regarding copyrighted work and real artist performances must be addressed. Using AI to replicate human actors’ likeliness in future content, indefinitely and without proper approval and compensation, is not acceptable. AI-ML creative engines cannot continue to be trained using real artist-created content without consent. 100% AI-ML generated content must be able to be identified as such. On a cautiously positive note, priority action is being directed to the rapid development of regulations, evolved consideration of copyright laws, and new opt-in compensation models for artists’ past portfolios and future works and performances. The WGA and SAG-AFTRA have both made AI-ML concessions deal-breaker priorities in their on-going strike negotiations with studios. On the heels of a White House summit regarding AI guardrails in late July, OpenAI and Google and five other major tech corporations agreed to implement watermarks for all AI-generated content, enhancing security and authenticity awareness. These issues will continue to require deep, perpetual examination, always moving towards mutually acceptable resolutions and paths to sustainability for all willing and active participants.

Note 3: Buried beneath headlines like those at the top of this essay and more often altogether ignored is the fact that not all AI is generative. Generative is absolutely the most broadly public facing example available to most people right now. As such, it may not be surprising that generative is the only one of the four distinct types of machine learning that has its own Wikipedia page.  However, there are indeed four distinct types of machine learning and related artificial intelligence: classification, interpretation, prediction, and generation. In terms of their real-world application, each is powerful enough to handle an array of complex tasks within their categorical function. Each has discernible, high potential use-cases across a variety of stages in the cinematic, musical, and publishing creative processes. Throughout this essay, when not specifically addressing one of these four categories, I refer to AI-ML as a singular technology for the sake of simple syntax.

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Creepy Completeness

A disconcerting trait of generative AI is its ability to return finished products, or what very closely resemble finished products, from simple input prompts. Whether that’s detailed visual imagery from engines like Stable Diffusion or nuanced narrative and comedy writing from Chat-GPT, the wholeness and perceived polish of generative AI’s creative output is remarkable, even jaw-dropping, when rendered in the blink of an eye. Putting human margin doodles and stick figures to shame, these results feel excellent, professional. This perception underlies certain human discomfort right now. In crafting whole creative assets on demand, the demonstrable capabilities of generative AI could eliminate the time and toil of human minds, historically creativity’s essential ingredient. Artists wrestling with the blank canvas or writers staring at the blank page could feel obsolete when said canvas and page need only be blank for the milliseconds of compute time it takes for generative AI models to process prompts.

Underneath the very real concern for sustained human working value, there may be a more visceral trigger in human nature that generative AI pulls. Instant AI output of human-created quality provoking an unsettled human reaction rhymes with another techno-centric developmental concept common in humanoid robotics and certain areas of computer animation and virtual reality. The uncanny valley suggests that humanoid objects that nearly yet imperfectly resemble actual human beings elicit uncanny or strangely familiar feelings of uneasiness in observers. [See the movie version of Cats to immediately experience it or the movie Ex Machina for a nuanced exploration of the subject.] While not exact comparisons, similarities are apparent. The uncanny valley reaction is essentially a distorted mirror effect. It’s eerie to be in the presence of precise human physiology rendered through inorganic objects with uncanny accuracy. The creative output of generative AI provokes a similar instinctive discomfort by delivering completed versions of human artistic work of nearly comparable quality. Though not nearly as slick a name as uncanny valley, I’ll call this related concept “creepy completeness” in AI-human creativity.

Two considerations that can help assuage creepy completeness to reconcile human-centric creativity in a future creative paradigm pulled forward in part by AI-ML. First, just reiterating Note #3 above. Creepy completeness and an array of AI-only creative outputs are products of generative AI and limited to this type. That’s not to diminish what this single category of AI is proving capable of creating. However, it’s important to separate the collaborative potential of all categories of creative AI-ML from the initial disruption of generative AI right now. Second, we remain in the proverbial driver’s seat of these technologies development and their presence in creative production. Generative and otherwise, human engineers build and train these sophisticated models and human creatives determine the developmental inputs and drivers of each creative process. Both groups of humans share an ever more obvious responsibility to delineate the purpose, scope, and function of AI-ML as a cooperative asset in shaping the creative future.
 

Another Tool in the Kit

An antidote to creepy completeness exists in an evolving perspective on AI-ML technology so delineated along a tactical axis. Where AI products prove uniquely capable as point solutions within a human-centric creative process, their use will be a boon to creators eager to maximize their own creative thinking. As mechanisms of unprecedented functional efficiencies, AI-ML can enable human creatives to work at superior levels of abstraction. Seen this way, these innovative tools applied to specific production tasks can elevate human creativity — affording more space for imagination and original thought to initiate and drive those processes. In a recent interview with Variety, director Steven Soderbergh expressed a similar sentiment through a judicious and pragmatic lens:

“I may be the Neville Chamberlain of this subject, but I am not afraid of A.I. in this specific context. It has no life experience. It’s never been hungover. It’s never made a meal for anybody it loved. It’s just another tool. If it helps you finish a first draft of a script, great. But can it finish that thing and make it great on its own? Absolutely not.”

- Steven Soderbergh, interview with Variety, 6.12.23

This point of view is instructive for AI-ML across all artistic mediums. Seeing AI-ML as another tool, one that’s definitely useful but hardly a replacement for human intuition and originality is thoroughly practical and absolutely sufficient. The imaginations of directors and all creators are limitless — ever seeking to do more, say more, bring more to life in their form. Its the creative tools that at once enable art to work and define its boundaries. In this way, Soderbergh expresses openness to the unique additive value of a new technology as a tool in service to a greater cinematic vision. It’s an egalitarian perspective on the functional means of innovative technology shared by many of the most highly regarded, groundbreaking filmmakers of all time.

Throughout history, technology has expanded creative and professional opportunities for artists dramatically, by providing newer and more powerful tools for artists. The advent of new technologies often causes fears of displacement among traditional artists. In fact, these new tools ultimately enable new artistic styles and inject vitality into art forms that might otherwise grow stale. These new tools also make art more accessible to wider sections of society. This dynamic is paramount to interconnected nature of filmmaking and new technology. The history of film is filled with artist-tinkerers, as well as teams of artists and technologists. Together they’ve advanced the techno-cinematic frontier, often resulting in symbiotic inflection points that accelerated nascent technology and redrew the bounds of cinematic possibility. Walt Disney adopted and advanced the use of the multi-plane camera as well as breakthrough technologies in motion picture color and sound recording. Novel types of camera lenses enabled many of Orson Welles’ groundbreaking cinematographic techniques. The development of cheaper, portable camera and audio hardware facilitated the unbridled experimentation of the New Wave era, heavily influencing landmark directors including Stanley Kubrick, George Lucas, and Francis Ford Coppola. In more recent history, two seismic techno-cinematic inflection points stand out: the arrival of CGI special effects and the shift to digital filming. Both inflection points display distinct characteristics in achieving transformational impact, a close examination of which can contribute to building a better, more informed view of the potential next order effects to come from AI-ML.

Digital Inflection Points in Cinematic History

Part One — Special Effects: Practical to CGI

In the early 1990s, computer-generated imagery in movies arrived — not with the current thunder of AI-ML, but in no less consequential a manner for the future of filmmaking. Beyond a few notable spot uses in the 70s and 80s, fledging CGI was a limited afterthought for minor finishing techniques, primarily adding motion blur realism to stop-motion sequences. Then James Cameron and Steven Spielberg turned to the cutting edge of digital computer graphics innovation to realize audacious visions for two landmark films: Terminator 2 and Jurassic Park. In production, these movies actively advanced the dynamic capabilities of nascent CGI technology. The blockbuster theatrical runs of both movies elevated mainstream awareness of the new creative possibilities that CGI made possible. The in-depth stories of these technical breakthroughs manifest in pre-production of these films are fascinating and can be enjoyed here. However, the abridged version is this, Jurassic Park and T2 would become the first movies to feature 100% digitally rendered main characters, the T-1000 liquid villain and the dinosaurs of Isla Nublar. At premiere, these movies opened to box office records; both would remain among the top 30 highest grossing films of all time for the next 25 years. In an audacious and rare technological two-step, these two films combined to trigger an extraordinary inflection point for the novel technology – initiating the step-function changes which created production-level CGI in Hollywood and instantly winning the global acclaim and approval that cemented their cinematic value. Within a year, Pixar would release Toy Story, the first 100% digitally rendered film, debuting a new form of animated movie that would dominate the next 30 years. A year after that, Peter Jackson started pre-production on The Lord of The Rings trilogy while the Wachowskis did the same on The Matrix. CGI continues to redefine filmmaking across genres and production budgets, unleashing ever-expanding creative possibilities. Today, the technology is virtually omnipresent.

CGI’s inflection point in the entertainment industry is a valuable precedent for how creative industries may understand and engage with novel technologies to achieve a progressive creativity that doesn’t sacrifice or subjugate human centrality. In examining the impact of CGI over time, several transformative distinctions in film and TV become clear. These next-order effects can help frame how we perceive the directional purpose of AI-ML anchored to the intent and expectation that the technology assists, enhances, and expands the creative process and its achievable possibilities.

1. Cost-Effective, Time-Efficient: While modern CGI can require significant upfront investment, it often proves to be more cost-effective in the long run compared to practical effects, which are physical in origin and linear in production. Creating complex sets, building elaborate physical props, and executing dangerous stunts can be time-consuming, expensive, and impractical. CGI allows filmmakers to achieve similar results digitally, reducing production costs, and minimizing safety risks. Where the Jurassic Park contained two small line items for CGI, the T-Rex animatronics alone took up nearly 25% of the movie’s $56M budget. CGI also eliminated many of the additional production schedules required with traditional effects — offering faster turnaround times and enabling edits to occur in real-time post-production alongside live action edits. With practical effects, there are often delays in constructing and setting up physical elements, such as props or models. Additionally, practical effects may require multiple takes or adjustments to achieve the desired result, further extending the production schedule. In contrast, CGI allows for quicker iterations and adjustments, speeding up the overall production timeline, with no additional shooting or editing time or budget required.

2. Creative Force Multiplier: CGI unlocked profound functional freedoms and new paradigms of creativity in the cinematography, direction, and editing of visual mediums of entertainment. In terms of imaginative visions fully realized, CGI can create anything the human mind can conceive. Facilitating creative output without the limitations imposed by physical constraints, CGI is a tool of unprecedented power expanding the bounds of storytelling and world building. Once an onerous and exacting process divorced from filming, editing with CGI is instantaneous, malleable, and frictionless. This CGI distinction elevated post-production effects editing to a multidimensional creative complement and companion to the initial production. Advancements in CGI technology have led to highly realistic and visually stunning results. The level of detail, texture, and overall visual quality achievable with CGI has continued to improve, enabling the creation of virtual characters, environments, and effects that are indistinguishable from reality. Without CGI, we wouldn’t have Wall-E or Wakanda, Gollum or Groot, or innumerable other examples, from short to feature length. The finished results — often astounding, at times meh — always reinforce the scale and scope of the new creative paradigm that CGI brought to entertainment.

3. Consummate Co-Creation: CGI in entertainment continues to exemplify a successful and deliberate creative partnership between a technical innovation and incumbent entities and elements. CGI seamlessly integrates with live-action footage, allowing for the combination of real actors, sets, and props with digital elements. This integration enables filmmakers to blend practical and digital effects, enhancing the overall visual experience and expanding storytelling possibilities. CGI can enhance practical effects and vice versa in the same sequence. Terminator 2 and Jurassic Park were the first two films to debut this effects collaboration. Cameron and Spielberg recognized that practical effects and CGI would work best in tandem and completed post-production through creative meritocracy — using whichever technique best achieved each shot for the film. While this isn’t to make a claim that old and new work together in perfect harmony always and forever, the symbiosis is a lesser known, yet notable pattern characteristic of past technical inflection points in creative industries. Rather than rendering incumbents outmoded, transformative new technologies have a discernible history of embedding within or alongside existing participants, workflows, products, and systems in a collaborative sync. These powers combined tend to deliver creative output more capably and completely than those incumbents did beforehand, or than the new technology could alone. In the case of CGI, it also further fostered creative collaboration between film and tv production teams and technologists and software developers.

4. Accessibility Unlocked: Counterintuitive to the immediate perception of disruptive innovations at technical inflection points, CGI technology has been a remarkable plus to aggregate human employment. The availability and accessibility of CGI has increased over time as the tools, software, and FX library databases have standardized. While advanced CGI techniques were once limited to large studios, the democratization of technology has made CGI more accessible to independent filmmakers and smaller productions. This has unlocked opportunities for a wider range of filmmakers to explore and incorporate CGI into their projects. Similarly, CGI tools’ ease of use and lower cost to learn has reduced specialized barriers, making entertainment careers more feasible for many more creative individuals to pursue. For industry incumbents on active productions, CGI has been a multiplier of job creation. As the charts below detail:

Source: StephenFollows.com

In fact, outpacing every other major production department on top 200 grossing films each year, VFX teams on on domestic feature films have grown 325% on average from 1997 to 2020. Contrary a vocal human concern about AI-ML, history would indicate that the zero-sum fear of new technology retiring humans broadly is unfounded. And in most cases, the lasting impact beyond the technical inflection point is substantial new job expansion and growth within the new creative paradigm.

Part Two — Shooting Motion Pictures: Film to Digital

Six years after the ascent of CGI, digital technology arrived that could replicate if not supplant the physical medium of the motion picture itself, celluloid film stock. At the turn of the millennium, major improvements in the usability and video quality of professional studio digital cameras unleashed digital cinematography, editing, and distribution. This would turn out to be a monumental turning point for Hollywood, which had spent ninety years beholden to physical film stock as its creative canvas. The entire industry revolved around movies shot on real film and seen in theaters on real film. Where CGI caught fire at a fledgling development stage, early digital cinematography debuted to friction, backlash and resistance in the late 90s. Prominent directors believed the soul of cinema resided in 35mm film stock. Audio-visual manufacturers sought to protect their lucrative business as suppliers of the specialized, expensive equipment necessary to shoot, process, edit, and distribute physical film. Movie theaters exclusively equipped with traditional film reel projectors were reluctant to consider the upfront cost of adding a digital system or converting entirely to digital projection. Though first feature-length film entirely shot and edited on digital premiered in 1998, its not surprising that The Last Broadcast was a self-financed indie horror movie, filmed on a $900 budget and edited in Adobe Premiere home desktop computer. The gradual adoption of digital filming took place in fits and starts over the following ten years. Mainstream approval arrived when digitally-produced Slumdog Millionaire dominated the 2009 Academy Awards — winning 8 Oscars, including Best Picture, Best Director, Best Cinematography and Best Film Editing. Having delivered on Hollywood’s biggest night, digital filmmaking soared. As the charts below show, In 2009 nearly 90% of the top 200 films were shot on film against less than 20% on digital. Within five years, these percentages would completely invert with movie theaters quickly followed suit.

Source: StephenFollows.com

As with CGI, the deeply technical complete story of this cinematic sea change is fascinating and not without auteur controversy. However, in decoding a second, more recent digital inflection point in motion pictures, consequential, high-impact traits akin to those from CGI become apparent. Such data helps establish an impact blueprint that enables pattern matching between these past events — when techno-cinematic convergence produced unprecedented collaborative value — and those of the AI-ML present and future.

1. Budget Obliteration: Removing the physical constraint of the medium, the tangible cost and time savings of digitization were immediate, immense, and nearly universal across every line item and stage of film and TV production. Entire budgets previously required for raw film stock, negative processing, and reel scanning disappeared. Shooting on film requires purchasing and loading multiple reels, processing the footage, and transferring it to a digital format for post-production. These expenses can be substantial, especially for larger productions. In contrast, digital cameras offered reusable memory cards or hard drives, reducing ongoing material costs and enabling more cost-effective workflows. 2002’s Attack of the Clones, the first domestic feature shot entirely on digital, spent $16k to shoot 220 hours saved on SSD hard drives. The equivalent amount of film would have cost nearly 115x more or $1.8 million. In order to theatrically release on film, every theater needed a physical copy of the movie, running about $1,500 per cut. For wide feature releases in, say, 5,000 theaters, this added up to an industry average of $7.5 million to distribute each film. For a digital release in theaters, digital film files sent via mailed hard drives, satellite or cloud systems could complete wide distribution for 90% less.

2. Dynamic Creative Flexibility: Digital cameras offer a wide range of settings and options, allowing filmmakers to experiment with different looks, frame rates, and resolutions. This flexibility grants creative freedom and the ability to adapt to various shooting conditions. Shooting on film comes with a tangible cost per frame, which can influence decision-making on the number of takes and overall shooting ratio. Digital filmmaking removes this constraint, enabling filmmakers to shoot more footage without worrying about additional expenses. This freedom encourages exploration, improvisation, and capturing multiple angles or performances, leading to richer storytelling and more creative possibilities and advantages: Digital footage can be easily transferred and processed in a digital workflow. Editing, color grading, visual effects, and other post-production processes can be performed more efficiently and cost-effectively compared to working with film. Digital workflows also enable seamless integration with computer-based visual effects and CGI techniques, expanding creative possibilities in post-production.

3. Cross-Discipline Collaboration: Shooting on film was often a plodding process since it could be several days if not weeks between filming a scene and seeing how the raw footage looked. Practically, drawn out turnaround times meant lengthy production schedules, frequent reshoots, and inevitable budget strain. These fragmented, piecemeal constraints neutered spontaneous ideation on set, limiting creativity in production workflows where it is essential to success. Furthermore, single copies of eventually reviewable footage went to directors and higher ups, reinforcing a sense of rigid top-down process that deterred potential iterative creative collaboration between a films leadership and everyone else who brings it to life. Digital cameras enabled instant playback of footage on set. Real-time feedback empowered filmmakers to make adjustments to any element of a shot or scene on the fly. The immediacy of digital film review streamlines and powerfully elevates linear film shoots to more dynamic creative flows. The availability of digital film allows for potential collaboration between the director, cinematographer, and other departments, enabling them to fine-tune the visuals and storytelling more effectively. The software systems that enable digital filmmaking are designed to be mutually available to all parties - facilitating input across departments and stages of production - all conducive enhanced collective creativity in action.

4. Availability Amplified: For decades, the cost of raw materials, complex equipment, and specialized training necessary to shoot and edit film stock made film and tv production extremely exclusive. with deep pockets to cover the above costs upfront, Major studios and broadcast corporations had a true oligopoly over what was made for both big and small screens. The emergence of digital filming shattered these exclusivities by drastically lowering barriers to entry. Moreover, digital filmmaking has become more accessible, as high-quality digital cameras, highly open editing software, and large capacity SD cards are more affordable and accessible, empowering many more aspiring filmmakers, independent productions, and small studios to enter the industry. It should be noted as well that this technical inflection point bring about the end of shooting films on film, a long-term reality which I reference again towards the end of this essay. It remains perfectly acceptable and possible for filmmaking traditionalists with enough resources to make movies on film, and they do. While non-studio independent film releases have exploded in the years since digital filming broke through , the total number of all films — studio, indie, and hybrid — released has remained steady every year as well:

Source: StephenFollows.com

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Now, side by side, two techno-cinematic inflection points in filmmaking compared to the implications of AI-ML for creative industries today could invite more contrast than comparison. This, of course, is not the intent or purpose of this essay. Emergent technologies do so at different speeds, their development, adoption, and application curves shaped by wholly unique externalities of their given times. Further, these circumstantial factors often influence how novel technologies are perceived, especially in immediate and shorter-term timeframes. However, the progress of technology’s irrepressible innovation tends to position its technological phases in an evolutionary sequence as opposed to independent categories warranting evaluation side by side. While cyclical in invention-adoption-installation from one innovative paradigm to the next, the irruptive and installation phases of novel technology often evolve up from mature technologies that allow them to exist. The pace of AI-ML development has been predicated not just on innovative algorithms, but also on both our ability to generate, access, and store massive amounts of data as well as the advances in graphics processing architectures and in-kind hardware to process such enormous amounts of data. Continuing concurrent advances in computing power, storage capacities and communication technologies (like 5G broadband) will support the embedding of AI processing within and at the edge of the network. Applying a broader picture lens over time, innovative technological breakthroughs come to resemble interdependent layers of a broad technology stack — each building block enmeshed in both its foundational predecessor and in the future innovative layers it inevitably helps create.

Creative origination often relies on human imagination, emotion, and lived experience to ask abstracted questions and drive new ideas not addressed by constrained learning systems. Conversely, many production tasks within creative output are much more repetitive and predictable, executed in structured workflows and data conformity. Production tasks are also often time-consuming or otherwise costly for humans to complete due to the necessity to find and retrieve specific information from enormous datasets (i.e. raw footage files, music recording files, digital stock image databases) or otherwise analyze and infer insight from those sprawling datasets. And so, identifying and separating these two types of tasks within the overall creative process provides a much clearer framework to position AI-ML in their most positive and collaborative creative applications.  AI-ML works well when there are clearly defined problems that do not depend on external context or require long chains of inference or reasoning in decision making. It also benefits significantly from large amounts of diverse and unbiased data for training. Accordingly, production tasks that require manipulation of immense amounts of data, stand out as amenable to integration of AI-ML functions.

Let’s look now at existing examples of creative AI-ML, in the realm of Soderbergh’s tool concept, that fit the function of production tasks. Emerging use-cases that display the immediate value of AI-ML as a tool for highly specific point solutions along existing planes of artistic output. These creative applications fit into four core categories: information analysis, post-production workflows, information extraction, and data compression. Within a given category, AI-ML delivers extraordinary if not unprecedented efficiency or flexibility in completing specific tasks, often at significantly lower cost than previously required. Think the dinosaur walking track and CGI’s first use-case on Jurassic Park or the first digitally shot single sequences in the 1996 film Rainbow. As discussed, each successful application examined herein also serves as a mechanism enabling greater human conceptualization and abstraction — freeing up human creatives’ time and energy to focus on creative thinking within the scope of their process that may previously have been muted, ignored, or bogged down by completing these functions themselves. It should be said that these examples and many other applications may quickly come to address several categories in combination. These combinations will likely blossom into broader and more substantial AI-ML adoption in creative processes. However, each represents an initial real-world entry point and anchor that demonstrates the technology as a collaborative enhancement to human-centric creativity.

AI-ML Production Task Point Solutions

Film & TV Post-Production Ops: For motion pictures, post-production is when raw footage is cut and assembled. This editing can include touching up visual elements for clarity and quality, shot matching and colorization for visual and narrative consistency, syncing sound-mixed audio, music, and sound effects, and the addition of digital visual effects. Most relevant to potential applications of AI-ML, many post-production functions revolve around the dynamic manipulation of enormous amounts of uniform data: hundreds of hours of digital film files. RunwayML and Descript are two startups of note in the AI-ML video editing space.

Resolution Quality: Upscaling and super-resolution are algorithmic processes common in post-production that calculate higher pixel counts from low-res frames to provide an improved high-res frames for distribution. Such video scaler systems require separate implementation and often separate servers, which can be expensive. Upscaling also can leave nonsense anomalies in frames or frame transitions. AI-ML models known as residual convolutional neural networks have been trained with matched low-res and high-res images to learn the redundancy and differences present the two images’ pixel data. Subsequently combined with a generative AI layer, these models can produce high-res images from low-res frames as the only input. Similarly trained models have been built to improve both temporal and spatial motion for frame sequences as images change. AI-ML upscaling has produced shaper textural and motion details, with a crispness and clarity that outperforms traditional image scalers. Animated movie productions have been early adopters. In 2020, computer graphics engineers at Pixar developed super resolution system mapping low to high-res image creation trained on Pixar’s animated film archive. This deep learned architecture capably “reconstructed artifact-free images with detail and sharpness indistinguishable from ground truth…consistent even on scenes with depth of field and motion blur.” In addition to this quality, their most recent models can reduce approximately 50-75% of the studio’s render farm costs.

De-noising, Color Correction, and Color Grading: Various types of unwanted visual noise can be introduced to film during recording, processing, and broadcast signal acquisition, white snow picture blur is a common example. As such, de-noising nodes are commonplace in post-production workflows. Similar to AI-ML resolution quality above, neural networks trained with matching noisy and clean video clips estimate a residual noise map that’s currently delivering state-of-the-art performance in removing spatial and temporal noise from raw footage. Color correction, grading and matching for film and television is an essential part of the late post-edit process. It’s also surprisingly expensive and time intensive. It can take several weeks for feature length films and generally requires 5-10% of the entire production budget. Motion picture focused machine learning interpretation and classification models can be trained to analyze input footage and apply appropriate color grading adjustments. Automatically matching individual filmmakers’ inputs for color aesthetics from shot to shot and ensuring tone and palette are remain consistent saves an enormous amount of time and budget normally allotted to a traditional color correction process. Startups working in this space include Colourlab.ai and VEED.

Sound Library Management: Within studios and labels, disorganized sound libraries without classification systems delay new music creation and release every year across genres. Externally, 3rd party pre-recorded sound libraries, for music and sound effects, without user-friendly order cause time delays in production. The advent of certified digitized audio increased the volume of available, lower cost sound files while and the emergence of cloud databases made whole libraries accessible on-demand. However, upon this digital, cloud-enabled base, the absence of reliable search and efficient discovery in process remains a prominent source of time lost across industries including music recording, film, tv, and other forms of audio including podcasts. With machine learning, classifiers like Convolutional Neural Networks (CNNs), used prominently in image identification and matching models today, provide a basis for sophisticated, large scale audio classification architecture, which, combined with interpretive and predictive layers, can dynamically tag, categorize and structurally organize public and private sound libraries – delivering search-friendly design and rapid results from Boolean or otherwise complex queries. OG digital sound library startup Splice has development machine learning-powered sample search. Other startups building in the space include Cyanite and Pibox.

Music Copyright Protection and Sample Clearance: Copyright lawsuits have beleaguered the music industry recently and certain notable past cases have dragged on for years. On the surface, it seems AI is shaping up to be a near-future synthetic defendant further complicating this issue considering the AI-generated Deep Drake track fallout from earlier this year. There are AI/ML applications can predict copyright conflict, avoiding later lawsuits and restoring some confidence in artist collaboration, directly or through sampling. As things stand, perceived concern about lawsuits has many recording artists in recoil, abandoning sampling as a potential creative source. This risks limiting certain creativity if recording artists are siloed and absent a fundamental musical inspiration — that which already exists. AI-ML can’t mend every burnt collab bridge and more powerful neural networks may be needed to finally bring many of the lost musical gems from the sample-heavy mixtape era to streaming (shout out Dedication II and Drought 3). As a point solution however, AI can analyze each element of complex audio recordings and cross-compare with against copyrighted libraries. Likeliness percentage rankings flag any song element above acceptable similarity thresholds. Startups working in this area include Audioshake and StarCoder from HuggingFace.

Audio Mixing and Song Mastering: As with most of the process of audio recording and production, final mixing and mastering have experienced increased accessibility and relative costs reduction thanks to workflow digitization and user-friendly software tools. However, the entire mastering process can still be frustratingly meticulous to complete. AI-powered audio mixing and mastering tools currently analyze audio tracks and automatically balance levels, EQ frequencies, and apply dynamic processing for optimal sound quality. Certain products offer mix and master by predetermined artist or producer settings as well as genre-trained quality thresholds. Two startups bringing compelling products to market here are: LANDR and RoEx.

Script Assistance: Compelling and original narratives are a vital undercurrent that shape many forms of creative art, including film, tv, gaming, and literature. For anyone who writes there are few things more daunting than a blank Word doc. For writing teams across many creative mediums, the draft edit stage for a screenplay or script is often an excruciating, stop-and-start process, as painful as it is slow. During the earliest phases of film or TV development, revising and punching up a draft script into its final version can be the heaviest lift of an entire production. AI-powered platforms can facilitate real-time collaboration among multiple scriptwriters, enabling seamless co-writing, version control, and feedback integration. Natural language processing models can be deployed to run draft analysis for plot holes, inconsistencies, or logical errors, ensuring that the story maintains plausibility and continuity. As an originality failsafe akin to sample clearance, certain models can compare drafts against huge archives of existing script databases to ensure they are sufficiently unique and distinct from other copyrighted materials. Working to help solve the dreaded blank page, Lex is a startup building a seamless, intelligent writing aid while Writesonic and Colossyan are two text-focused AI-ML startups that offer products focused on script assistance.

Pre-visualization: Previz is the pre-production process of creating visual representations of scenes and sequences before filming. Less expensive then principal filming, previz utilizes mediums like 2D storyboard sketches, 3D reconstruction, location scouting imagery, and animation to enable filmmakers to plan and refine shots, set designs, and special effects as well as test staging and art direction options. The concept is also utilized in other creative arts including still photography, performing arts, video game design, and narrative animation. Traditionally, previz is often a painstaking, linear process. Transforming a director’s earliest imaginative visions into first draft visuals can be time-consuming and labor-intensive for the teams involved. Image and video generative specific AI-ML models can help filmmakers and their teams create quick visual representations of their ideas and dynamically iterate different lightning, locations, camera angles, character blocking and movements. Making more efficient use of valuable pre-production time and resources, AI-ML-assisted previz helps bridge any gaps between a director’s creative vision and the technical execution of their production teams. Storia AI and PentoPix are both video-based AI startups bringing fascinating pre-visualization products to market currently.

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In performing the above functions and many others with precision and fidelity today, AI-ML provide advance data and early signal of the techno-creative inflection point at hand, through which we can begin to anticipate the next order effects to come of a kind with those examined in the two techno-cinematic examples earlier.

Routine Efficiency: Faster & Cheaper

“ML-AI here are about the augmentation of creativity. In the end, it's more about how can you get better efficiencies in human creative production. With filmmaking, 99% of the work is actually very mundane. It's going through hundreds of hours of video in some cases to arrive at the core pieces to use. So there's still a very good reason to use this technology as an assistant here, rather than replace the human in the loop."

- John R. Smith, Discovery Technology Foundations IBM Watson Research 

The twin hallmarks fundamental to many an innovative technology, cost and time savings drive breakaway new value realization and current applications of creative AI-ML deliver both in abundance. More so than the other next order effects explored herein, significantly faster and cheaper task completion is a clear and present value proposition for each of the existing AI-ML point solutions outlined in the previous section. Below are a few more AI-ML time and cost saving statistics to highlight just how powerful and consistent these two value characteristics already are, despite technology still in the early stages of development:

• A survey of film editors reported a 63% reduction their raw footage search time using AI-ML database search products

• The post-production team for Addams Family 2 estimates a 20% cost savings using AI-ML models for image de-noising

• Surveyed studio script departments estimated their script clearance was 55% faster due to AI-ML script analysis tools

• In 2022, 99% of Youtube copyright infringement flags were detected by its in-house AI-ML content ID tool

• VFX studio Electric Theatre Collective completed a complex animated ad for Coca-Cola 5x faster using Stable Diffusion

• 1,500 music industry producers polled reported 25-35% lower mix and master costs using collaborative AI-ML audio tools

What makes these examples of AI-ML performing functions faster and cheaper into a lasting higher order impact is the scale of the efficiencies delivered. These are not marginal production time improvements or minor savings on a line item or two in budgets. Not unlike the techno-cinematic examples previously, these examples are evidence a technology that can be light speeds faster at a fraction of the going cost in certain areas of creative production.

Given concerned rhetoric swirling around generative AI, we’ll tread softly in expounding on the further manifestation of faster and cheaper that these technologies can come to provide. With AI-ML, one could conjure a near future by pulling forward the current speed and low cost that project’s the imminent reduction of human contribution in these fields. However, as we’ve seen with the two examples of digital inflection points in filmmaking, the extraordinary cost and time benefits of novel technology innovations do not sentence human creative collaborators to an outmoded junkyard. While not without some friction upon arrival and during their installation phase, these technical inflection points tend to jumpstart an expansive creation of human opportunity as they progress into a paradigm shift. The specific solutions described above are, nevertheless, data-based evidence of these technologies unprecedented capabilities and extraordinary value in reducing time cost and money cost of many stages of creative production. It wouldn’t be surprising to see certain point solutions of AI-ML expand along a horizontal creative plane to similar adjacent production tasks or to vertically specialize around specific content types such as audio, video or text. Wherever the technology can address creative production tasks for which the prior completion was cumbersome, repetitive, and a known drain on time or budget, AI-ML will have potential to be adopted as a collaborative tool. Crucially, these types of functional AI-ML adoption will translate to increased productivity through each stage and so more human-made creative output overall.

Imagination Manifest: Freedom To Try

“An essential aspect of creativity is not being afraid to fail.”

- Dr. Edwin Land, Co-Founder, Polaroid, LIFE Magazine October, 1972

One of the most transformative effects of the cinematic digitizations discussed earlier was advancing the ability to experiment, especially in real-time on active productions. The ease-of-use creative flexibility and cheaper production costs introduced a new filmmaking paradigm in which trying new ideas, including those that turned out to be bad ones, was no longer prohibitive by cost, equipment complexity, or production time. A spur of the moment idea on set for a different shot angle or in post-production for a new VFX design could be brought to life, where before those experimental thoughts may not even be voiced. New ideas are creativity’s foundation and the technological tools and infrastructure of digitization pulled Hollywood forward such that it could better afford to facilitate their suggestion at many more points in production. Much of the early progress of creative AI-ML suggest that these novel technologies are poised to further expand the freedom to try things across industries of art and entertainment and for modern creativity more broadly.

Stepping out of the weeds of industry for a moment, human confidence to creatively express is a characteristic state of mind that diminishes with age. Abundant in children bound only by available surfaces to draw on, creative confidence gradually encounters external influences – form, rules, trained skills, and, perhaps most significantly, criticism – which tend to subdue that self-belief. Artistic wunderkinds and Peter Pan aside, over time growing up inevitably limits potential creative pursuits for the masses. With creative AI-ML, especially generative, a fundamental and innovative purpose is the capability to take an idea and turn it into something. The low barriers to entry and ease of use of many leading AI-ML products suggest immense potential to foster and restore creative confidence and expression. So, what does this mean for the existing professional fields in creative arts? Machine learning will give creatives more freedom to test out novel ideas in the flow of production at myriad points during the process. Returning nearly instant results at tiny relative costs, the fears of failure, reprimand, or being deemed ‘bad’ essentially vanish. Human creatives will be able to build up new confidence to suggest and try out creative ideas that aren’t necessarily fail-proof, aren’t perfect, or even ultimately just aren’t good, because AI-ML output will be able to give some indication in advance of what a conceptual idea would look, sound, or read like.

AI-ML enabled freedom to try should also enhance and help answer the question what would “x” look like? in the earliest ideation phases of creativity. Substitute the “x” with an idea for: a storyboard, a 3D reconstruction of an environment, a sizzle reel, a character sketch, a POV shot, or a drum machine over a classical music genre. A version of a take on what that would be comes to life immediately at a low relative cost. In terms of process-oriented creativity, these applications of AI-ML, even their generative versions, then can function as spark and springboard which expand imaginative possibilities our own creative inspiration and output. Within this kind of framework, AI-ML can powerfully augment and so encourage the age-old simple ideation process of throwing stuff against the wall to see what sticks. The technologies wouldn’t choose what sticks – that creative necessity to discern, to choose remains a human responsibility drawn from intuition, emotional resonance, lived experience, and abstract perspective. However, an instantaneous means of palpably visualizing the stuff that’s throw at the proverbial wall is an extraordinary enhancement, not just for efficiency, but as a means of adjusting our aperture to realize new horizons.

In his autobiography, Mark Twain opined on the fallacy of new ideas. He reasoned that “we simply take a lot of old ideas and put them into a sort of mental kaleidoscope. We give them a turn and they make new and curious combinations. We keep on turning and making new combinations indefinitely; but they are the same old pieces of colored glass that have been in use through all the ages.” Whether one agrees entirely with Mr. Twain or not, there is immense creative power in re-examining all the possibilities that have come before, exploring new combinations of existing artistic elements, or telling an old story in a distinctly novel way. In gathering the same old pieces of colored glass that we put into said kaleidoscope, AI-ML can powerfully enhance the breadth and depth of their sourced domains as well as the speed of their collection, enabling our ability to explore exponentially more combined choices and compare endless new concepts immediately. Natural language processing (NLP) that’s foundational to AI-ML interpretation, classification and so search can draw across the entire spectrum of available knowledge bases, fields of study, and collective histories to deliver exhaustive lists of existing ideas and concepts. In this way, generating Twain’s old ideas can return existing possibilities as broad or specific in their type and origin as the input prompt desires or requires. If certain creativity then depends on the art of human choice, AI-ML applications can deliver the entire realm of the existing to our fingertips in an instant, freeing our minds to focus on attempting combinations - new, curious, or thus far overlooked - that may provide centrifugal inspiration that blossoms into an eventual work of art.

Visual Communication: A Collaborative Force Multiplier

"The great enemy of communication is the illusion of it.”

- William H. Whyte, Fortune Magazine, September, 1950

Creativity is an essential means of expression and so communication. The American Jazz aficionado Lionel Hampton said that “all art is communication of artists’ ideas, sounds, thoughts”. If communication then underpins the whole purpose of creative expression, it is when creative production exceeds the capacity of an individual artist that in-process human-to-human communication becomes a necessity that can be a functionally time-consuming burden to get right at various stages. For example, a cartoon creator must convey to storyboard sketch artists their vision for characters, scenes, and environment, creator and sketch artists must explain their color pallet concepts to colorists, all three must communicate ideas for character expressions, emotions, movements and interactions to digital animators, and so on. While at each design stage, the domain expertise and skills of each new participant and the evolving visual asset assist these co-creator communications, it can be a challenge from drafting phases through to post-production digital edits to extract from spoken vision a mutually satisfying result — certainly on the first few tries. The visual drafting capabilities of AI-ML can be a powerful means of collaborative communication during these crucial interactions. Generative models can take a state vision at the given production stage as input prompt and return countless visual concepts in the appropriate medium. Depending on results, the vision input can be adjusted on the fly as creative juices flow in response to instant output. The effect can be to enable these production stages to progress faster, each artist contributing to a dynamic that continuously refines a final product as opposed to burning time drafting preliminary visuals from more linear communication. Not at all a means of replacing the overall creative process or any specific production stage, AI-ML applied in this way should function as a collaborative force multiplier. Assisting the translation of imagination to a visual example will potentially benefit artist-to-artist communication especially around concepts that they are struggling to accurately explain.

Greater Human Reach: Unprecedented Accessibility

“Anyone can cook.”

- Chef Auguste Gusteau, Ratatouille

Given the availability of consumer and enterprise AI-ML applications and the current state of external, enabling technologies – 5G broadband network saturation, ubiquitous public data availability, high volume cloud data storage and processing power – the existing stages of these innovations may be more accessible than any other breakthrough technology to date, including the internet 1.0 and the mobile revolution. Along with the twin tenets of faster and cheaper, democratizing access is a persistent redeeming quality of novel technologies and a strong bellwether of its potential staying power. Within the realm of creative industries, many more potential creators empowered to create – to turn an idea more ably into something – is inevitable progress, especially long-term.

This does not suggest that everything these technologies help to create must be good or will be. I am not predicting the immediate, high-volume creation of works of art born out of every new prompt or AI-ML creative utilization. To expect such from a novel technology that’s broadened creative access to individuals who may previously have had limited creative experience is attempting to construct an artificial and frankly weak and snobby barrier to acceptable entry in the face of a technological dynamic that’s breaking them down. Recall though the brilliance of Chef Gusteau’s simple coda about his profession from Ratatouille one of my all-time favorite movie quotes. Aside from access to grocery ingredients and a kitchen, there are no barriers of entry to cooking. Anyone, anyone, can cook. Does he believe everyone should? Perhaps not. Does he mean anyone can go on to earn a Michelin Star? Certainly not. The reality with these and future versions of creative AI-ML means more people, so motivated, can more easily try. That is a good thing.

The potential human functional boost from AI-ML — operating at superior levels of abstraction in production with more time to focus on creative thinking — is only an opportunity if its recognized, grasped, and embraced. As explored above, AI-ML can be a significant new factor in completing certain functions in creative production, notably those heavy lifts that are repetitive, menial, structured, or data intensive tasks. Engaging these technologies as such does not retire the human but empowers emergent individual human qualities and pushes human creatives to access complex thinking that can have a more consequential impact on that which is created. And so, if synthetic computer intelligence may free us up to concentrate creatively, we must recognize that freedom and develop original concepts, tell old stories through new narrative lenses, and inject our unique human empathy, perspective, emotion, and experience into all that we do with this newly available creative time and abstracted mindshare.

Following the pattern of past techno-cinematic digital inflection points, AI-ML ,while able to shift and transform entire creative industries, aren’t predestined to become absolute. Neither CGI-VFX nor digital cinematography became required tools and neither eliminated the methods of filmmaking that preceded them. Practical, physical special effects lived on alongside CGI and continue to do so depending on a director’s preferences and vision. Many of today’s beloved directorial auteurs – Quentin Tarantino, Paul Thomas Anderson, Christopher Nolan, Wes Anderson, and, yes, when they can, Steven Spielberg and Martin Scorsese – are ardent devotees of shooting, editing, and, in some cases, distributing their movies on traditional film stock. Sure, these names can afford the cost barriers of such tradition, but the point is they still can. The explosion of digital cinema and the saturation of filming digitally and, crucially, digital theater distribution, did not snuff out traditional techniques. Developing in a like fashion, creative applications of AI-ML are unlikely to become definitive pre-requisites for every creative production.

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Coda: Change, Trust and Evolving Responsibility

Recalling Soderbergh once more: AI-ML technology is a tool with creative yield that depends on data it's trained with and the algorithms used. As such, standalone AI-ML art in any medium today is derivative. It exists as a synthesis of historical data upon which its algorithms are trained. With immense sophistication, this output often mixes elements of that training data in dizzyingly complex and fascinating combinations, but it nonetheless derives from recorded human ideas. Pulling this nuance forward, it may be straightforward for these technologies to produce something novel by random chance. But it’s much more challenging for creative AI-ML to come up with something new that’s uniquely inspirational, unexpected and useful to a particular creative flow. Given this, the true potential of AI-ML in unlocking new forms of creativity lies in the symbiotic relationship between human creativity and AI-powered tools, where humans guide and shape the technology to achieve creative objectives. As AI-ML continues to advance, its impact on creativity is likely to evolve. However, its power and consequences come down to how we build it and how we use it.

The enduring impacts of previous techno-cinematic inflection points depended on a crucial precedent that will again be essential to achieving any of the potential next order effects of AI-ML explored above — the willingness and intent of human creators to develop a proactive, transparent, and trusted partnership with new technologies in the creative process. At the respective technical inflection points for CGI and DIGI, several prescient and ambitious filmmakers embraced their emerging capabilities and future promise and their peers quickly followed suit. With vigorous, thorough intent, legions of entertainment creatives immersed themselves in understanding the inner workings of these tools and actively tested their limits, fostering collaborative efforts that embedded them in production workflows industry wide. In so doing, those human partners continually discovered, evolved, expanded, proliferated the cutting-edge capabilities of those technologies over time. That mutually driven advancement helped transform two groundbreaking new technologies into enduring, dynamic ecosystems that facilitate and encourage audacious human creativity and experimentation in ways that previous techniques, workflows, systems had not or could not. Many of the AI-ML point solution examples above feel like they could be v3 AI-ML evolutions of the v2 digital innovations that preceded them. Being digitally flexible and cloud redundant, CGI and digital filming debuted paradigms within which mistakes didn’t cost extra or compromise completed work, inspiring human creative confidence to try new ideas in the moment that might not work. Building upon its digital predecessor, AI-ML can be a paradigm shift that encourages that same human creative confidence elevated to scale of transposing their entire imaginations. Any scenario, setting, soundscape or sequence, from minuscule to galactic, can be realized in a remarkably complete form instantly without additional cost. This is a remarkable benefit to expanding creativity. Trying new ideas that might not work may evolve into Trying Everything or even Trying Anything if it comes to mind. Through an array of applications, AI-ML can breath life into the creative abstracts that live inside all our heads and anyone can try it out already.

Attaining such an outcome will depend on how we perceive, approach, and ensure a collaborative dynamism with AI-ML into the many future unknowns we’ve yet to realize. The levels of intent, enthusiasm, trust, openness, and genuine desire with which we engage AI-ML will determine the contours, contributions, characteristics and capabilities of the technology’s current inflection point and the new paradigm thereafter. How we partner is all that much more critical with AI-ML, given that it involves human creatives with empathy and every other emotion on one hand and a built technology with no inherent concern for human emotions in functional objectives on the other. Should humans lean into the reactionary fear, zero-sum insecurity, and dismissive angst that rejects this technology, we could manifest a synthetic intelligence that evolves to reflect the very worst in us. An upside of advanced AI-ML however is that it can learn, does learn, and can be actively trained to recognize and know the very best, most optimistic, and forward-thinking versions of its human creative collaborators. If creatives embrace this collaborative opportunity with enthusiasm and an eyes wide open view towards enabling previous impossibilities, far from marginalizing human artistic impact, AI-ML will elevate it, broadening the scope of what we can create and democratizing access for who can create it.

Epilogue

It’s worth noting that the arrival of AI-ML comes at an inauspicious time for the media and entertainment sector that’s worth $2.47T worldwide. Surging financial stakes have forced actors and writers to strike for fair compensation and driven the industry to codify derivative project production with no appetite for creative risk. As a result, the business of entertainment today operates at a diametric inverse to the bold, roaming nature of artistic imagination. Studios, labels, and publishers are designed to restrict creativity. They mine existing IP for a kind of product output that has delivered strong financial returns in the past and lean into imitating or extending that known entity for the bulk of what gets produced going forward. In real-time, that’s meant an inescapable deluge of sound-a-likes, sequels, franchises, spinoffs, and reboots. Of course, the incongruence with artistic creativity is that once something truly groundbreaking is made — a movie, album, book, or show — its repurposed versions are generally made absent originality. By design and expected financial performance, ancillary offshoots are exactly that. Connecting converging dots, a modern entertainment industry prioritizing productions based on repetition may seem ripe for adoption of AI-ML technology that has the potential to create polished content of a kind with existing media data that trains it. The current state of the entertainment business may be intensifying the resounding sense of existential vulnerability in artists and creators regarding AI-ML. That is completely understandable. If, however, creatives choose to utilize this technology for the creatively profound, increased collaborative adoption can give rise to positive next order effects like those outlined in this essay, whereby the lasting impact of AI-ML on these fields has been to transform, expand, and improve. In a recent interview on The Town with Matt Belloni, Rick Nicita, former co-chair of CAA, made clear the reality and importance of this moment in entertainment. “It’s been too much corporatization and not enough innovation,” he said. “Art can only flourish with the new. Studios and financiers have to really realize and digest that what makes hits are fresh takes, not just repetition. The entertainment business will always exist but for it to thrive artistic risk-taking has to be encouraged again.” AI-ML can provide a remarkable boost towards new artistic heights if the entertainment industries once again embrace the creative risk of originality.

We humans are a curious lot. We rapidly develop and en-masse enthusiastically engage new technology that changes things while stoking fear of the worst possible scenarios we can imagine for what that technological change could take from us — even as we use it more and more. Only human, I suppose. Our contradictions give us certain depth. As director David Milch once said: “any good poem or any good human being in any good story spins against the way it drives.” Our collective natural drive is change. In truth, change shapes our reality and our ability to change defines us. Change is the perpetual motor of the human condition and the fundamental state of our biological existence all the way down to the atom. It is then only natural that the very essence of creativity is change. Harnessing change within the specificity of an active creative effort requires ingenuity and, more than anything else, originality. Original thought remains a distinctly human quality. Movies like Barbie and Oppenheimer and series like The Bear and Reservation Dogs show us human originality succeeds. It’s thanks to that originality that artistic works can become works of art.

A Year of Books

A few months ago, I was meeting up with a friend for coffee, and she asked me if I was working on a dissertation on Marlon James when she saw all the sticky note nubs in the copy of his most recent book that I had with me, pictured here. Surely not evidence of post-grad research, its just the latest method I’ve been trying for a bit to both note things that feel special to me while reading and also be reasonably able to find again later on. Like many readers, I’ve tried lots of different versions of this process: just underlining (too hard to find desired quotes again), dog-ear’d pages (unreliable over time), and even the heavy-lift of writing a highlights citation list on any blank pages at the front of the book (way too disruptive and so easily abandoned). Since I still do most of my book reading analog for now, I figure there’s no perfect solution; and so far I’d say this one works ok, which is just fine. Thinking back through the books I’ve read this year (all of which look like the one above, excepting library books of course) I wanted to see, or am curious to try to open the lid a little on what’s so far been and become a running archive of writing that I find especially special, and that only I see, reread, or think about later on in that context. Just to see if things resonate or react beyond one reader. So one pinky toe in the water towards trying this out, I’d thumbed back through all my sticky note nubs and include one quote below each title that I’d saved as special when I read it. Here goes:

Fiction

Moon Witch, Spider King, Marlon James

No thought is wise just because you have it.

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The Inheritors, William Golding

Who would sharpen a point against the darkness of the world?

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Klara and the Sun, Kazuo Ishiguro

At the same time, what was becoming clear to me was the extent to which humans, in their wish to escape loneliness, made maneuvers that were very complex and hard to fathom. People often felt the need to prepare a side of themselves to display to passers-by – as they might in a store window – and that such a display needn’t be taken so seriously.

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Sea of Tranquility, Emily St John Mandel

and my point is, there’s always something. I think, as a species, we have a desire to believe that we’re living at the climax of the story. It’s a kind of narcissism. We want to believe that we’re uniquely important, that we’re living at the end of history, that now, after all these millennia of false alarms, now is finally the worst that it’s ever been, that finally we have reached the end of the world.

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Monkey: The Journey to the West, Wu Cheng’en

Nothing in the world is difficult, it is only our own thoughts that make things seem so.

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The People in the Trees, Hanya Yanagihara

I found myself thinking that perhaps there was something inexorable about the way events unfolded, as if my life — which had begun to seem something not my own but rather something into which I found myself blindly toppling — was indeed something living, that existed without my knowledge but that pulled me along in its strong, insistent undertow.

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The Summer Book, Tove Jansson

It is still summer, but the summer is no longer alive. It has come to a standstill; nothing withers, and fall is not ready to begin. There are no stars yet, just darkness.

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Traveling Light, Tove Jansson

What is constantly changing is superior to what is static.

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The Lesser Known Monsters of the 21st Century, Kim Fu

One adult can be lured into pretend, can taste the tea in our toy cup, hear the voice on the toy phone. One adult could have seen what we saw and carried it quietly within her forever. But not four. Four adults have to agree on what happened, agree on the rules. Four adults can talk to each other until reality straightens, until doubt is crushed, until their memories unstitch and reform. Four adults never see a miracle at once.

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The Seven Moons of Maali Almeida, Shehan Karunatilaka

Though, as every gambler knows, the biggest killer in this godless universe is the random roll of the dice. Plain stinking jungle variety bad luck. The thing that gets us all.

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The Legend of Pradeep Mathew: A Novel, Shehan Karunatilaka

How much love does one need in a lifetime? Is there a quantity of brain space that is allocated to love? And for those of us who have loved less, does this space become occupied by something else? Like cricket, or religion, perhaps.

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Illuminations, Alan Moore

I’m just scared because everything feels weird. It’s as if everything’s changed. Not just you: everything!

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Tomorrow and Tomorrow and Tomorrow, Gabrielle Zevin

But it is worth noting that to be good at something is not quite the same as loving it.

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The Passenger, Cormac McCarthy

Grief is the stuff of life. A life without grief is no life at all. But regret is a prison. Some part of you which you deeply value lies forever impaled at a crossroads you can no longer find and never forget.

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Last and First Men, W. Olaf Stapledon

Is the beauty of the Whole really enhanced by our agony? And is the Whole really beautiful? And what is beauty? Throughout all his existence man has been striving to hear the music of the spheres, and has seemed to himself once and again to catch some phrase of it, or even a hint of the whole form of it. Yet he can never be sure that he has truly heard it, nor even that there is any such perfect music at all to be heard. Inevitably so, for if it exists, it is not for him in his littleness. But one thing is certain. Man himself, at the very least, is music, a brave theme that makes music also of its vast accompaniment, its matrix of storms and stars. Man himself in his degree is eternally a beauty in the eternal form of things. And so we may go forward together with laughter in our hearts, and peace, thankful for the past, and for our own courage. For we shall make after all a fair conclusion to this brief music that is us.

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Things Fall Apart, Chinua Achebe

Fortunately, among these people a man was judged according to his worth and not according to the worth of his father.

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Been Down So Long It Looks Like Up To Me, Richard Fariña

I cool it here, dig? You never knew anybody so cool. I'm Emir Faisal in Constantinople in 1916, dig, that's how cool I am. This whole scene, I keep at thirty-seven degrees Fahrenheit. Average.

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Non-Fiction

Thinking, Fast and Slow, Daniel Kahneman

It is the consistency of the information that matters for a good story, not its completeness. Indeed, you will often find that knowing little makes it easier to fit everything you know into a coherent pattern.

Finally, the illusions of validity and skill are supported by a powerful professional culture. We know that people can maintain an unshakable faith in any proposition, however absurd, when they are sustained by a community of like-minded believers. Given the professional culture of the financial community, it is not surprising that large numbers of individuals in that world believe themselves to be among the chosen few who can do what they believe others cannot.

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The AI Doesn’t Hate You, Tom Shivers

So the danger isn’t a god-like, Skynet AI, but rather a very smart AI with goals that lead to a range of unintended consequences because of the difference in alignment between the goals we give it, and the way it accomplishes them.

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Art Objects: Essays on Ecstasy and Effrontery, Jeanette Winterson

To say exactly what one means, even to one's own private satisfaction, is difficult. To say exactly what one means and to involve another person is harder still. Communication between you and me relies on assumptions, associations, commonalities and a kind of agreed shorthand, which no-one could precisely define but which everyone would admit exists. That is one reason why it is an effort to have a proper conversation in a foreign language. Even if I am quite fluent, even if I understand the dictionary definitions of words and phrases, I cannot rely on a shorthand with the other party, whose habit of mind is subtly different from my own. Nevertheless, all of us know of times when we have not been able to communicate in words a deep emotion and yet we know we have been understood. This can happen in the most foreign of foreign parts and it can happen in our own homes. It would seem that for most of us, most of the time, communication depends on more than words.

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Sculpting In Time, Andrey Tarkovsky

Artistic creation, after all, is not subject to absolute laws, valid from age to age; since it is related to the more general aim of mastery of the world, it has an infinite number of facets, the vincula that connect man with his vital activity; and even if the path towards knowledge is unending, no step that takes man nearer to a full understanding of the meaning of his existence can be too small to count.

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Make Your Own Damn Movie!: Secrets of a Renegade Director, Lloyd Kaufman

Comedy isn’t commercial; it is risky, because what is funny in one place isn’t always funny somewhere else.

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The Kingmakers: Venture Capital and the Money Behind the Net, Karen Southwick

In the moment intoxicating and mildly unnerving — and upon reflection actually sort of terrifying — bravado in the quick and confidence writ large wins the day right now. A charismatic pitch has ever been an ingredient in the entrepreneurial stew, though brash, uncompromising belief seems to have superseded and rendered insignificant any and all other data points in the eyes and wallets of those funding these egos. The money flows.

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The River of Doubt: Theodore Roosevelt’s Darkest Journey, Candice Millard

In its intense and remorseless competition for every available nutrient, the Amazon offered little just for the taking. Each time he faced personal tragedy or weakness, he found his strength not in the sympathy of others, but in the harsh ordeal of unfamiliar new challenges and lonely adventure.

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River of the Gods: Genius, Courage, and Betrayal in the Search for the Source of the Nile, Candice Millard

“How melancholy a thing is success,” British explorer Richard Francis Burton would later write. “Whilst failure inspires a man, attainment reads the sad prosy lesson that all our glories are shadows, not substantial things.”

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Evicted, Matthew Desmond

By and large, the poor do not want some small life. They don't want to game the system or eke out an existence; they want to thrive and contribute.

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Down and Out in Paris and London, George Orwell

It is a feeling of relief, almost of pleasure, at knowing yourself at last genuinely down and out. You have talked so often of going to the dogs — and well, here are the dogs, and you have reached them, and you can stand it. It takes off a lot of anxiety.

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Jay’s Journal of Anomalies, Ricky Jay

The book says we may be through with the past but the past ain’t through with us.

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Eye Marty: The Newly Discovered Autobiography of a Comic Genius, Marty Feldman

The function of my comedy is not to provide answers, but to postulate questions, impertinent questions and therefore finally, pertinent questions. Not to open doors, merely to unlock them. To not invade the boundaries of probability but stab’d a cool guard this side of the boundaries. Somewhere between there's a thesis. To pump up the muscle of dialectic (or in my case Di-Eclectic!) against the brawn of surrealistic solution. My mind is an attic full of crazy dreams that never quit or disappoint me, and I have been blessed with these eyEs to see things differently and have people see me in a different way. I play not Hamlet, but the second gravedigger, not Lear but the fool.

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M Train, Patti Smith

In my way of thinking, anything is possible. Life is at the bottom of things and belief at the top, while the creative impulse, dwelling in the center, informs all.

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You Can’t Win, Jack Black

There were times when I thought I got a bit more punishment than was coming to me, but I don't regret a minute of it now. Each of us must be tempered in some fire. Nobody had more to do with choosing the fire that tempered me than myself, and instead of finding fault with the fire I give thanks that I had the metal to take the temper and hold it.

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Silicon: From The Invention of the Microprocessor to the Science of Consciousness, Federico Faggin

The driver of evolution is the urge of one to know itself. Yet any existing system will forcefully defend the status quo. Thus a paradigm of cooperation must be this future’s hallmark.

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Natural History: A Selection, Pliny The Elder

The force of the stars keeps down all terrestrial things which tend towards the heavens. The tide of the next day is never at the same time with that of the preceding.

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The Rise and Reign of The Mammals, Steve Brusatte

For many millions of years, oxygen was a toxic pollutant to life on earth. Most of living organisms were anaerobic — metabolizing food without free oxygen. People tend to forget that or not know. Profound is the capacity for dynamic change of this planet’s biogeographic realms. This adaptive nature that’s fundamentally Earth is now realized in the fascinating and beautiful menagerie of mammalian life that now inhabits it.

Considering Venture Platforms

When most people hear the word platform, they probably think of something like this:

Platform: a horizontal surface raised above the level of a surrounding area

The venture capital version of platform — built with a purpose true to the definition above — has become an influential and impactful addition to the org charts and operating strategies of many firms. Broadly, a venture platform (also called network) is the cumulative efforts of a firm employee(s) or team(s) creating and managing non-investing initiatives to raise the value and profile of the firm and its portfolio. While increasingly prevalent, venture platforms aren’t yet standard issue. Given a still formative stage, existing venture platforms come in a variety of shapes and sizes with a varying degree of formal announcement and public expression.

I get the sense that for some in startup land outside of the firms themselves, venture platforms have been at times difficult to decipher. So, I want to share my thoughts on the extraordinary power and endless potential of the venture platform and why defining it properly is an evolving process. Spoiler Alert: this is one of those posts the various parts and drafts of which have been wandering around in my head for awhile, and this manifestation is nothing more than my own thoughts, making no claim to expert or influencer status on the subject.

There are some common motivations I’ve heard from firms building out a platform and team: to formalize a firm’s existing efforts to support its portfolio; to share and amplify a firm’s investment theses and POVs on emerging sectors and technologies; to promote and differentiate a firm with quality thoughts and memorable services. I believe there’s also intrinsic benefit to platform as a complimentary source of proofs of work done over a fund’s lifetime. The time-stamped record of the contributing parts — the content, programs, partnerships, announcements, and events — can help mark a legible, if not always expected, path taken through the mercurial swings in startup investment activity and the illiquid nature of investing so early.

I’ve always envisioned the venture platform as quilt or mosaic, sort of like this tiled table top:

Distinct as the tiles above, each platform effort, in itself, and as part of the collective support of the whole, is intended to elevate the identity and awareness of the firm as a valuable partner to superlative entrepreneurs seeking capital. Generally, those initiatives fall into five value-additive disciplines:

Talent, Content, Community, Mentorship, Public Relations

Within these categories, a cornucopia of platform initiatives can shine. Below are just a few specific examples of platform efforts that I’ve experienced or encountered.

Independent while often interrelated and interoperable, these initiatives create a platform that can play host to inventive and curious experimentation. Certain initiatives last longer than others, some are inevitably more impactful than others, and some more easily replicated. Fluid while thorough, determined while nimble, and always optimistic — characteristics of many dynamic investment teams — are qualities shared by innovative platform teams.

And it works!

The subject of venture platforms and platform impact truly deserves a more celebrated and wider circulated deep-dive analysis. It’s undeniable that the rise of venture platforms has advanced the venture capital industry, helping firms become more transparent, clear, direct, and available to founders. At peak performance, a venture platform can have an amplifying effect on a firm’s reach to new and future founders shown in the visualization below. A note that the anonymous figure icons represent founders.

Contributing to some misunderstanding of VC platform definition is a mismatched method of assessment. Going from future founder to funded founder is an unpredictable series of events with many variables beyond any one firm’s ability to influence. Trying to attribute unique platform data points to a new investment, let alone successful exit, is susceptible to interpreting correlation as causation. Highlighted in the diagram and text below are three stages that can impede linear attribution of platform efficacy.

Stage 1: Engagement Timeframe

It’s often years in between an entrepreneur’s first interaction with a firm’s platform and their decision to start a company. Keeping comprehensive track of every entrepreneur or tech operator who hits a venture platform is a harrowing endeavor as the external variables that can influence those individuals’ decisions are many.

They might become a follower of your firm, they might chew on a few startup ideas but not take the plunge, they might work at 10 different startups but never start one, they might start a company and raise from other firms, they might move to another industry. Platform attribution is challenging in a firm’s active operating process. Despite the shape of the diagram on the left, at the pre-fundraising stage, a venture platform nurturing entrepreneur relationships rarely resembles a funnel or pipeline.

Stage 2: Competition and Firm Alignment

At the stage that a platform-identified entrepreneur begins to fundraise for a new company, there’s less information asymmetry as in Stage 1. Specifics about the company and round can fill in alongside known founder profiles and platform touch points. This hinges on said founder communicating the NewCo raise to the firm. A new set of variables emerge that can cloud sourcing attribution.

First, firm and startup idea may simply be misaligned. A firm may not be active in that sector or a founder may be prioritizing firms with sector specialization. As a side note, this isn’t the fault of platform teams or investment teams. I think it’s actually a healthy sign of both operating as intended.

Second, competition for fundraising founders’ attention is fierce and unrelenting, especially at the point that a round is being discussed, ahead of asking for commitments. Here, a founder(s) might just forget to reach back out to your firm. Their fundraise may simply move quickly from their first few pitch meetings and close without a chance to participate or let your firm know about it. A founder may have had an unremarkable impression of a firm-platform and so it slips through their fundraising cracks.

Stage 3: Hindsight and High Pass Rate

And finally, there are attributional challenges for vc platform tracking along the firm’s decision to pass or invest. Net new entrepreneurs whose interaction(s) with a firm’s platform eventually result in them pitching the partnership should be a well-oiled platform doing its job, regardless of the final investment decision. Hindsight can also obscure clear “sourcing” attribution. Relationships outside and inside a firm overlap endlessly, people come and go and come back again. Understandably, the firm’s priority in real-time is that fundraising founders seek them out. Though a sourcing maze of touch points, relationships and introductions and an always high ratio of passes to term sheets can minimize or obscure total platform contribution and impact.

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I don’t have breakthrough or profound ideas to end with really. I’m just hoping to shed some light, share some praise, and express the nuances of the phenomenon of the venture platform that I’ve experienced.

Nonetheless, some parting attempts at meaningfully conclusive thoughts:

• Linear cause-and-effect is a mismatched method for attribution of any one platform activity to a new investment or successful exit for a firm. If the venture industry often thrives on the counterintuitive – POVs, product ideas, timing to market, entrepreneur profiles, investment theses and decisions – then venture platforms may be in the best position to succeed when viewed through a similar lens.

• If a platform intent and strategy doesn’t involve the portfolio, don’t do it.

• If a firm is or has added a platform because it feels like it has to, it doesn’t! There are virtually zero pre-requisites in VC. Competition is fierce, intellects are driven, and value-add is imperative. The customers firms are competing for, however, are intelligent human beings who are discerning and alert to bs platforms. Authenticity matters.

• If there aren’t enough resources at a firm – time, focus, energy, or capital – to commit to a full-assed platform, don’t do it. It will end up being a drain on resources anyways that frustrates more than it elevates. This approach can also result in a firm cycling through many hires to run platform, if the role is poorly defined or not prioritized.

• If a firm wants to do pieces of what I’ve called platform above – or any of the other parts I’ve not thought of – do them! A platform doesn’t have to be called a platform – see Network and Community – and doesn’t have to be called anything at all. There are myriad ways to help the portfolio and see great, new deals. A platform doesn’t guarantee performance. Many firms have excellent returns without one. As stated, a poorly executed or underpowered platform can impede more than it elevates.

• Each every platform activity is a potent opportunity to stand up and stand out as a firm.

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Like so many wonderful, weird avenues in this industry, platform is grand. An on-going experiment in the vanguard of the profession’s functional evolution that’s delivered new value from the jump. As with building any new thing, there are blind spots and growing pains to creating and delivering palpable value and the form-factor may not retrofit to traditional methods of assessment. The VC platform quilt comes malleable and yet stitched together. There’s as much feel-gut as there is available, informative data to put to each initiative, in deciding how to do it and whether to double-down on it or even expand it further. Another unmistakeable similarity between a venture platform and the investment decisions it enables.

Note:

While platform has not been in my official venture titles to date, I have spent time throughout my career contributing to venture platforms and value-add generally. At Nextview, while my primary role was a venture capitalist on the investment team, I was also responsible for creating and managing the NYC-focused platform for the firm. With the brilliant guidance of Jay Acunzo and Ginny Mineo, I dove in and did my best with our platform initiatives. These came to included a NYC Tech Guide website, a State of NY Seed research report, a NYTech podcast series called Gotham Alpha, dual large-scale and small scale events programs including the NYCode Conference, launching and managing the NY Talent Exchange, a vetted candidate referral program for the portfolio , and at least one love letter to entrepreneurial New York. I’m not one to brag, humbly or otherwise, I just feel that it’s important to produce my venture capital platform credentials along with this post.

Seeking the Transformative Promise of World Wide Web3

Crypto is a lot right now. The hypemospheric worlds that web3 and its antecedent eponym have come to represent overflow with hot takes and volatile stakes. At its genuine best, the sector’s status quo and no so distant past have felt like Professor Perez’s irruption and frenzy phases, the conflation of indelible enthusiasm for new technology that’s, at certain periods, essential to transforming it from potential to permanent. At its perplexing worst, its all felt like a surging cart that’s somehow managed to lap its horse while also jumping a shark. While the zeitgeist boils to a froth, ping ponging between unbridled sure thing conviction and flame-throwing told ya so cynicism, the loudest voices yet to pipe up at scale are those of satisfied users, enterprise and consumer. First-hand user testimonials confirming the new, unique value of the decentralized promise - this was better/cheaper/faster, so I’m going to keep using it - feel less frequent and less amplified than other mainstay crypto perspectives. Absent from the ultra-enthusiastic crypto hype machine cacophony are the voices of everyday users mesmerized by the magic of a novel technology in their real worlds — who ask of crypto not why? but how?!

I few months ago, I tried to take a few steps back, to reexamine the core underlying principles of blockchain technology and seek out where, how, or if their transformative potential has begun to show early signs of proving out in the real world. And if they are - hopefully to then develop a few frameworks and theses helpful in identifying, understanding, and evaluating them in the sectors and ecosystems spearheading that value capture. That process resulted in the research deck below, feel free to check it out, I’d love to hear anyone’s thoughts!

Learning in Lockdown

As of this writing, we’re past Memorial Day still locked down, with 93% of U.S. schools physically closed through the summer. Administrators, teachers, and parents are hacking ways to wrap up the spring semester from a distance. The education industry seems aware that lots of things aren’t working and everyone is salvaging what they can and getting an abruptly minimized Spring session behind them. This all seems appropriate and the efforts should be lauded given the circumstances. Pandemic contingency plans aren’t built into school districts or private institutions (though they certainly will be now).  

Before I go any further, let me be very clear. There’s much lost in school having to go online. I don’t believe the four-year on-campus undergrad degree is a prerequisite , though it is a profound and singular life experience for those who matriculate. Across all levels of education, the relationships forged in focused, often collaborative, learning together will suffer and that’s tragic. So, I’m not overlooking those losses nor am I trying to spin an ongoing terrible situation into one that’s better than pre-virus normalcy. It feels like anyone’s guess what happens in the fall.

However, the coming summer months, especially with no summer campus programs, provide planning time for schools to function adequately online. If we’re all still at home come September, schools should have intentional, comprehensive, and capable systems and processes in place for educating online. It seems more than likely that this will be the case. If we’re in quarantine, it’s a guarantee. If, for instance, large gatherings and live events are still suspended, it seems unlikely that parents will want to send their kids back to daycare, daily school, or college dorms.   

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In his excellent piece on the history of Ed Tech, Martin Weller writes “there has always been something of a year-zero mentality in the field”. While there are many fast-growing EdTech startups in the market today, the majority have found their fit outside the established stages of formal, full-time education, private and public. Over centuries of growth, private education has become a colossus of recurring revenue with a perpetually restocked pool of new and returning students all willing to pay for the service – via parents or loans – for years. Perceived as gateways to future success, private schools can charge ever more expensive tuitions without reducing applicant demand. A case of the innovator’s dilemma, there’s no motivation for privates to innovate. Incentives are most misaligned with online learning providers that deliver quality education in a much smaller package. Any threat to their perceived value, and so their acceptable price, has been a non-starter on the private side. On the public side, the dynamics are inverted. Consistently underfunded, public school coffers are emptied just trying to keep status quo academic operations afloat. Furthermore, school districts employ myriad, non-standard processes for new vendor approvals across various administrative levels by state.

Increasingly, it seems the safest option for continuity in the fall is for schools to determine how to digitize and go online before September. The drastic constraints of an extended national lockdown manifest one of the few situations in which ed tech and online learning receive broad adoption, sustained usage, and meaningful buy-in from students, instructors, institutions, administrations, school districts, and parents simultaneously. A lockdown semester can be an immense proving ground for online education and a lifeline for the industry. On a partner relationship level, with schools directing their services to web-video, much clearer and likely expedited tracks will be laid for relevant service providers to engage with the institutional stakeholders. Where previously there have been administrative buffers obscuring these connections, now everyone -- from department heads, heads of schools, and anyone involved in budgetary purse strings -- will be directly involved in product evaluation and onboarding. The same should be true for executed contracts, accounts receivable and real revenue vs. free trials and MOUs of the past. Of course, bookable recurring revenue commitments and contract lengths probably won’t extend beyond the first semester of the 2020-21 academic year, with some schools eager for outs in the event of campuses reopening. Nonetheless, it’s a step forward for ed tech-online providers to be approved and paid by institutions.

Lastly, on the real integration progress, there’s more value than meets the action in relevant education users – namely teachers, professors, and students – actually signing up for and creating active profiles with various online education products. Ghost accounts are all too common for services that aren’t free and aren’t required for school or work. For the sake of measurable usage as well as reengagement , honest and intentional user signups matter. Obviously, the administrative categories above are pretty mundane but they’re not insignificant given that each one has been a barrier to market penetration to date.

This summer will be a heavy lift for academia taking distribution to the cloud and shifting to digital tools to communicate and collaborate. They’ll need to digitize course materials and syllabi, potentially shift on-campus financial aid to broadband wifi subsidies, and focus on test systems for virtual attendance, participation, and evaluation. Recognizing that some fields of study will transition more easily than others, there’s much to be determined running up to September. If robust plans are conscientiously made by and for all participants, led by institutions intent on making the most out of a semester in quarantine, the fall semester could create the first real-world datasets for formal, online education at true scale. Completing a semester online-only can thus serve a clear and present value to enrolled students and also inform future years of discovery and development in ed tech and online learning.

A priority for all schools not related to new technology adoption is recalculation of their tuition for a semester online. On a practical level, certain core costs of operating a boarding or day school/college don’t exist with campuses closed. Two glaring examples — Student Housing and Student Meal Plan — accounted for 34% of undergrad tuition paid in the U.S. last year. Students will live and eat at home if lockdowns continue into the fall. Particularly during a devastating economic environment, it would be appalling for any school to expect parents to pay twice for these services in a pandemic. Lawsuits are already piling up from parents seeking partial tuition reimbursements for the abridged Spring semester. That’s distressing heading into a potential full semester at home. At a minimum, tuition costs normally incurred due to students’ physical presence at school should not be charged this fall. It won’t silence all the haters, but proactively offering a 1/3 tuition discount this coming semester would be a strong place for educational institutions to start in leading the way through a transparent shift to pandemic education online.

Regarding other cost conflicts, there’s undoubtedly a gray area in interpreting the dollar value of formal academic instruction. There are, however, ways to redirect existing tuition line items to improve the likelihood of success for digital classes. High fidelity wireless broadband and low latency digital conferencing will both be crucial. Many student and teacher homes don’t have access to either right now. Failure to adjust tuition costs seems like the surest way for the education industry to stumble out of the blocks ahead of a quarantined fall semester. With cooperative participation essential now, the prospect of schools squeezing customers for legacy pricing while their services are constrained risks alienating student bodies and infuriating  parents. There’s more on the line for institutions then a single bad year excused by coronavirus. Forever losing whole graduating classes of alumni to the annual giving “Do Not Call” lists could compromise future endowments. More widespread scrutiny of tuition cost versus real value could damage application rates.

With louder voices saying that it’s time to build better institutions, this fall can be exactly that for education. The industry can emerge from quarantine with a new, complementary model for school that’s both more flexible and more durable, safer, and available to many more students. The entire industry can successfully complete a semester online. It should then reasonably follow that online learning post-COVID is seen through a more accepting, approving lens. If it’s still seen as subpar then by its own rubric every enrolled student will be seen as ‘less educated’ in 2021 than their predecessors. Should they all repeat? As important as building better institutions is societal acknowledgement of their quality. It takes nothing away from the value of traditional schools and colleges. However if, in their absence, online education is still perceived as just a bandaid needed to fill a pandemic vacuum, we won’t take that leap. Until we reward excellent online education with the same legitimacy and prestige that we hand in-person graduates along with their diplomas, it’s more likely that this fall plays out as a strange pandemic blip rather than a course-correcting watershed for all levels of learning.

Pandemic Binge

Predictions populate the startup sphere at the end and beginning of each year: times of reflection and looking ahead. Right now, it feels more like we’ve slipped dimensional seams of the multiverse, living in an alternate reality in 2020. Since we’re all new to a global pandemic driven by a virus so fluid that it has forced  swaths of the world population to shelter in place, I’m thinking about industries that are transforming in the time of coronavirus. It seems like many sectors and business models that survive the pandemic may emerge in a post-vaccine world almost unrecognizable. I’m starting here with three entertainment categories: Broadcast Cable Networks, Movies - Theatrical Release, and OTT Streaming.

Broadcast Cable Networks

Over the past 5 years, cable TV has seen cord-cutting go from a dismissible future concern to a clear and present  threat as subscribers steadily cancel service for OTT streaming. Grim Q1 subscriber churn set the table for the quarantine economy to hasten cable TV’s decline. It suggests a tenuous future for the business post-COVID. Combined, the big four pay TV providers lost 5% of their paying subscriber total in the first three months of this year. As lockdowns swept the country, cable TV lost its most dependable, differentiated, and defensible entertainment moat: live television. Live sports are all on hiatus; other live events have been cancelled. Morning and late-night talk shows are shadows of their normal production. The single, scary topic on daily news has overstressed viewers tuning out.

For now, at least, the timing of corona lockdowns offers a tiny bit of solace for broadcast cable reeling from the collapse of their live tv coverage. The majority of networks’ series and programs currently on the air are pre-recorded and can run as planned through the end of the spring season. Airing reruns and old movies is standard on network TV during the summer months, audiences are used to that lull. Unfortunately, that timing band aid won’t last. Interviewed on a Ringer podcast at the beginning of April, director and writer Alan Yang laid bare the a looming issue for TV and Film…“the lockdowns obviously delay series that are airing now and were still in post on a season’s final episodes...but the real problem will be delayed...more like 6-8 months from now because nothing is getting produced now that would air/premiere six months ahead from today.”

Scripted network television operates on a set 12-month calendar to ensure development and promotional stages have adequate lead-time running up to predetermined release dates. The annual ops year has remained largely the same as it was when the networks launched in the 1940s. So, the plan for 2020 was no different. Coronavirus lockdowns have likely erased six months of spring and summer production. All major networks are facing the brutal likelihood of arriving at their broadcasting start month with none of their returning series or new pilots ready to air. Prior to the rise of OTT streaming, that reality would be painful for cable networks. Today, a September without cable’s returning lineup will likely have long-term consequences for whether or not the businesses survive post-COVID.

Cable providers and broadcast networks do not have consistently robust archives, especially of movies. Due to the complexity of licensing between all entities involved, there’s major inconsistency in what’s available when on-demand in terms of old shows, earlier seasons, and licensed movies from each channel. Furthermore, most networks-providers digital and mobile interfaces turn “ease of access” into a struggle for viewers. Many channels require the user to choose to make each show available on demand, while the cable provider interfaces treat each network like a siloed channel interface. Nothing is easily available within a few clicks.

In the absence of live sports, there’s already been rumbling from customers for refunds from pay TV providers If those same customers are left with nothing but series reruns and limited movie options, cord cutting could be fatal for cable TV. There is a chance here for cable networks to finally experiment with new models for introducing new content, timetables for development, and catching up with the competition in web/mobile experiences.They have nothing more to lose by trying, though it will require a brutally honest recognition of past mistakes and failures. For instance, broadcast networks still rely on an expensive slate of productions, with just a few hit series retaining audiences year to year. Since 2010, across all networks, 58% of original programming is cancelled every year. Furthermore, clinging to outmoded pricing from legacy customers who may not realize what they’re paying is a failing proposition. In 2020, the average cable TV customer STILL pays $107/month for the box and bundle. That same customer could stack 4-5 OTT platforms and still save on the cable bill today. There may be a path forward post-corona virus, though it will require reducing expectations of revenue and growth for now.

Movies - Theatrical Release

For the film business, where both creating supply and realizing demand depend on the ability of large groups to gather, the coronavirus lockdowns have already paralyzed the industry. With productions delayed indefinitely, studio lots vacant, and theaters closed, the Hollywood box office, which normally earns $11B annually, has been taken off the board. Not unlike cable TV and cord cutters, moviegoing audiences have been gradually shrinking for twenty years. In 2018, 336 million fewer movie tickets were sold compared to the industry’s peak in 2002. The rising cost of movie tickets -- nearly doubling over the same period -- has obscured this decline from reflecting in top line revenue. That contraction has not frozen the whole business, but I think there are a few emerging consequences of a Hollywood on ice during the virus.

There is a real possibility that theaters won’t reopen on a scale that facilitates standard nationwide premieres for the foreseeable future. In response to that scenario, many major theatrical releases planned for the end of the year are being pushed back into 2021. Even films that had scheduled domestic releases much later in 2021 are pulling the theater plan and cutting deals to premiere on Netflix or other streaming services instead. The new production gap mentioned above will also severely limit the number of films in the can and ready to be released in the fall/winter even if theaters are open. Arriving at the end of the year with no star-studded blockbuster films hitting theaters will slaughter box office revenue and could shatter the theatrical release distribution model. November and December are crucial for the film industry, accounting for more than 40% of the $11.3 billion total domestic box office revenue in 2019. If studios and theaters rush to spin-up some theatrical re-releases of popular or classic films to fill the void, those will likely turn out a fraction of the dependable, large audiences normally seen at theaters at the end of the year.

The emotional reluctance of moviegoers and state timelines to officially reopen businesses are intertwined. Even if some productions make it into the theaters, even if the virus is tamed and distancing guidelines relax,  every day that passes makes it harder to imagine moviegoers venturing out to crowd into theaters in numbers anywhere near the projected norm for the box office. People are going to be cautious and will be warned to take small steps towards reengaging in normal activity. Large gatherings, especially with whole families, will probably be among the last consumer events to come back to life.

The longest-lasting negative consequence for studios and theaters arises from the complete shuttering of their businesses while their major industry competition, OTT streaming platforms, are surging to new heights. Streaming offers homebound movie lovers of every age and demographic a cheaper, safer, high quality alternative in bottomless quantity.  I wouldn’t be surprised if, in a post-COVID world, theatrical releases and going out to the movies settle into niche consumer activity. Existing theater businesses like Alamo Drafthouse nationwide and Nighthawk Cinema and Metrograph in NYC are already serving the market. These theaters have full-service kitchens, high quality menus, and full bars, all of which can be accessed throughout a film from your seats, which are La-Z-Boy quality plush. Such models would significantly reduce overall box office dollars and, as a higher-priced cinematic experience, would likely be reserved more for special occasions and special films rather than remaining a regular family experience.

OTT Streaming

A lone bright spot in battered public markets, Netflix shattered analyst revenue projections in Q1 2020 by adding 16 million new subscribers. At first blush this might seem extra impressive given the pandemic. Growing their total subscriber base by nearly 10% in one quarter is amazing for sure, but not the shocker that blindsided Wall Street. Reid Hastings said in 2018 that his company wasn’t competing with all the other new streaming options, Netflix was competing with our sleep. A global shelter-in-place order is a dream come true for the company that invented binge watching and is by far the closest thing to a generic eponym for streaming services. Netflix in particular and the OTT category broadly are uniquely built to thrive and lead during COVID and immediately thereafter.

The lockdown confinement advantage is a major positive swing for OTT providers right now. We are all stuck at home, with very little to do, hungry for distractions, delights, and diversion from the real world outside. Of course, it will end at some point and viewership hours will revert to normal. However, given the probability of a more enduring economic downturn as well as the subscription model for all the TV providers (OTT and cable), this lockdown surge won’t be a one-time blip in streaming popularity. Within OTT streaming’s most unprecedented collective binge, there’s a distinct exercise happening with real implications for OTT long-term retention. Approaching Month 3 of lockdown, viewers have binged beyond their favorite programming lineup and so, consciously or subconsciously, are devoting many hours evaluating their content providers: How deep are their libraries? How many genres do they cover? How easy or clunky are their interfaces to navigate?

This degree of rigorous kicking of the tires doesn’t really happen in the course of peoples’ normal, busy daily lives, but life during coronavirus is a whole new world and, while I’m not a fan of the phrase, content is the king we’re all kneeling before right now. There’s no way the powers that be could have predicted the lockdown, though it seems prescient of Netflix to have dropped so much acquisition money over the last few years to beef up their library alongside all the original programming. Expect to see a much higher degree of long-term consumer lock-in for the OTT providers they choose during a period of intensive consumption.

A regular thread in the emergence of the OTT competitive field has been how much signing up for multiple streaming services feels like cable bundle and will soon cost more too. In reality, the cost comparison is the opposite. Shockingly, and likely aided by a substantial number of long-term subscribers who don’t know better, the average monthly cable TV bill is $107.00 in the U.S. $107!!! Add up monthly subscriptions to Netflix, Hulu. Disney+, HBO Now, and Apple TV and at $45/month this streaming bill saves  consumers more than 50% compared to cable. They could even add in a Live TV OTT like YouTube TV and still pay less than the current cable bill.

That true cost comparison will likely be revealed to more consumers due to pandemic impact on the economy. Payer price sensitivity is being felt in an acute spike, especially for those on lockdown without any income. It will have an even more widespread impact in a post-COVID economy with consumers looking for ways to reduce their monthly expenses. The custom streaming bundle is cheaper than pay TV head-to-head regardless of pandemic. In a pandemic country on lockdown, with cable TV having lost its live programming advantage, the inflated cable TV bill seems all the more glaring. Switching to OTT providers will be a no-brainer for more consumers. The cord cutting floodgates may just burst open in the months ahead.

When Netflix dropped all of the first season of House of Cards in 2013, the company blew up the timed-release marketing-distribution playbook that was industry standard. The company took a significant risk on the notion that, if the content was good, audiences would swap anticipation and premieres for volume and availability. That theory proving out since, they’ve been rewarded with ultimate flexibility and autonomy in how and when they release. Not beholden to concrete lease plans, they can be strategic and responsive to genre popularity trends. They can test bolder creative risks on a 365 days-a-year release window. And, they can adjust on the fly to external issues, from production delays to global pandemics, without sacrificing their style or reputation. We’re used to Netflix as a place to find interesting stuff to watch, stuff that often experiments with episode, season, and series constraints. This makes dynamic OTTs more durable through an industry-wide production shutdown. Interruptions, delays, and some shows that just don’t come back -- all are much more copacetic on Netflix and streamers. For broadcast networks with audiences trained to watch on a regular and consistent programming schedule, these same issues may cause much more friction.

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A coronavirus effect on the entertainment industry so far seems to be accelerating the adoption curve for new technologies transforming how we watch TV and movies. Life in lockdown won’t last forever, but post-pandemic Hollywood may reveal an industry that’s leapfrogged a few stages in the broad shift to being streaming-first.

Here Be Dragons

A few weeks ago, I went to the Special Collections – Rare Books Division on the 3rd floor of the New York Public Library’s headquarters at Bryant Park to see The Hunt-Lenox Globe. As a history nerd and a fan of cartography, I fell in love with the intent of the phrase Here Be Dragons, first found etched into the Hunt-Lenox Globe, c. 1504. On the earliest maps of the world, here be dragons identified areas that were either unknown or rumored to be perilous to seafaring travelers. Avoid at all costs. It’s my title because the phrase strikes me as an appropriate identifier for the perilous and unpredictable state of our rapidly warming planet. Where Here Be Dragons was once reserved for remote corners of the map to avoid, in 2019 it can justifiably represent every inch of the globe that none of us can avoid. As we knowingly push the planet up against any known limits of its ability to sustain human life, I want to write a few thoughts on what new trouble may be immediately around the corner, areas of innovation emerging to lead us into a survivable future, and finally some dead simple, minimal effort changes anyone can make in their day-to-day to meaningfully help avert catastrophe.

I don’t put this into words in order to try and convert climate deniers into advocates. I’m really appealing to the apathetic majority, that represents the largest population and so the most cumulative potential to actually fix things. The generally good people who still rib their colleagues saying things like well I’m no tree hugger to absolve themselves or who may be unmoved or simply overwhelmed by the events and heavy statistics now being recorded every day on our planet. And, I’m writing this now because, honestly, I do not know what else to do. So here goes, for anyone who reads through, I really appreciate it and hope it’s helpful.

Part I. Endless Summer

 Coastal Property Value Collapse

Today, one in three U.S. residents live on a coast, to be specific in a county directly adjacent to the Atlantic Ocean, the Pacific Ocean, or the Gulf of Mexico. As a matter of function and fashion, our coastlines also store a staggering 73% of the total value of the U.S. housing market.[i] Naturally, coastlines bear the brunt of impact from storm surge and sea-level rise. News headlines tell the stories of coastal damage and recovery efforts in the aftermath of a major storm. Foreclosures in the Rockaways, Houston-area communities abandoned, Miami underwater from king tides. Between the lines of losses from any one storm, more devastating consequences are eating away at the long-term viability of the coastal real estate market. Analyzing data compiled by HUD, the four primary indicators of housing market health – total new homes on the market, total new home construction, average number of transaction, and average transaction size – have all been on a decade-long decline in U.S. coastal counties, by as much as 11% in certain regions.[ii]

As of yet, no single storm has permanently drowned a housing market, but recovery in the face of more intense and frequent extreme weather now means unrecoverable losses of value that can’t be rebuilt or made whole from insurance claims. Examining the strength of the coastal housing market relative to comparable inland control groups exposes the negative impact that sea-level rise has already had. Research from First Street Foundation and Columbia University found that the eastern seaboard has already lost a combined $15.3 billion in relative property value due to the impact and growing risk of tidal flooding since 2005.[iii] The lynchpin preventing collapse at the moment is a vital safety net for all real estate markets: insurance. Proof of property insurance and, in coastal and flood plains states flood insurance, are required for all FHA mortgages to protect the value of the loan. Insurers don’t like to pay claims; they really don’t like to payout full policies; they don’t make bets when the odds aren’t in their favor. When a coverage area experiences a higher rate of policy payouts, the losses can be severe short-term – bankrupting California’s largest public utility in January – and long-term if property insurers stop binding new policies in the area at risk.

Effectively declaring a locality uninsurable is a death sentence for its real estate. Real estate markets begin and end with insurance. If you can’t get a property insured, then you can’t get a mortgage. If you can’t get a mortgage, then you can’t own a home. The inability to get new mortgages or refinance existing ones is a mounting possibility in coastal communities and the trigger that can take a slowing market into a collapse. Panic sets in for any sector when new business can’t be done and, for coastal homeowners with significant net worth tied to properties at risk, there is very little action that can be taken to reverse the losses.

For some, the extreme flooding headlines haven’t hit home yet as, to date, the surge in tidal flooding has had a more pronounced impact on coastal geographies that aren’t as wealthy. Many rich coastal enclaves have spent the past ten years building extravagant protections for their properties, including steel stilts, elaborate sump systems, and erosion barriers dredged from sand on the bottom of the ocean. No matter how expensive these Band-Aids are, they only delay the inevitable. Tidal flooding and global weirding are indifferent to socioeconomic status. No matter how insulated a beachside mansion is from extreme weather, the property value evaporates if the surrounding area is abandoned or submerged. You can’t throw money at problems this complex. Unfortunately, it stands to reason that the headlines will become most dire when the playgrounds of the rich come under flooding threat. First Street Foundation and Columbia University conducted a follow-up study to examine the projected risk to coastal property value from 2019 onward. Over the next decade, the Hamptons, West Palm Beach, and Cape Cod stand to lose a combined $10.4 billion in property value.[iv] There’s bright red writing all over the walls at this point. Its straightforward to understand the massive, collective loss of wealth that would result in a full-scale write-off of coastal properties. More likely to be a sledgehammer to the cogs of the system than a bursting bubble, there’s a point of no return, literally, when mortgages start being denied across the board.

Population Density Endures Extreme Weather

The total population of the U.S. has doubled since the mid 20th century, thanks primarily to hyper population growth on the country’s coastlines and in the eastern floodplain.

United States Coastal and Eastern Floodplain Regions

In fact, since 1960, the three U.S. coasts and the eastern flood plain have combined to outpace global population growth by 2.5 times.[v] Layering the above map over the U.S. population distribution maps for 1960 and 2017 from the US census bureau highlights this concentration.

United States Population Distribution, 1960

data source: U.S. Census Bureau

United States Population Distribution, 2017

data source: U.S. Census Bureau

The problem is that these two regions are the most susceptible to direct impact from the two most destructive natural disasters: flooding, across the board, and wildfires, specific to the pacific coast. In recent years, two emerging trends of global weirding related to climate change are amplifying the risk of living in these areas: the increased severity and frequency of natural disasters. That we’ve entered new territory for natural disaster severity is evident in the FEMA historical archive, where a new record for total damages seems to be set with virtually every successive storm, flood, and fire. Over the past ten years, we’ve experienced the seven worst hurricanes, the three worst wildfires, and three of the four worst floods ever recorded in the U.S.[vi] The mounting losses from any one of these events, by measures financial and otherwise, are obviously enormous. In the shock reaction to each extreme weather event, the accelerated frequency with which they happen can become obscured. However, the country is now enduring successive natural disasters at an unprecedented clip. In a 27-day period in 2017, hurricanes Maria, Irma, and Harvey destroyed $300 Billion of property.[vii] Just two weeks ago, the National Weather Service announced a new record for the largest number of simultaneously active named storms:

source: National Weather Service via weather.com

Extreme weather that’s more costly and more common is a trend that’s been building steadily over the past three decades. FEMA is responsible for official weather declarations and includes earthquakes, floods, hailstorms, blizzards, hurricane storm surge, tornados, and wildfires in its categorization of natural disasters. Up until the late 1980s, the United States averaged fewer than 30 individual disaster declarations per year. Since 1990, the country has experienced an eye-popping 4x increase in annual natural disasters. When the same areas are hit, harder and harder, and much more often, there is a compounding effect on the costs to recover. The graph below charts the 30-year trend in natural disaster declarations along with the resultant annual damages in dollar terms, which have grown 10x.

data source: FEMA.gov

While extreme weather is indifferent to wealth, the immediate term effects will differ depending on it. Rich people in these areas may see net worth shrink or may flee immediate danger zones, poor people will lose lifesavings or entire homes without financial protections that allow them to rebuild. Ultimately, though, we are all at extreme risk. If the natural disaster trend lines above continue unabated, everyone stands to lose everything.

Our Diets Are Forced To Change

Over the past few decades thanks to advances in large-scale machine-based harvests and yield management technology, we’ve become remarkably efficient at farming. Some analysts would argue we’re way too good at it. The prodigious production that’s filled groceries with fresh everything and given a few generations lots of options for what we eat still depends on arable land to graze livestock and plant crops and seasonal weather to sustain both and harvest appropriately. Without predictable rainfall, the system stops producing. Climate change and global weirding threaten to upset the delicate atmospheric balance that’s given us the opportunity to feed ourselves in such abundance.

There are two crippling agricultural consequences of temperature increases: high-intensity rainfall and extreme drought. This doesn’t sound like it makes sense as one could solve the other. However, it’s that type of uninformed logic that’s allowed far too many people an out to dismiss the entire crisis. These two events don’t happen in predictable sequence or in the same places. When air warms, it holds more moisture. More moisture in the air makes more intense rainfall when it happens. Unexpected heavy rainfall can ruin crops outright and any instance where flooding occurs can render entire fields useless forever. A temperature increase also accelerates groundwater evaporation. This in turn creates extended droughts and water shortage. Today, 80% of the world’s crops are rain-fed and another 10% are irrigated with groundwater.[viii] Keep in mind that crops don’t just mean the vegetables we eat, corn is the primary resource used to feed livestock globally.

So, as you can see, the strain on our food supply as a result of small climate increases is being felt and can continue to get worse without urbanites feeling any meaningful difference in their own temperatures or extreme weather. At the moment, crop yield for the top 10 most commonly produced, imported, and consumed foodstuffs in the U.S. is decreasing by 1% every year.[ix] That’s in current circumstances and despite all of the farming advancements. Might not seem like much however it amounts to enough calories to feed 50 million human beings wiped out, every year. Those foodstuffs categories include or directly reduce: avocados, beer craft and otherwise, every cut of red meat, and, yes oh yes, coffee.

Like the coastal housing section, the immediate term consequence from our economic and food system will be rich people stockpiling through expensive imports and everyone else struggling. Also, in any complicated crisis, things tend to get uncomfortable first and unlivable later. Ideally, you start changing your eating habits now, but if you’re a stubborn beer swilling, steak and potatoes manly man, be prepared to pay craft brew and filet mignon prices for Natty Ice and Salisbury steak quality.

4 Seasons become 1 and 1/4

This impact may seem more superficial than the previously three, its not and its onset sucks either way. Anyone living in the Northeast United States for the past decade has already started to experience this reality: our transitional seasons, Fall and Spring, are rapidly disappearing and a winter is becoming a short, frozen blast. The definitive and only culprit is global temperature increase. Since 2010 in Central Park, the average temperatures for March and April as well as September and October are 3 degrees Fahrenheit higher than the average historical temperatures for those four months since we started recording them.[x]

A three-degree bump may be pretty easy to ignore, especially in modernized big cities. We may even welcome it. There may be no more significant factor contributing to societal apathy towards the climate crisis than the broad human preference for warmth over cold. Everyone loves sun shiny days. We don’t balk at an earlier spring thaw or do anything beyond celebrate extra summery days into October. Maybe there’s passing regret for fewer days to see the foliage change or a shorter PSL season. Like many slow-build calamities in history, rising temperatures may well be hailed as a pleasant change, right up until the moment that it’s too hot and, unfortunately, too late to do anything about it.

The direct implications of fundamentally changing our planet’s natural seasonal progressions are dire. A shift from air conditioning as a comfort to relying on it in order to function will put unsustainable pressure on the country’s energy utilities, many of which are already giving out under current, skyrocketing resident demand (see: PG&E, California’s largest utility, bankrupted by wildfire related insurance claims last year and instituting mandatory black outs today). With broad medical acknowledgement of seasonal depression, it’s not difficult to imagine the new physical and emotional health problems that will result from life lived indoors. Furthermore, the intermediate knock-on effects are extreme and destructive. Shorter winters means less annual snowpack in the country’s western ranges. So, for the wealthy, ski trips become memories from yesteryear. Far more importantly, snowpack that melts earlier in the season causes unexpected flooding throughout the Midwest and contributes to harsher, longer drought west of the U.S. mountain ranges into California.[xi]

As with many of the climate crisis effects today, the seasonal disruption is less a doomsday prediction and more an increasingly present reality. 2018 was the fourth hottest year on record. The three years ahead of it? 2015, 2016, and 2017. There are solutions that can help solve this problem and they need dedicated, intelligent minds to see them through. If the three degrees Fahrenheit bump we’ve already experienced becomes six, then eight, then 12, the great outdoors will become largely off-limits for human beings.

Part II. Effective Innovations

Alternative Protein Delivers

There are few sectors where the supercharged startup hype machine has been more tangibly beneficial than plant-based proteins and alternative meats. With a better than expected IPO from Beyond Meat, fast food chains lining up to rapidly deliver distribution to mass consumer segments. Progress has been made when general consensus moves from sounds gross, tastes not as good to oh wait, its delicious? And its cool? American tourists are now visiting New York City not for pizza or steak but for a hamburger made from plants. Thank you David Chang.

The chasm we now must cross is in expanding and diversifying the menu, so that the current dietary evolution doesn’t stop at the impossible burger. Culturally, we must overcome the culinary subset of toxic masculinity that says manly men eat meat. A concept that’s deeply ingrained in American identity and extremely detrimental to the environment. The enduring demand for beef in the states drives subsistence farming tactics like slash-and-burn methods in other parts of the world and puts exponentially more carbon into the atmosphere – more than any other contributing factor. The most important thing to remember is that this dumb western edict isn’t true. However, you define ‘manliness’ it can be achieved without meat. You can get yoked in the gym off plant-based protein and it’s much better for your overall health.

If we can course correct our consumption culturally and continue to embrace entrepreneurial efforts to set out tables with delicious alternatives, then prices will become competitive and everyone will be able to benefit. Here are some delicious alternatives startups emerging today: Soylent, Hungryroot, Exo Protein Bars, Ripple Foods, Wild Type and Simple Mills.

Renewable Energy Achieves Pricing Parity

Historically, alternative energy options have been limited by non-competitive pricing. The mass market wouldn’t pay a premium for renewable energy, in their homes or their cars. This is because consumers don’t want to spend extra cash for an intangible or indirect benefit, or they simply couldn’t afford to do so. Thanks to the indomitable efforts of scientists and entrepreneurs, those prices are finally dropping to a point that they are competitive with traditional fossil fuels, without subsidies.

There are two particularly remarkable areas of innovation that helped make renewable energy an affordable option. One is on the energy sourcing side and the other is on the energy storage side. The first is rapid progress in solar photovoltaics, or solar panels. A unique attribute that’s allowed meaningful adoption of solar panels ahead of the pricing parity curve and other renewables is how easily panels can retrofit to existing infrastructure and plug into traditional energy grids. Citibike docks are powered by solar panels and new buildings regularly install solar panels on new roofs. That early adoption combined with persistent research and development to increase panel efficiency have brought the price of solar panels down 73% since 2010.[xii]

The cost to store alternative energy and still have it readily available to use has contributed to limited adoption of hybrid and electric vehicles (EVs) up to this point. The average MSRP for electric vehicles still hovers about $10k more than comparable gas-powered vehicle models. There’s been notable progress recently in the reducing the price per kilowatt hour of energy storage in the lithium-ion battery packs that run electric vehicles. Electric vehicles face a dual challenge in needing to lower costs that produce a battery pack with a significantly increased carrying capacity. In order to compete with gas-powered cars, EVs need to price competitively and be able to drive longer distances, faster, on a single charge. Fortunately, both these requirements are rapidly being met by the industry. As the chart below shows, the cost of lithium-ion packs for EVs has dropped 80% since 2010 is projected to continue to fall.

This breakthrough will allow EVs to price at parity with and eventually be significantly less expensive than filling up a tank of gas. The significance of continued development of lithium-ion battery efficiency and capacity can’t be understated. In fact, just last week, the scientists responsible for many of the innovations in this field won the Nobel prize.

Autonomous Fleets Reduce Carbon Emissions

Deforestation is the single largest contributor global warming, but combustion engine emission is a close second.[xiii] There about as many positive are there are negative opinions and headlines regarding autonomous driving right now. I believe that’s to be expected. Autonomous systems are incredibly complex to build, test, and prove out and the technology is only just emerging. Furthermore, when a new technology has the potential to dramatically change a massive incumbent industry like auto manufacturing as well as the predominant means of transportation over the past 100 years, dismissiveness and flack are inevitable. (FWIW, the automobile endured the same fearful headlines during the early 20th century).

As it pertains to the climate crisis, I’ll focus on the impact of autonomous vehicle adoption at scale. Though it’s cool to experience taking your hands off the wheel in a Tesla today, independently autonomous vehicles (AVs) are more demo than endgame for the industry right now. The profound shift will be AVs that operate as fleets or on an interconnected mesh that lets them adjust speed, acceleration, deceleration, and directional path in real-time coordination with one another. This would represent a step-function improvement over the status quo where each driver decides all of those variables on their own. Human beings aren’t good drivers. That’s not to say we aren’t intelligent enough to get licensed and drive. It’s just that we are constantly distractible. That danger combined with incongruous acceleration and breaking are what create traffic congestion and what kill 100 people every day in auto accidents in the U.S.

AVs on connected networks may take a variety of forms. Fully autonomous fleets may be deployed through ride-hailing services and taxi liveries. Privately-owned AVs may plug into a mesh network whenever turned on or when they come within a certain distance of other AVs. Whichever distribution method puts them on the road, AVs will gradually mean fewer total cars being driven thanks to ride-shares, carpooling, and the ability for the network to recognize shared destinations for passengers and so pickup along the most efficient paths. A recent Center for American Progress research report cites an MIT City Lab study on AVs in urban settings to conclude:[xvi]

“Fully autonomous vehicles incorporated into ride- and car-sharing programs—known as shared autonomous vehicles—could reduce the number of vehicles on the road 80 percent while still getting every passenger to where they need to be, when they need to be there.”

Fewer cars and less traffic means less carbon in the atmosphere. The additional environmental kicker, of course, is that the majority autonomous vehicles being developed and introduced today are 100% electric or hybrids. Since the Nissan Leaf, we’ve already experienced the redesigning of the vehicle to maximize energy efficiency throughout their systems. We’re still years away from this hyper efficient transportation, however, the foundational steps are being taken today by big tech companies and entrepreneurs alike. The next time you’re in the BK Navy Yard try out the Optimus Ride autonomous vehicles in operation there.

Farming Goes Vertical and Aquaponic

A particularly hopeful area of innovation today is in redesigning our agricultural structure and process. The traditional farming industry discussed earlier via which we all eat is as massive as it is efficient. It’s loaded with layers of lobbyists and subsidies that have aided in ensuring our continued dependence on it. Many of the emerging ag innovators, however, don’t enter the market as threats to either Big Ag or local farming. Much like the potential integration of autonomous vehicles into Big Auto, these new methods can be the next evolutions of the existing industry especially if they find processing and distribution partners in the existing agriculture establishment.

Vertical, indoor farming provides growers direct controls over many of the natural variables – sunlight, rainfall, temperature – that are becoming unpredictable due to global weirding. As the name indicates, vertical farming is uses significantly less square footage and does not put repeat stress on soil the way that field farming tends to do. This dynamic also makes vertical farming viable in urban areas, reducing the cost and carbon-emissions of harvest delivery to cities. Bowery Farming and Freight Farms are two leading startup examples. Another even newer indoor farming system is the aquaponic farm system. These maximize yield and sustainability at each farming cycle stage. The most complex examples are integrated symbiotic systems: the nutrient-rich water from raising fish provides a natural fertilizer for plants grown, the plants in turn help purify the same water that circulates back to raising the fish. It’s fascinating to see and there are many local examples.

I believe it’s crucial that these potentially transformational new farming techniques not be seen or positioned as ways to sustain agricultural supply to overwhelming demand in a world that’s losing its naturally arable land or predictable rainfall. These methods should be viewed as evolutions of the industry that, with the proper marketing and distribution, can help make farming as sustainable as it is productive.

Part III. When Our Powers Combine

Four Changes Everyone Can Make

I’m going to conclude with four dead simple, everyday changes that everyone can decide to make and commit to continuing today that absolutely will help end this crisis.

1. Skip meat twice a week: if everyone in the U.S. did this, it would achieve the carbon emissions equivalent of taking all the cars, trains, and planes out of operation for a year. Just think about that for a second. Its remarkably easy to not eat meat two out of seven days. Its also way healthier for you to eat more plants, more often.

2. Recycle correctly: Everyone knows how. Separate your plastics and metals, your compostable trash, and your papers. Pro tip 1: done correctly, your recycling bin will likely be much faster to fill up, so have a larger bin for recycling and a smaller one for compost. Pro tip 2: recyclable papers include all types of cardboard, especially pizza boxes. Just do this. I know, laziness and selfishness is a thing and it’s much more tempting when your home alone and no one is looking. Hold yourself accountable for the few extra seconds it takes to recycle.

3. Carbon credit your air travel: If you fly often for work or pleasure, this action is as easy as clicking yes on the insurance option while booking a flight. The additional cost is minimal and, if its work air travel, you aren’t paying for it.

4. Stay Informed: Climate change maybe the only issue that entered the Fake News era already saddled with myriad claims that it’s not real. Poorly informed participants will cut the legs out from under any of the above efforts. Read, listen, and stay up on these issues.

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end notes:

[i] https://public.tableau.com/profile/zillow.real.estate.research#!/vizhome/TotalMarketValue/States

[ii] https://www.huduser.gov/portal/ushmc/hmi-update.html

[iii] https://assets.floodiq.com/2019/01/a22bd29b007a783c7d3fa7f5c4531c9a-ne-homevalue-loss-slr.pdf

[iv] https://assets.floodiq.com/2019/01/a22bd29b007a783c7d3fa7f5c4531c9a-ne-homevalue-loss-slr.pdf

[v] https://census.gov/history/www/reference/maps/population_distribution_over_time.html

[vi] https://www.fema.gov/media-library/collections/339

[vii] https://en.wikipedia.org/wiki/List_of_natural_disasters_in_the_United_States

[viii] https://www.globalagriculture.org/report-topics/industrial-agriculture-and-small-scale-farming.html

[ix] https://www.worldbank.org/en/topic/agriculture/overview#1

[x] https://www.weather.gov/okx/CentralParkHistorical

[xi] https://www.nytimes.com/interactive/2019/09/11/us/midwest-flooding.html

[xii] https://www.engineering.com/ElectronicsDesign/ElectronicsDesignArticles/ArticleID/16694/Oil-Company-Says-Renewable-Energy-is-Cost-Competitive-with-Fossil-Fuels.aspx

[xiii] https://www.epa.gov/ghgemissions/global-greenhouse-gas-emissions-data

[xvi] https://www.americanprogress.org/issues/green/reports/2016/11/18/292588/the-impact-of-vehicle-automation-on-carbon-emissions-where-uncertainty-lies/

Human Nature and Consumer Demand

This past summer I spent some time learning about genomics from a frontier tech perspective seeking to identify areas of startup opportunity. It was fascinating to be a complete noob in a sector and absorb all of its complexities as best I could. During those months, there was one very specific consumer genomics product that I found frustrating: DNA-informed exercise plans. The question of how much our genes can inform and improve how we exercise today is not the subject of this post or what nagged at me. I couldn’t see past a more fundamental piece of the human condition that the genomics for personal fitness products seemed to have left behind in the all-out effort to market from the cutting edge – people don’t skip the gym because their workouts aren’t hardcoded to the order of their nucleobases. People skip the gym because it’s easier than going. The negative impact of skipping is not clear or felt in any immediate term. The health and fitness upside of going takes a long time to realize and consistently going takes consistent effort. In short, it felt that DNA-based workouts may have unknowingly dismissed the core challenge of motivation. Human error is the largest problem waiting to be solved by consumer fitness startups.

I started thinking more broadly about human nature’s influence on consumer sectors when reviewing this chart from Bureau of Labor Statistics data that Fred posted last October. What stands out to me, aside from the clearly diverging cost trends, is that the goods and services categories with dramatically increased costs also belong to sectors that require more participatory effort from the consumer in exchange for deferred outcome gratification. Conversely, the goods and services with steep cost reductions generally belong to consumer sectors that deliver much more immediate gratification with relatively minimal effort required. Referring back to the BLS chart, consumer preference (and so demand and so increasingly affordable goods and services competing for that spend) has clearly been for sectors that don’t require a lot of effort and deliver their positive impacts relatively quickly. See: TVs, Toys, Clothing, Smart Phones. In an effort to show these dynamics more clearly, I came up with the following graph and plotted a few consumer sectors here:

The x-axis represents the range of time to satisfaction from immediate to deferred. The y-axis represents the amount of consumer effort required to engage in or otherwise utilize goods and services in a given category. There is nothing scientific or data-backed in my graph. I’m just using this framework to illustrate my thinking. The categories that fall into the bottom left quadrant (lowest effort, fastest satisfaction) have sustained mass market consumer demand with two beneficial resultant effects for the consumers. First, highly competitive markets produce a greater variety of goods and services to choose from and lower prices paid for those goods and services. Second, these categories have delivered some of the most significant consumer startup successes ever (Facebook, Twitter, Instagram, Amazon, Netflix, Spotify and many others). By contrast, the categories in the upper right quadrant, in addition to seeing a spike in costs, have not yet experienced as direct a benefit from the foundational technology innovations of the past two decades.

I don’t believe this is due to lack of trying. I believe it’s due to the root human motivation to choose or engage in sectors that satisfy quickly with ease. We humans are real-time, me-first, me-now consumers. We want what we want when we want it. We stream more than we read, we use social media more than we exercise, we drink more than we recycle. Looking back through the lens of the effort vs. satisfaction graph, it makes a lot of sense that social networks and marketplaces have sustained a dominant run both in venture-backed outcomes and public market appetite and enthusiasm. Both sectors and their internet-enabled advances facilitate expedited or near-instant consumer satisfaction with less and less effort required. The same thinking can be applied to the usage and saturation of the smart phone. By no means am I dismissing any other quadrant on my graph. Yet reimagined or more challenging categories like those in the top right have problems and so opportunities that technology can and will solve. I do believe, however, that there’s a trap in expecting that the breakthrough technologies and startup models that solved for the bottom left quadrant will be as effective in the upper right sectors. The consumer motivation and engagement dynamics are completely different.

When the inputs change, the outcomes do too. The innovations that produce smarter, faster, cheaper for sectors like education, personal finance, and health-wellness may look and function in ways contrary to or at least diverging significantly from what’s come before. It warrants a deeper examination of how these sectors operate before assuming that the answer is an app for that. Individual human nature is the essential creator of consumer demand. Demand establishes markets and drives competition. Competition combined with sustained demand fuels real innovation. As entrepreneurs and investors taking aim at these sectors, we may need to key into human nature more now. Working to evolve consumer perceptions and nurture nascent demand can lay the groundwork for building truly novel startups that solve the appropriate problems and advance whole industries.