Is the AI boom distorting the investment market?

For the past two years, there’s been a relatively simple story about the funding market. AI is booming, and capital and innovation into the industry is accelerating. Depending on who you ask, that’s a necessary correction after a more challenging funding environment.

That broader sense of inevitability is increasingly reflected across the market. In Barclays’ recent AI:100 report, Global Chairman of Investment Banking Mark Zanoli described AI not as “a trend”, but as “a structural shift in how economies create value” — a framing that helps explain why investors are deploying capital with such speed and conviction in this sector.

But that version of events is a little too neat. It assumes that capital is expanding to meet this new opportunity, and that increased investment into AI simply reflects increased value being created. What it doesn’t fully account for is what might be happening elsewhere. When one area of the market attracts this level of attention and capital, the more revealing question is not just how much it is gaining, but what it may be changing.

Is the AI boom simply a reflection of opportunity, or is it reshaping how capital is allocated across the wider market? To answer that, we looked at how funding is being distributed across sectors, stages, and deal sizes, and how that balance has shifted over time.

The AI landscape at a glance

Investment into UK-based AI companies reached an all-time high in 2025, with £8.32b invested across 1,270 funding rounds. This was a huge 73% increase in the amount invested from 2024. And we’re on track for another record-breaking year in 2026, with £5.23b invested into AI companies so far this year. If that pace continues, 2026 could see total AI investment reach around £15–16b, almost doubling 2025’s record.

To put that in perspective, there was a total of £30b invested into all UK companies in 2025, with AI investment accounting for 28% of that total. That proportion is huge, particularly when you look at the scale of investment into other industries.

Not just concentration, but compression

It would be easy to describe this as a familiar cycle of sector concentration. Capital has always moved in waves, flowing into areas of perceived opportunity before shifting again. However, the dynamics around AI appear different, not just in scale but in speed and intensity.

One of the clearest indicators of this shift is deal size and timing. AI companies are not only attracting more funding; they are raising larger rounds earlier in their lifecycle. For example, the average deal size for Seed stage AI companies is £978k, 46% higher than the average across all Seed-stage companies. And the average Venture-stage deal size for AI companies is £2.60m, 64% higher than the average deal size for all Venture-stage companies.

Capital that might previously have been deployed over multiple funding rounds, across a longer timespan, is now being committed earlier, often at higher valuations and with greater conviction.

Alejandro Giacometti, Head of Machine Learning at Beauhurst, says: “Part of what makes the current AI cycle feel different is the sheer amount of capital required to compete at the frontier. Training models, accessing compute, and scaling infrastructure all demand huge investment upfront, which naturally concentrates funding around a smaller number of companies with the resources to move quickly.”

This acceleration has consequences. Capital is finite, and when larger amounts are deployed into fewer companies, it reduces the flexibility investors have to allocate funding elsewhere. In practice, this creates a form of compression across the rest of the market. Fewer deals are completed, fundraising timelines extend, and the threshold for investment rises.

Importantly, this does not necessarily reflect a decline in the quality of companies outside AI. Instead, it reflects a shift in how capital is being prioritised and deployed.

Is funding being stripped away from other industries?

Looking beyond AI, the picture becomes more uneven, and more revealing. According to Beauhurst’s The Deal 2026, despite a tougher funding environment overall, a small group of just 15 industries stood out in 2025, outperforming their three-year averages on both deal volume and average deal value. These include AI, robotics, cloud computing, and tech consulting — sectors closely aligned with digital infrastructure and enabling technologies.

Alongside this relatively small group of outperforming industries, a more significant 29 industries experienced a slowdown in both deal volume and investment value, reflecting a broader cooling in funding activity across much of the market.

Within that group, life sciences stands out in particular, where industries including pharmaceuticals, clinical diagnostics, medical devices and reagents all saw sustained declines in investment activity, with fewer deals completed and lower average deal values compared to recent years.

What makes this divergence important is more than just some sectors growing while others are contracting. The strength of the market is increasingly concentrated in a narrow set of categories, many of which are directly or indirectly linked to AI and adjacent technologies. Outside of that cluster, the picture is materially weaker, with fewer deals, lower values, and sustained declines across a much wider set of sectors.

The latest edition of The Deal suggests the concentration of investment into AI is becoming even more extreme. Justin Tsui, author of The Deal and Beauhurst Insights Associate says: “The implications go beyond sector performance. When capital begins to concentrate so heavily on a small number of themes, the impact is not limited to where money is going — it starts to shape what the market pays attention to in the first place. And once attention becomes concentrated, capital rarely follows evenly.”

A shift in investor behaviour

To understand this more fully, it is necessary to look beyond industries and consider investor behaviour. Two distinct funding environments are increasingly emerging across the market.

In one, capital is deployed quickly, with high conviction and a greater tolerance for uncertainty. In the other, capital moves more cautiously, with increased scrutiny around revenue traction, operational efficiency, and profitability pathways. This divergence is not always visible in aggregate figures, but it has a significant impact on how companies experience the fundraising process.

The growing willingness to commit significant capital earlier in a company’s development, often before traditional indicators of maturity are visible, is partly driven by competitive dynamics within AI. Rapid technological change and the scale of potential upside mean delaying investment carries its own risks.

At the same time, AI is increasingly being treated as a foundational capability with applications across a huge range of industries. This framing supports larger and earlier investments, often with less emphasis on near-term financial performance.

The Bank of England has noted that AI adoption is now being actively assessed through a financial stability lens, including how rapidly it could reshape risk transmission and behaviour across the financial system. Regulators are no longer treating AI purely as a technological development; they are treating it as a potential driver of systemic change in market functioning.

The challenge is that this logic is not being applied consistently across the wider market. Companies operating outside of AI are still typically assessed against more traditional criteria, creating a widening gap in how investors evaluate risk, growth, and long-term value across sectors.

The role of attention

Alongside capital, attention also plays a critical role. Media coverage, investor focus, and broader ecosystem attention are increasingly concentrated on AI-related developments. This shapes perception as much as it shapes funding decisions.

When a single theme dominates, it becomes easier for other opportunities to be overlooked, not because they lack potential, but because they fall outside the prevailing narrative. Historically, some of the most attractive investment opportunities have emerged in precisely these conditions, when capital and attention are concentrated elsewhere.

For example, during the late 1990s, capital and attention became overwhelmingly concentrated around internet companies. Investors rushed to back anything associated with the web, often with limited due diligence and inflated expectations — driven in large part by fear of missing out.

At the peak, this concentration of capital created the impression of a thriving, unstoppable market. But much of that activity was narrowly focused. When the bubble burst, venture capital investment fell dramatically and the industry contracted significantly in the years that followed.

What’s more interesting, though, is what happened next. In the years after the crash, when attention and capital had pulled back, a new generation of companies emerged. Businesses like Facebook, LinkedIn, and Twitter were founded or scaled in the early 2000s, benefiting from a less crowded funding environment and a shift toward more disciplined investment.

In that sense, the concentration of attention is shaping where future opportunities are missed.

Is this what distortion looks like?

This leads back to the central question: is the market being distorted?

If distortion is defined simply as capital flowing towards areas of genuine opportunity, then the current trend may be justified. AI clearly represents a significant technological shift with wide-ranging implications.

However, if distortion is understood as a change in the structure and balance of capital allocation, the picture becomes more complex. The current environment isn’t just characterised by concentration, it’s also characterised by acceleration and asymmetry. Capital is being deployed faster, earlier, and with greater intensity in a narrower set of opportunities.

That, in turn, changes the conditions for the rest of the market.

Beyond the boom

AI is clearly attracting capital for legitimate reasons. The scale of technological change, the speed of adoption, and the size of the potential market opportunity all help explain why investors are moving aggressively.

But the more important shift is how the growth we’re seeing in AI is reshaping the wider market.

Capital is becoming more concentrated, deployed earlier, and allocated with greater conviction around a relatively narrow set of themes. That changes fundraising conditions for everyone else. It alters which companies receive attention, which sectors are prioritised, and how investors evaluate risk.

History suggests these periods of concentration rarely affect only the companies at the centre of the boom. They also create opportunities elsewhere, particularly for businesses operating outside the dominant narrative, where competition for capital and pressure to build sustainable models is often higher.

The question, then, is not whether AI deserves the investment it is attracting. It is whether the wider market is becoming too dependent on a single story to define its sense of growth. Understanding that distinction may be critical to identifying where the next generation of resilient companies emerges.