Subsidized Ferraris
The AI industry is handing out the keys to the most powerful machines money can build — and hiding the bill. That can't last.
This week at Microsoft Build 2026, Satya Nadella made a point that deserves more attention than it got. Microsoft unveiled its MAI family of in-house models and claimed — with some credibility, though the benchmarks deserve scrutiny — 10x better cost efficiency than the frontier models it has been paying OpenAI and Anthropic to serve through Azure. The message was blunt: the era of reflexively reaching for the most powerful model on the market needs to end.
Of course, Microsoft is playing its own strategic game of catch-up here - they’re not entirely altruistic in making this argument. But that doesn’t make them wrong.
Everyone’s Driving a Ferrari to the Grocery Store
Let me use an analogy that I think captures what’s actually happening in AI right now.
The financial markets are subsidizing Ferraris — and giving everyone the ability to drive one. There’s nothing wrong with Ferraris - I’d love to have one. They look spectacular, they’re undeniably thrilling to drive, and they impress your neighbors. But are they really what you need to run errands? To pick up your kids from school? Is burning that much horsepower, that much fuel, that much money the right call for every single trip you take?
Of course not.
The same question applies to AI models — and right now, most people aren’t asking it.
The AI industry is spending enormous time and energy on every lab’s latest frontier release. The hype around Anthropic’s Claude Mythos — a model that, as of this writing, isn’t yet publicly available — is a perfect illustration of this dynamic. Frontier models are absolutely remarkable and incredibly powerful. But they are also breathtakingly expensive, and a growing share of those costs are being obscured by investors who are still funding market share over margins, and by model providers who are actively incentivizing adoption over efficiency.
That cannot last forever. And when it stops, the reckoning will be uncomfortable for those who never developed the habit of asking: is this the right model for this task?
The Hidden Tab
Here’s what the current moment looks like beneath the surface.
Yes, per-token costs are falling — that’s real. But the unit economics argument misses the bigger picture. Frontier reasoning models consume orders of magnitude more tokens per task than their lighter counterparts. As agentic workflows scale, and as organizations pile on more complex, multi-step AI processes, volume outpaces the per-token price drop. The bill goes up even as each line item looks cheaper. It’s the classic “we’ll make it up in volume” fallacy playing out in real-time.
The anecdotes are starting to pile up. It’s been well-publicized that Uber’s CTO recently disclosed that the company burned through its entire 2026 AI coding tools budget in four months — after internal leaderboards aggressively incentivized adoption, without anyone tracking what, exactly, they were optimizing for. That’s not a story about AI failing. It’s a story about an organization that belatedly developed the discipline to ask whether the tool it was reaching for was the right one for the job. Other organizations such as ServiceNow, Walmart and even Microsoft and Amazon themselves have been dialing back their encouragement to their teams to max out AI usage.
These are early warning signals. Most organizations — and most individuals — are not yet reading them.
Christensen Had a Word for This
Those of you familiar with disruption theory will recognize the dynamic immediately.
Christensen called this dynamic overshooting — the moment when a technology’s raw capabilities outpace what the mainstream market actually requires. When that happens, chasing the absolute frontier yields diminishing returns for users, while quietly compounding the costs. Overshooting creates the classic conditions for disruption from below: a “good enough” alternative, priced for accessibility, eats the market from the bottom up while the incumbent keeps racing toward a frontier most customers don’t require.
We are watching this happen in real time in AI. Frontier models are extraordinary — genuinely so. But for many, arguably most, business tasks, they are already well past “good enough.” The question isn’t whether they’re impressive. The question is whether the incremental capability justifies the incremental cost.
For most tasks, it doesn’t.
Seeing This From the Inside
I run a business. I use AI constantly — it’s foundational to how I work, not a novelty or a tool I reach for occasionally. When you get the bills - even the subsidized ones - it becomes crystal clear that defaulting to the most powerful model for every task is inherently wasteful.
Drafting a routine email? A smaller, faster, cheaper model handles it better than you’d expect — and returns the result in a fraction of the time. Summarizing a document? Same story. Doing deep comparative research across a complex strategic landscape? Now you need the horsepower. That’s where frontier reasoning earns its expense.
The discipline of matching model to task — what I think of as right-sizing your AI — is something I now treat as a core operational practice. It saves money. It saves time. And frankly, it often produces better results, because a well-scoped smaller model frequently outperforms a frontier model given a vague, underpowered prompt.
But most people haven’t developed this habit. “Tokenmaxxing” is dumb - but it’s still the mindset in too many places. The default setting — especially when someone else appears to be picking up the tab — is too often the most powerful option available.
The Reckoning Coming
The subsidies won’t last forever - investor patience for labs burning capital to subsidize market share is not infinite. Granted, with the massive SpaceX, Anthropic, and OpenAI IPOs looming on the horizon, the hype machine will likely drown out talk of capital efficiency for a while longer. But the music will eventually stop.
When pricing starts to finally reflect real costs, organizations that have built workflows around the assumption that frontier performance comes at commodity prices are going to face a hard reset.
The smart move, for individuals and for organizations, is to get ahead of this now. Not by abandoning frontier models — they have a real and important role — but by developing genuine fluency in the question that the industry currently discourages you from asking:
Is this the right model for this task?
Microsoft is asking it, even if their motives are mixed. CIOs and CFOs are starting to ask it loudly. The Uber story won’t be the last one.
You should be asking it too.