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Venture investors will make revenue per employee a leading metric for technology fundraising in 2027, according to FinFlare. Chief Executive Officer Angela Brown said the shift reflects tighter scrutiny of capital efficiency and AI spending.
Investors are moving away from headcount growth as a marker of progress, particularly for early-stage companies seeking backing from larger funds, Brown said. Founders now face a more disciplined market, with Series A rounds likely to come under greater valuation pressure.
The comments suggest a shift in what many investors want from startups after several years in which rapid hiring often signalled ambition and scale. Brown said that pattern began to reverse after the 2022 downturn, and that wider use of AI has increased pressure on founders to show stronger output from smaller teams.
"Founders raising next year will be asked a different question by potential investors: not 'how big is your team?' but 'how much revenue does each person in it bring in?' For years, hiring ahead of growth was what investors expected to see. That started to change in the 2022 downturn, and AI is speeding it up. In the rounds I'm seeing, investors - including the bigger London funds - are paying close attention to lean teams that use AI well and can show it in the numbers. Scottish founders have a lot going for them, but they can't afford to fall behind when competing alongside companies from London and beyond," Brown said.
She also expects fewer deals from pre-seed to Series A in 2027, even as average round sizes increase. In Brown's view, that combination is likely to bring tougher terms for founders, including preference structures, milestone-based tranches and bridge extensions.
That would add another layer of difficulty for startups trying to raise in an increasingly selective market. Brown said current tax relief limits under SEIS, EIS and VCT remain unchanged, while recent swings in US technology stocks are unlikely to shut off early-stage money immediately.
Funding focus
Another pressure point is the narrowing pool of grant and venture funding for companies outside a handful of sectors, Brown said. Capital is becoming more concentrated in areas seen as strategic priorities, including cybersecurity, defence, deep tech, clean tech and advanced manufacturing.
That leaves many software founders facing a different route to growth. Businesses outside those segments may need to rely more heavily on customer revenue, rather than expecting grant support or repeated equity rounds to sustain expansion.
"Grant funding has typically been a vital non-dilutive R&D safety net that investors previously used to de-risk early bets. I am seeing more in the way of available grants and follow-on VC cheques being increasingly consolidated around sovereign and strategic priority areas - specifically cyber security, defence, deep tech, clean tech, and advanced manufacturing. Next year, founders outside these verticals will need to move away from external capital dependence and grow on customer revenue," Brown said.
AI premium
The spread of AI tools across organisations has also reduced the barriers to building software products, Brown said. As a result, she expects investors to take a harder look at the valuation premium some startups have attracted by presenting themselves as AI businesses.
Her argument is that software code alone is becoming less defensible as AI shortens development times and lowers build costs. Instead, investors are likely to place greater weight on proprietary data, workflow integration and unit economics when deciding whether a business has a durable edge.
That is relevant beyond software-only businesses, she said, although regulated sectors such as medtech, biotech and financial services still retain barriers linked to intellectual property and compliance. Even there, investors will want the same level of financial discipline, Brown said.
"For pure software businesses, code isn't the moat it used to be. AI has made software cheaper and faster to build, which also makes it cheaper and faster to copy. What's harder to copy is the proprietary data only your business has, deep workflow integration, and unit economics that stand up to scrutiny. For our clients in medtech, biotech, and financial services, regulation and IP still matter a great deal - but investors will demand the exact same discipline in the numbers," Brown said.
Finance scrutiny
Brown also said investors will dig more deeply into whether AI tools are producing measurable returns inside finance functions. She warned that automated systems may help with routine work, but will not remove the need for finance leaders to explain and defend numbers during due diligence.
That matters for scaling companies preparing to raise capital because financial reporting is often tested in detail once investors move beyond headline growth figures. Brown said founders should be careful not to remove too much human oversight from finance processes before entering fundraising discussions.
"AI can take a lot of the grind out of finance, and it should. But it works on probability, and 'probably correct' won't get you through due diligence. I've sat on the finance side of investor due diligence many times. The first question is never about your AI stack - it's 'why does this number say that?' If the answer is 'the tool produced it', you've lost the room. That's why your finance function needs to be human led: AI doing the groundwork, and a qualified person owning the answer," Brown said.