Let’s be honest about what’s happening.
When every startup suddenly becomes “AI-native,” when every
product roadmap leads with a model upgrade instead of a customer problem, and
when questioning the economics gets you labeled as “behind the curve,” you’re
no longer watching innovation unfold. You’re watching a bubble inflate.
AI is not fake. That’s precisely why this is dangerous.
Bubbles don’t form around empty ideas, they form around
powerful technologies that people overextend. Right now, AI is being treated
less like a tool and more like a substitute for strategy. The assumption is
implicit but widespread: if you add AI, value will follow. If you move fast
enough, fundamentals can wait.
They can’t.
Across the industry, we’re seeing products that demo
beautifully and struggle quietly. Models hallucinate in ways that are
unacceptable in real-world systems. Costs scale in non-intuitive ways,
especially at volume. Reliability drops precisely where trust matters most. Yet
companies ship anyway, because in a hype cycle, momentum matters more than
correctness. When something breaks, it’s waved away as “early days.” When
margins collapse, it’s framed as “temporary infrastructure spend.”
This isn’t optimism. It’s avoidance.
Look at customer support automation, one of the most
aggressively pushed AI use cases. On paper, it’s perfect: repetitive queries,
large datasets, clear cost-cutting incentives. In reality, many companies
rushed to replace human agents with chatbots before the technology, or the
organization, was ready. The result wasn’t efficiency. It was customer
frustration, escalation loops, brand damage, and churn. Eventually, humans were
reintroduced, often under worse conditions and tighter margins. AI didn’t fail
here. The belief that intelligence can be separated from accountability did.
The same pattern is playing out elsewhere. Startups are
being built on top of foundation models they don’t control, offering features
that competitors can replicate in weeks. Their “moat” is access, until it
isn’t. Their differentiation is speed, until everyone has the same tools. In
many cases, there’s no proprietary data, no workflow lock-in, no reason the
product should exist once the novelty wears off.
What’s propping this up is capital and narrative. Investors
don’t want to miss the next platform shift, so they fund optionality instead of
durability. Founders optimize for visibility over viability. “AI-first” becomes
an identity rather than an architectural choice. And as long as the story
holds, the numbers can be explained away.
But stories have expiration dates.
When the correction comes, and it will, it won’t look like a
dramatic collapse. It will look like quieter rounds, down valuations, stalled
pilots, and customers asking harder questions. The companies that survive won’t
be the ones with the best demos or the largest models. They’ll be the ones that
treated AI as infrastructure, not a personality trait.
The uncomfortable truth is that a large portion of today’s
AI landscape isn’t building companies, it’s burning time. Many teams don’t have
a path to defensibility, only a race against commoditization. Many products
don’t solve new problems; they repackage old ones with a probabilistic engine
and hope users won’t notice the tradeoffs.
AI will absolutely change how software is built, how work is
done, and how value is created. But this phase, the phase where AI is used to
justify everything and explain nothing, is not the future. It’s the froth.
And froth always clears.
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