This is the first in series of posts dedicated to understanding defensibility in technology-driven markets.
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New York, 1807—Step off the first viable paddle-wheel steamship and gasp in awe at humankind’s triumph. Harnessing steam, we’re a species no longer held prisoner by the mercurial winds.
In a city 70 years away from its first electric lights, envision trying to predict transportation in the 21st century. How could we possibly anticipate the impact of high-efficiency container ships and massive aircraft on the global economy? What might seem a fool’s errand is, I believe, tractable.
At that time, internal combustion engines, standardized shipping containers, heavier-than-air flight, and turbofans were still unknown; however, we could draw a box around a ship and ask: “What happens if moving things from point A to B becomes faster, cheaper, and more reliable?”
With that framing and a bit of thought, we might conjure the world of international tourism and globalized supply chains.
The general algorithm for predicting the future of technology-driven industries is to know the “figures of merit” and chart their trajectory through time.
With AI we ask: “What happens if intelligence is smarter, faster, cheaper, and more reliable?”
The obvious near term answer is: “Magical new products will disrupt many industries.” True. However, AI is different from an ordinary disruptive technology revolution in two ways:
AI is obvious and available to all, not a proprietary or under-appreciated secret.
The pace of progress in AI is creating multiple technology revolutions in short succession; it’s not a single disruptive technology to be percolated throughout the global economy.
As a result of these two differences, startups must seriously consider competition—both from peers and future disruptors—much earlier in their lifecycle than the past. Let’s look at each:
Because AI is obvious and available to all: great products are necessary, but not sufficient.
In recent history, products that were “10x” better than the status quo became wildly successful.
“Building something people want” became a startup mantra because few could build better products than what customers already had. Unsurprisingly, companies with exceptional products won big. Google, Amazon, Uber, Zoom, iPhone, and so on.
In AI, things are different. For those pattern-matching to the last 20 years, great AI product demos are now a trap. Because 10x products were historically indicative of a great team at the helm, we may be tempted to think that a revolutionary AI product also indicates greatness.
Today, everyone can build something 10x better than the status quo simultaneously. Great AI products might indicate a great team, but given the wide availability of state-of-the-art models, even average teams can build seemingly-revolutionary AI products.
When everyone uses the same revolutionary technology, today’s groundbreaking product becomes tomorrow’s table-stakes feature. Blindly matching the 10x pattern leads us astray.
Unlike past technology revolutions when disruptive products blindsided incumbents, today’s incumbents are aggressively investing in AI. Startups competing on the virtue of AI features alone will get crushed; both can build 10x AI products, but incumbents have a lot more distribution.
When multiple technology revolutions occur in short succession, disruptive startups can themselves be disrupted before they get to scale.
Startups used to focus on product and distribution for years before worrying about defensibility. By betting on technology shifts—“why now?”—startups were pure beneficiaries of disruption.
Historically, industries had many years to digest a disruptive technology before a subsequent wave of disruption occurred. The revolutionary shift from mainframes to minicomputers, then minicomputers to PCs played out over decades. As did the transition from 14 inch to 8 inch disk drives, followed by the transition to 5.25 inch and eventually 3.5 inch drives.
In AI, even if a startup wins during a first wave of disruption, there’s no guarantee they will continue to win. As models become increasingly capable, new architectures will repeatedly disrupt prior generations. The shift from co-pilots to limited function agents and agents to superhuman AGI may take place over a few years. Each of these shifts is likely to make startups built on a prior architecture obsolete.
When multiple technology revolutions occur in short succession, startups must worry about disruption before they get to scale. In this case even immature companies founded last year must worry about disruption from startups in the next Y Combinator batch. Startups are now both beneficiaries and victims of disruption.
If startups 1). cannot solely rely on building 10x products to win and 2). must face multiple consecutive waves of disruption, we’re forced to confront the central question of this series: “What is defensible during a period of rapid technological change?”
Centered upon today, we can rephrase that as “Where will value accrue in AI?”.
To build a durable business in AI, startups must have some form of unfair advantage today that transfers across any near term wave of disruption to come. They need some way of tilting the scales away from competitors and towards them despite substantial technological change.
Why do customers choose one vendor over another? Power. Without it, a company might win for a short period of time, but they won’t win forever.
What is power? It’s the ability to achieve some advantage (e.g. lower costs or ability to charge higher prices) in a way that competitors can’t replicate. Product-market fit is delighting customers, but power is beating competitors.
To illustrate, let’s imagine a bedtime story for my future daughter:
Daughter: Daddy, where does power come from?
[Jeni glances at Kevin, eyebrow raised, as if to say: What have you done to our poor child?]
Kevin: Well, imagine the world of apples… You love apples and your friend Bob sells them for $2 each.
Everyone’s happy. Bob’s business is so successful that he buys a plane and a boat. And you have apples.
But then Alice shows up with a fancy automated apple harvester, selling the same apples for just $1. Being a smart cookie, you start buying Alice’s apples instead.
Bob’s upset! He can’t afford an automated apple harvester, and why would you pay an extra dollar per apple just so Bob can fly his plane to St Barth’s?
Bob doesn’t have power. Instead of buying expensive toys, he should have invested in his apple business. Not smart, Bob.
Now your friend Carol comes along. She’s a brilliant genetic engineer who worked really hard to create a super apple that tastes absolutely delicious. She buys an automated apple harvester as well.
She charges $1.50 for her apples—a little pricier that Alice’s, but way more delicious. You happily pay an extra 50 cents and so does everybody else.
Alice doesn’t have power. Anyone can buy an apple harvester. Not smart, Alice.
A few years later, Carol’s business is booming. One day, she talks to her friends from college, Dan and Eve. They’re also genetic engineers dreaming up new types of apples.
At first, Carol was a little scared. She thinks, “What if their apples are better than mine?”
But Carol is a smart entrepreneur. She remembered that the trickiest part of her business wasn’t modifying the apple genome, but growing millions of genetically engineered apple trees cheaply.
Because her apples were so popular, Carol worked extremely hard for years automating planting, growing, orchard maintenance, harvesting, and delivery with AI. She called it her Apple Workflow System (AWS).
It started off simply with just a few robots she programmed herself, but AI just kept getting better and better. Each time a new AI model came out, Carol quickly incorporated it everywhere she could.
Now Carol has thousands of robots tending her apple orchards with just a few people to supervise them. Her system is so good, nobody in the world can grow apples as cheaply.
And that’s when Carol had her eureka moment. Instead of trying to compete with her friends, why not sell them AWS? This way, her friends can focus on dreaming up new apple varieties without worrying about orchards and trucks.
Dan and Eve loved the idea! They got to focus on what they love—crafting apple genomes—without having to become orchard managers or produce logistics experts.
Fast forward a few years and it’s your dream come true as an apple-lover.
Dan and Eve have several wildly successful apple varieties and Carol is still hard at work making many improvements to the system that runs the orchards that produce them all.
Carol has power.
Notice how we avoid naming structural powers (e.g. “network effect”, “economies of scale”, “brand”)? Using big words views a market in “far mode”: great at justifying who is currently winning but poor at understanding the nuance of who might become a future winner.
Instead, I find it more helpful to think in “near mode” and use customer psychology thought experiments as ground truth. Imagining ourselves as a customer is the best way I’m aware of to avoid the irrational exuberance that accompanies many bubbles.
Specifically, Carol’s ultimate advantage comes from relentlessly building a platform that Dan and Eve don’t really want to build, at a pace they couldn’t hope to match. Carol’s speed is a type of “execution power” that is the subtle secret behind many companies’ success. Because AI gives individuals “infinite leverage”, execution power is especially potent in the post-AI world.
To adequately cover power dynamics in technology markets, I’ll be publishing multiple posts in the coming weeks. We’ll start with principles and then address the $100 trillion question of who will accrue power in AI.
Similar to prior posts, I’ll attempt to be as simple as possible—but no simpler—and, for the sake of brevity, make definitive conclusions instead of hedging with probabilistic language. As always, there are likely to be smart sophisticated and successful (SSS) who disagree with either the principles or some of the conclusions. I hope this spawns some great discussions to come :)
Thanks for reading.
– Kevin Mahaffey
Defensibility
A series of posts dedicated to answer the question: Where will value accrue in AI?
Introduction: Why this time is different.
"Execution power” is becoming more important than classical “structural power”.
Market revolutions occur when “critical" technology makes a new stack “viable”.
When multiple stacks become viable in rapid succession, companies must “AND” or “OR”.
Power within the AI stack—hardware, hosting, models, and infrastructure.
Power in AI applications—big tech, switching costs, network effects, and the $100 trillion of global GDP up for grabs