I want to pull up a chair and walk you through something I’ve been thinking about a lot: the AI boom that everyone talks about is real, but I believe it’s about to hit a wall most people aren’t ready for. As someone who’s spent years building deep learning systems and running an ML company, I’ve seen hype cycles come and go. Lately the signs point to something different — not the end of AI, but a heavy course correction. In plain terms, I think AI valuation corrections are coming, and here’s why.
The scaling law problem
For a long time the playbook was straightforward: add compute, add data, and models get better. That faith in scaling laws powered enormous investments and skyrocketing valuations. But now, multiple signals suggest diminishing returns. Big model releases that were supposed to be leapfrogs have been, at best, incremental. Researchers at top conferences have even suggested pretraining paradigms are reaching practical limits.
“Pretraining as we know it will end.” — paraphrase of Ilya Sutskever’s remarks at NeurIPS
That doesn’t mean models won’t improve, but it suggests the path forward isn’t just throwing more FLOPs and parameters at the problem. Fundamental research into architectures, learning algorithms, and data efficiency will be needed — and that kind of research is slow and uncertain.
The economic death spiral
Let’s talk money. Even the biggest players face a brutal economic reality: running and improving today’s models is outrageously expensive. Consider a simplified breakdown of operational costs you’ve probably read about in industry reporting:
- Billions spent on inference to keep chat services live
- Billions on training cycles for new models
- Huge personnel and infrastructure overhead
When a company is burning billions annually while the revenue side grows more slowly, it becomes risk-averse. That’s not a moral failing — it’s math. You can’t afford to fund decade-long, uncertain research when your balance sheet requires short-term revenue to stay alive. The result: the incumbents are forced to focus on maintenance and incremental gains, not the risky breakthroughs that might change everything.
Commoditization and DeepSeek
At the same time, a different kind of disruption is happening: commoditization. Competitors, particularly some well-funded and scrappy teams overseas, are showing that you can achieve competitive benchmarks for a tiny fraction of the cost. The headline-grabbing example is a Chinese model family that matched many benchmark results while being orders of magnitude cheaper to develop and operate.
The effects are straightforward and profound:
- Lower API prices, undercutting cloud-based pricing power
- Distilled and quantized models that run on consumer hardware
- Open weights that let organizations self-host and customize without vendor lock-in
That combination erodes the economic moat of incumbents. If organizations can run high-quality models locally or use extremely cheap APIs, the revenue calculus for large cloud-based model providers changes dramatically.
Signals that point to AI valuation corrections
There are measurable signals I watch closely that feed my thesis. These are things I see both in public reporting and inside my own company:
- Stalled performance gains from the latest model generations compared to the previous leaps
- New entrants delivering comparable performance with tiny budgets and much lower latency
- Enterprises shifting spend from external APIs to internal deployments
- Research comments from influential lab leaders suggesting a need for new paradigms
Put them together and you get a picture where expectations for perpetual exponential improvement are unrealistic. That mismatch — between expectation baked into valuations and the realistic pace of progress — is a classic precursor to a market correction.
The enterprise exodus
I’m living this shift as customers ask a different question: why pay persistent API fees when we can host models internally for a fraction of the cost? The math is compelling. For mid-to-large organizations, the break-even for moving workloads on-prem can be months, not years. That changes procurement strategy.
It’s also changing product priorities. Instead of chasing the absolute bleeding edge for general-purpose models, many companies are asking for fit-for-purpose models that are cheaper, faster, and more private. That’s fertile ground for specialization and niche models that solve specific problems extremely well.
When AI valuation corrections meet commoditization
When commoditization and enterprise migration converge, the impact on valuations is multiplicative. Imagine a world where:
- API revenue growth stalls because customers self-host
- R&D investment dries up because operational costs consume capital
- Smaller, specialized vendors pick off profitable niches
That scenario doesn’t require any single catastrophic event. It’s a gradual repricing as the market internalizes new economics. Companies that were valued on exponential performance improvements will be reassessed under more conservative growth assumptions.
The innovation trap
Here’s an irony: the organizations with the most resources are often the least likely to pivot into high-risk, high-reward fundamental research. Why? Because their existing operational obligations — keeping services live, supporting millions of users — generate the liabilities that force conservative decisions. Smaller labs and more nimble research groups can take intellectual risks that might produce the next architectural breakthrough. That’s how disruptive advances usually happen.
So the crown jewels of future AI may very well come from outside today’s market leaders. If so, valuations tied purely to current architecture scaling will look overheated.
Where I think we go from here
To sum up my perspective as someone in the room where this stuff is built: the boom isn’t ending. AI remains transformative. But the commercial and technical landscape is shifting in ways that favor specialization, efficiency, and new architecture research over blind scaling. I expect a period of re-pricing in public and private markets, driven by more realistic expectations about performance trajectories and by the commoditization of inference and model access. In short, expect AI valuation corrections to filter through the market as economic realities meet technical ceilings.
If you’re a founder or operator, my advice is simple: focus on defensible value, not on being the biggest model. If you’re an investor, ask whether growth assumptions rely on indefinite scaling. And if you’re just watching as a curious person, buckle up for an era where utility and specialization matter more than headline parameter counts.
Q&A
Q: Are we running out of ways to make models better?
A: Not running out, but the low-hanging fruit from scale is diminishing. Future gains will likely come from smarter architectures, better data curation, and algorithmic breakthroughs that take longer to discover and validate.
Q: Will on-prem AI really replace cloud providers?
A: Not entirely. Cloud providers will still host many workloads, especially those needing huge scale or integration. But expect a meaningful shift where cost-sensitive and privacy-conscious workloads migrate on-prem or to hybrid models.