D-AI-vid vs Gol-AI-th
The right-to-win of AI-native startups
Once upon a time, say 10 to 15 years ago, the tale of startups vs incumbents was an easy one: it was a coming-of-age story where startups had a bunch of assets to leverage against their more established, mature and capitalized counterparts:
they were faster, agile and customer-centric: nimble and quick in product development through a novel methodology infused with MVP, design thinking and product management principles against more stage-gated V-shaped development processes
they benefited from scalability: through the technology they leveraged, they could materialize increasing returns to scale (where a marginal unit can yield extra positive margin indefinitely) – and be appealing to venture capitalists looking for returns.
The combination of those features made them fierce adversaries for incumbents - old behemoths comfortably sat in their value chain - who feared “disruption” or “uberization” (not always with a firm grasp of what those concepts strategically entail, btw).
It was a David v. Goliath trope, a cautionary tale for large institutions to beware of fast-moving, customer-centric, tech-enabled competitors moving into their value chain and a beacon of hope for the small teams trying to come up with new approaches and business models in a range of industries.
So what happened in those 10-15 years?
Some startups have absolutely become inevitable and enacted the tale: Uber, Airbnb, Stripe, Revolut … set new standards in their respective industries and are now household names.
Many other cases are more nuanced:
Slack was obliterated (at least in DAU figures) by Microsoft Teams, although it was an icon of product-led growth. Microsoft demonstrated that a traditional sales-led approach executed through powerful distribution and a complete product ecosystem prevailed. It joined Salesforce as a result to be part of a larger product ecosystem, but still lags behind.
Disney+ showed it was possible for an establish incumbent to rival (again, in DAU metrics) with pure players such as Netflix or Amazon.
And from those nuanced cases, the David v. Goliath myth seems to fade.
Another remake ?
We’re now in 2025 and the landscape is even less decipherable.
It feels like we’ve crossed a threshold. Every company now has an “AI strategy,” just like they had a “mobile strategy” in 2010. OpenAI, Google, and Anthropic are releasing updates monthly, if not weekly. NVIDIA is worth more than the entire German stock market. Meanwhile, early-stage startups are pitching “GPT wrappers” at $30m valuations. This post isn’t about one specific moment, but the accumulation of signals. We’re entering a new equilibrium, and the startup playbook of the 2010s may not apply the same way in 2025.
Magnificent 7 companies (Amazon, Apple, Meta, Microsoft, Alphabet, NVIDIA & Tesla) have leveraged their pool of capital to trust the core layers of the AI revolution: foundational models, data centers and infrastructure.
There is plenty of competition at the model layer, with constant leapfrogging for state-of-the-art capabilities. At the moment we have seen no evidence of a player figuring out continuous self-improvement of their model and take off vs. competitors, thereby dictating price. Quite to the contrary, the model layer is a knife-fight, with price per token decreasing fast for all models, including GPT-4 and likely to be commoditized going forward.
Cloud services are the bread & butter of those hyperscalers. They use this cash to subsidize the AI value chain CapEx with their balance sheet, acting as risk-absorbers within the system, taking on as much demand risk as they possibly can, and driving the supply chain toward greater and greater CapEx escalation (a jargony way to say they invest in infrastructure and are set to absorb computing demand crunches for AI by deploying the capabilities).
The next phase of AI development also emphasizes scaling physical infrastructure, with data centers becoming the critical battleground. Model sizes are set to grow 10x, requiring purpose-built facilities and optimal construction efficiency to stay competitive, driven by hyperscale CapEx from major players like Microsoft, Amazon, and Google.
All in all, the Mag7 are banking on AI and are positioning to reap in the profits to come. “Goliath” in this age is not a sleepy incumbent that has accumulated decades of fat and is clueless about the next technology wave: the hyperscalers are ahead of the curve.
But it’s not just the hyperscalers in the incumbent category: you have to toss in a number of now-mature tech companies trading in public markets (e.g Salesforce, Palantir, Intuit, Oracle, Snowflake, Datadog, etc.) or partially owned by late-stage PE firms (e.g Carta, Anaplan, Contentsquare, Highspot, Mirakl, etc.). Regardless of their individual trajectory and regarded as a group, those companies are no longer new contenders on the market, but neither are they lazy, slow-to-react incumbents. They all have plans to infuse AI in their products and their processes, product and dev teams (helped by AI) to deliver, distribution channels locked in and capital to make the necessary hires or acquisitions.
They’re a new character in our original story, a more advanced version of David but not yet Goliath: the tech incumbents.
And then there are the newly founded companies, the actual startups. Those that go on to raise pre-seed rounds at $20-30m valuation because YC set it as the norm. Those that brand themselves as “AI-native” and spin it as the advantage. Those that tackle often already addressed verticals with a new AI blueprint.
As an investor, I have to ask: what’s their right-to-win in this day and age? How can they beat incumbents with larger teams, larger datasets, established distributions, more funding and likely more talent? And can they ever rival with the hyperscalers or is that a fight never to be picked up?
Florent Quintini at Hexa has suggested a few ways AI-native startups can race ahead in their fight with incumbents: by reinventing workflows, capturing data more efficiently or accessing more data, by not being burdened with features rendered obsolete by AI, by transforming the business model or the distribution.
And he does make good points, serving untapped ends of the market (disruption) with new business models has been part of the startup strategic playbook for years.
One could also argue startups still have a few aces: velocity, focus, and permissionless thinking. They can chase edge cases incumbents ignore, break UX conventions, or test pricing models that feel “off-brand” for bigger players. They don’t have to maintain legacy features or appease large customers. That’s not nothing. But in a world where everyone can ship features fast thanks to GenAI and open infra, velocity may no longer be a moat - just a baseline.
And the question becomes, coming from a culture of product development that’s iterative and swift, how fast will incumbents adapt? How easy will it be to capture market share against competitors who not only have a headstart in distribution but also can deploy similar features rather easily?
If data and distribution are already claimed, what’s left for startups to defend? A few hypotheses, partly borrowed to Florent’s article:
Emotional resonance: Crafting products people love, not just use.
Workflow depth: Redesigning the entire workflow, not just bolting AI onto existing UI.
Synthetic data generation: Building proprietary data loops through usage.
Embedded distribution: Becoming part of the stack (API-first, infra-level hooks, product-led growth). Think Cursor
Regulatory arbitrage: Moving faster in sectors where incumbents are hamstrung
These aren’t silver bullets, but they might be slingshots in the new David vs. Goliath setup. And I’m sure the final result will be nuanced once we post-rationalize it, but it will sure be fascinating to be on the battlegrounds with the founders building for a win in their markets!
Drop me a line if you’re a risk-taker running against established competition with a vertical AI startup - younes@oprtrs.club :)
And truly sorry for this post’s lame name – as promised last week, I’ll send these things raw.
May I suggest some Ol’ Dirty Bastard wisdom to get in the mood: ‘OOh baby I like it raw, yeah baby I like it raw’