The News
Moon Pursuit Capital founder and managing partner Utkarsh Ahuja is making the case that venture capital’s concentration in AI is creating meaningful opportunity gaps in adjacent emerging technology sectors. Drawing on PitchBook data showing U.S. startups have attracted roughly $1 trillion in venture investment since 2020, with AI absorbing a growing share of that flow, Ahuja argues that digital assets, tokenization, blockchain infrastructure, and quantum security are comparatively underserved by current capital allocation. His thesis is straightforward: when everyone is crowding the same trade, the overlooked corners of the market tend to reprice.
Analyst Take
AI Valuation Concentration Is a Real Risk, Not Just a Talking Point
The argument that AI deal valuations are running hot is not contrarian anymore; it’s becoming consensus. When a single technology category absorbs the majority of a $1 trillion capital pool, late-stage entrants are paying peak prices for assets whose commercial returns remain, in many cases, unproven at scale. This is not a knock on AI’s long-term importance. It’s a structural observation about where risk-adjusted returns are likely to come from over the next five to seven years.
For ITDMs, this matters indirectly but consequentially. Vendor pricing, product roadmap ambition, and the rate of M&A consolidation in the AI tooling space are all downstream of how VC capital is flowing. Inflated valuations in AI infrastructure will eventually compress, and that compression will land on enterprise buyers in the form of pricing resets, pivot announcements, and vendor attrition. Buying decisions made today against AI vendors carrying aggressive multiples carry more counterparty risk than they might appear to on the surface.
The Innovation Time Tax
There’s a parallel dynamic on the developer side. According to ECI Research’s 2026 Application Development survey, 65.2% of respondents selected “0–20” when asked what percentage of engineering time is spent on net-new innovation. That figure is striking. The overwhelming majority of engineering organizations are spending most of their capacity on maintenance, operations, and incremental improvement rather than on building genuinely new capabilities. If AI-assisted development tools are supposed to change that ratio, the evidence of impact at scale is not yet visible in the data.
This matters to Ahuja’s thesis in a concrete way. The case for rotating capital toward tokenization infrastructure, quantum security, or decentralized data architectures is partly a case about where the next engineering frontier sits. If developer attention and enterprise investment are both saturated around AI, the opportunity cost of ignoring adjacent infrastructure categories grows. The organizations and investors who show up early in quantum-safe cryptography or programmable finance infrastructure are making a bet that the next wave of developer time will flow there, once AI tooling matures enough to free capacity for something new.
What Gets Left Behind When Capital Herds
The sectors Ahuja identifies as underserved share a common characteristic: they require patient capital and deep technical fluency to evaluate. Blockchain infrastructure and quantum security are not seed-stage narrative businesses. They involve cryptographic primitives, protocol-level architecture decisions, and regulatory exposure that most generalist VCs are not equipped to underwrite. That expertise gap, not a lack of commercial potential, is what keeps capital on the sidelines.
ECI Research’s 2026 DevSecOps and AppSec survey adds a relevant data point here: 29.1% of respondents selected “AI-generated package risk” as their biggest open-source security concern in 2026. That concern is real, but it also reflects how security attention is being pulled toward AI-adjacent risks while longer-horizon threats like quantum decryption of today’s encrypted data receive comparatively less organizational urgency. The “harvest now, decrypt later” attack vector that quantum computing enables is not a 2026 problem. But it is a problem for data being encrypted and stored today. Organizations and investors who treat quantum security as a future problem are misreading the timeline.
Looking Ahead
The VC market’s current AI concentration will likely continue through 2025 and into early 2026, driven by genuine commercial momentum in enterprise AI adoption. ECI Research’s 2026 Application Development survey found that 53.5% of respondents selected “AI-enabled development tools” as a top investment priority for the next 12 months, confirming that enterprise demand is real and that vendor revenue stories are credible enough to sustain current valuations for now. The rotation thesis Ahuja is articulating is more likely a 2026–2028 story than an immediate inflection, but investors who wait for obvious consensus signals before moving into quantum security or tokenization infrastructure will find the entry points considerably less attractive.
For enterprise technology buyers, the more immediate takeaway is to watch vendor health indicators closely in AI tooling categories where valuation has outrun revenue. The next 18 months will likely produce meaningful consolidation, and the winners in that consolidation will not necessarily be the best products. They will be the products with the best capital structures. Planning for vendor transitions in AI tooling is not pessimism; it’s sound procurement strategy given where the cycle sits.
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