British AI companies have just had their best six months on record, with UK AI funding reaching $12.6bn in the first half of 2026, more than four times the same period a year earlier and close to three-quarters of all venture capital invested in the country. Total UK venture funding reached $17bn, the strongest opening to a year since 2022, and the UK took 39% of all European venture capital.
The capital is being deployed into businesses whose unit economics look nothing like the software businesses that trained a generation of investors. The gap is not small; it is not temporary, and most of it is compute.
According to the report, the number that broke the model is the average AI product gross margins, which is around 52% in 2026, up from 41% in 2024 and 45% in 2025. However, this is still 25 to 30 points below the 75% to 85% that traditional software has delivered for two decades.
The distribution matters more than the average. Bessemer Venture Partners’ State of AI work found the fastest-scaling companies, those reaching $100m in annual recurring revenue in around eighteen months, were running at roughly 25% gross margins, effectively buying distribution with compute.
Then there is the finding that should worry anyone underwriting these businesses on a software template. In ICONIQ’s data, model inference rises from 20% to 23% of total spend as products mature, while talent costs fall from 32% to 26%. The compute line grows as a share of spend as the company scales, rather than shrinking.
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Software economics rested on the opposite assumption. Cost of goods sold was largely fixed, so each additional customer arrived at close to zero marginal cost and margins expanded with volume. In an AI product, the dominant cost is incurred per query. It scales with usage, sometimes faster than usage, and it does not amortise.
This flows straight through to how these companies are valued. The Rule of 40, which asks that growth plus profitability clears 40, quietly assumed COGS was mostly fixed. Strip 25 points out of gross margin and growth has to carry a far heavier share of the load to clear the same bar.
For most companies most of the time, the answer is still to call an API. If your volumes are modest, your usage is spiky, or you are still changing the product every fortnight, paying a provider for elasticity is straightforwardly correct. You are buying optionality, and optionality is worth paying for while you still need it.
They are running their own models, on their own GPUs, at sustained and predictable utilisation. That is a real threshold and a growing number of British AI companies are crossing it, particularly those training or fine-tuning their own models rather than wrapping someone else’s.
One company has published its numbers in enough detail to be useful, and it is worth examining precisely because it is not an AI business. 37signals, which makes Basecamp and HEY, spent $3,201,564 on cloud services in 2022 and decided to leave. The company bought around $700,000 of Dell hardware to replace its cloud instances, and that this outlay was entirely recouped during 2023 as contract commitments expired.
There is a wrinkle specific to companies operating in the UK, and it is one that does not appear in any American analysis of this question. If the maths does point towards running your own hardware, the capacity to put it in is rationed. Britain’s demand-connection queue for the transmission network reached 125GW against a national peak demand of around 45GW.
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The practical implication for a board is that this decision cannot be left until the compute bill becomes painful. Securing capacity with colocation providers that already hold power and can support high-density, liquid-cooled hardware takes planning measured in quarters. A company that runs the numbers, concludes it should move, and then starts looking is beginning a process rather than completing one.
Two things cut against this analysis. The first is that inference costs are collapsing. Equivalent model performance has become dramatically cheaper year on year, and every month that continues weakens the case for buying hardware to escape an API bill that may be substantially smaller by the time the kit is racked.
The second is that most AI companies should not be thinking about this. If you are pre-product-market-fit, iterating weekly, or running variable workloads, the flexibility premium is money well spent and infrastructure ownership is a distraction from the thing that actually kills startups, which is not building something people want, such as finding difficult challenges that drive engagement.
What has changed is narrower than the loudest version of the argument, and more durable. Compute is now a permanent, growing, structural line in an AI company’s cost of revenue rather than a temporary inefficiency to be optimised later. That makes it a board-level question about capital rather than a procurement detail.
It is a board-level question.
