Innovation doesn’t need a faster engine

The doomsayers of AI are having their moment. They correctly point out that the rapid progress of large language models has slowed. Context windows remain limited, hallucinations persist, and bigger models no longer guarantee smarter ones. From this, they conclude that the age of AI breakthroughs is ending.

They are mistaking the engine for the journey.

History offers many parallels. When the internal combustion engine stopped getting dramatically better, innovation didn’t stop. That was when it really started. The real transformation came from everything built around it: road networks, trucking logistics, suburbs, the global supply chain. Likewise, the shipping container changed the world not through further improvements, but because it became the standard that reshaped ports, labor systems, and trade. When the core technology stabilizes, people finally start reimagining what to do with it.

This is the point we’ve reached with AI. The models are powerful, but most of their potential remains untouched. Businesses are still treating AI as a novelty, something to sprinkle on top of existing processes. Education systems, government workflows, healthcare administration; these are built as if nothing new has happened. We haven’t even begun to redesign for a world where everyone has a competent digital assistant.

The real question is not whether an AI can pass a medical exam. It’s how we organize diagnosis and care when every doctor has instant access to thousands of case studies. It’s not about whether an AI can draft an email. It’s about how office communication changes when routine writing takes seconds. The innovation now lies in application, not invention.

Limits are not the enemy. In fact, recognizing limits often helps creativity flourish. When designers accept that screen size on phones is fixed, they find smarter interfaces. We become inventive when the boundaries are clear. The same will happen with AI once we stop waiting for miracle upgrades and start asking better questions.

The real bottleneck is attention. Investment still flows heavily into training larger and larger models, chasing diminishing returns. Meanwhile, the tools that would actually change how people work or learn get far less support. It’s as if we are building faster trains while neglecting the tracks, stations, and maps.

There is a similar problem in education, where energy goes into protecting the structure of institutions while ignoring how learning could be improved. Just because we can do something well does not mean it is worth doing. And just because AI researchers can build a bigger model does not mean they should.

The most meaningful innovation is ready to happen. It is no longer about raw power, but about redesign. Once we shift our focus from models to uses, the next revolution begins.

The Start-up Advantage and the Plain Bot Paradox

In the gold rush to AI, start-ups seem, at first glance, to have the upper hand. They are unburdened by legacy infrastructure, free from the gravitational pull of yesterday’s systems, and unshackled by customer expectations formed in a pre-AI era. They can begin with a blank canvas and sketch directly in silicon, building products that assume AI not as an add-on, but as the core substrate. These AI-native approaches are unencumbered by the need to retrofit or translate—start-ups speak the native dialect of today’s machine learning systems, while incumbents struggle with costly accents.

In contrast, larger, established companies suffer from what could be called "retrofitting fatigue." Their products, honed over decades, rest on architectures that predate the transformer model. Introducing AI into such ecosystems isn’t like adding a module; it’s more akin to attempting a heart transplant on a marathon runner mid-race. Not only must the product work post-op, it must continue to serve a massive, often demanding, user base—an asset that is both their moat and their constraint.

Yet even as start-ups celebrate their greenfield momentum, they stumble into what we might call the plain bot paradox. No matter how clever the product, if the end-user can get equivalent value from a general-purpose AI like ChatGPT, what exactly is the start-up offering? The open secret in AI product development is this: it is easier than ever to build a “custom” bot that mimics almost any vertical-specific product. The problem is not technical feasibility. It’s differentiation.

A travel-planning bot? A productivity coach? A recruiter-screening assistant? All of these are delightful until a user realizes they can recreate something just as functional using a combination of ChatGPT and a few well-worded prompts. Or worse, that OpenAI or Anthropic might quietly roll out a built-in feature next week that wipes out an entire startup category—just as the “Learn with ChatGPT” feature recently did to a slew of bespoke AI tutoring tools. This isn’t disruption. It’s preemption.

The real kicker is that start-ups not only compete with each other but also with the very platforms they’re building on. This is like opening a coffee stand on a street where Starbucks has a legal right to install a kiosk next to you at any moment—and they already own the espresso machine.

So if start-ups risk commodification and incumbents risk inertia, is anyone safe? Some large companies attempt a third route: the internal start-up. Known in management lore as a “skunk works” team—originally a term coined at Lockheed to describe a renegade engineering group—these are designed to operate with the nimbleness of a start-up but the resources of a conglomerate. But even these in-house rebels face the plain bot paradox. They too must justify why their innovation can’t be replicated by a general AI and a plug-in. A sandboxed innovation team is still building castles on the same sand.

Which brings us to a more realistic and arguably wiser path forward for incumbents: don’t chase AI gimmicks, and certainly don’t just layer AI onto old products and call it transformation. (Microsoft, bless its heart, seems to be taking this route—slathering Copilot across its suite like a condiment, hoping it will make stale workflows taste fresh again.) Instead, the challenge is to imagine and invest in products that are both fundamentally new and fundamentally anchored in the company’s core assets—distribution, brand trust, proprietary data, deep domain expertise—things no plain bot can copy overnight.

For example, a bank doesn’t need to build yet another AI budgeting assistant. It needs to ask what role it can play in a world where money advice is free and instant. Perhaps the future product isn’t a dashboard, but a financial operating system deeply integrated with the bank’s own infrastructure—automated, secure, regulated, and impossible for a start-up to replicate without decades of licensing and customer trust.

In other words, companies must bet not on AI as a bolt-on feature, but on rethinking the problems they’re uniquely positioned to solve in an AI-saturated world. This might mean fewer moonshots and more thoughtful recalibrations. It might mean killing legacy products before customers are ready, or inventing new categories that make sense only if AI is taken for granted.

The trick, perhaps, is to act like a start-up but think like an incumbent. And for start-ups? To act like an incumbent long before they become one. Because in a world of rapidly generalizing intelligence, the question is not what can be built, but what can endure.