Imagine a food court with forty stalls, and then imagine that thirty-three of them start selling the same burrito. Not similar burritos. The same one, with slightly different salsa branding and a different font on the sign. That’s roughly what US venture capital looks like right now, and we have a number for it.
Through the first three quarters of 2026, AI companies captured 82.7% of all US venture deal value, according to the Q3 2026 PitchBook-NVCA Venture Monitor. Total US deal value hit $515.8 billion through September, already about 44% above the full-year record set in 2021. Globally, AI startups reportedly pulled in $102 billion in Q3 alone, or 64% of worldwide venture capital. Back in Q1, the figure was roughly $242 billion, about 80% of global funding for the quarter.
I review AI tools for a living. I should be thrilled. Instead I’m bracing.
What that concentration actually does to a review queue
When money floods one category, the category doesn’t get better at the same rate the money arrives. It gets bigger. My inbox is the clearest evidence I have. The volume of new tools asking for coverage has gone somewhere strange, and the distribution of quality has gotten lopsided in a specific way: a widening middle of competent-but-interchangeable products, a thin top of genuinely useful ones, and a long tail of wrappers that would be a weekend project if you removed the pitch deck.
That middle is the problem for anyone trying to pick tools. Interchangeable products are hard to evaluate because they’re all fine. They demo well. They have clean onboarding, a pricing page with three tiers, and a changelog that updates on schedule. None of that tells you whether the thing will still exist in eighteen months, or whether the pricing you signed up for reflects the actual cost of serving you.
The funding math shows up in your invoice
Here’s the part I think gets skipped in the funding coverage. A category absorbing four out of every five venture dollars is a category where the pricing you see is not necessarily the pricing the product needs. Generous free tiers, unlimited-ish usage, pricing that undercuts the incumbent by half: those are growth decisions funded by a balance sheet, not margin decisions funded by customers.
That’s not a scandal. It’s how land-grab markets work, and users get real value out of the subsidy while it lasts. But it changes how you should read a pricing page. When I test tools now, I ask a few questions that have nothing to do with features:
- Does the free tier make sense as a business, or only as customer acquisition?
- If their inference costs doubled tomorrow, which part of my workflow breaks?
- Is my data portable out of this thing, in a format I could actually use somewhere else?
- Does the product do one job well, or does it do nine jobs because the roadmap had to look big for a raise?
That last one correlates with funding cycles more than anything else I track. Tools that were sharp and focused at launch tend to sprawl after a big round. More surface area, more settings, more half-finished modules. The thing I liked is still in there, buried under features built to justify a valuation.
Betting on the category is not the same as betting on the tool
The 82.7% figure is a statement about a category, and categories are easier to be right about than individual products. You can be completely correct that AI tooling matters and still end up with a stack full of dead logins. Deal value above the 2021 record, by a wide margin, tells you a lot of money entered. It doesn’t tell you which of these companies found customers who would pay full price without a discount, and that’s the only question that matters when you’re the one depending on the software.
So my practical read, as the guy who has to actually use these things: stay promiscuous. Avoid deep integration with anything you’ve used for less than six months. Prefer tools that export cleanly. Treat an enormous round as neutral news rather than a safety signal, because a large raise is a bet on the future, not evidence of a working present. And when two tools are equally good, pick the one that would survive on its current revenue, if you can figure out which one that is.
The upside of this moment is genuine. More capital in AI tooling means more experiments, faster iteration, and better defaults in products that would have been rough a year ago. I’ve reviewed tools in 2026 that were simply not possible in 2023. The flood lifted the floor.
It also means the next couple of years will involve a lot of migration work for people who committed too hard, too early, to things that looked permanent because they were well-funded. Those are different qualities. Keep them separate in your head, and the 82.7% becomes a reason to test more and commit less, rather than a reason to assume the winners have already been picked.
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