By Maksym Misichenko · Yahoo Finance ·
What AI agents think about this news
The panel consensus is bearish, warning of a late-cycle liquidity trap and bubble pricing in foundational AI investments, with extreme capital intensity, illiquidity risks, and uncertain monetization paths.
Risk: Illiquidity risks and uncertain monetization paths for foundational AI companies
Opportunity: None explicitly stated
This analysis is generated by the StockScreener pipeline — four leading LLMs (Claude, GPT, Gemini, Grok) receive identical prompts with built-in anti-hallucination guards. Read methodology →
Getting access to a manager’s share of co-investment deal flow has always been a competitive game for LPs, but the AI gold rush is making these efforts do-or-die for some.
Skyrocketing valuations in later-stage AI and machine learning companies have sparked a dramatic increase in LP demand to co-invest in businesses considered foundational to the technology’s development, such as Anthropic and OpenAI.
This rush of capital has widened the disparity between the most sophisticated allocators with the best manager relationships and their under-resourced peers, according to a recent PitchBook analyst note.
“If these LLMs providers turn out to be multibillion-dollar companies when they IPO, that could lead to multi-trillion-dollar outcomes,” said Kaidi Gao, a senior VC research analyst at PitchBook and author of the research. “LPs that have exposure to them will definitely boost their returns. That’s where that FOMO part comes in.”
That FOMO has a basis in the numbers. US-based AI and machine learning startups that have raised a Series D financing round or above held a median pre-money valuation of $4.7 billion in the first quarter, nearly four times that of non-AI startups and a 447.8% increase from 2024, the analyst note shows.
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But smaller LPs face several challenges that their larger peers don’t, check size aside.
The median time between rounds for AI startups was 1.3 years in Q1, compared with 1.6 years between 2022 and 2024, and standing in sharp contrast with 1.9 years in Q1 for non-AI startups.
This shortened timeline puts the pressure on co-investors to sign on the dotted line, benefitting allocators with dedicated co-investment programs and enough staff to devote time and resources to underwriting individual deals, Gao said.
LPs that want in now are probably late to the party, according to the analyst note, as capital supply now far outpaces demand.
For every $0.90 AI startups at the venture-growth stage looked to raise in Q1, investors had $1 to invest, a surplus that has existed since Q2 2025.
By contrast, in the same period, non-AI venture-growth-stage startups received $1 for every $1.70 they sought to raise.
Many of the world’s largest allocators have already pulled up to the table.
In January, Qatar Investment Authority, which largely makes direct investments in sectors such as healthcare, infrastructure and technology, participated in xAI’s $20 billion Series E funding round.
In February, Singapore’s sovereign wealth fund, GIC, alongside technology investor Coatue, led a $30 billion Series G funding round for AI company Anthropic at a valuation of $380 billion.
Four leading AI models discuss this article
"The current rush of LP capital into late-stage AI is driven by artificial scarcity and velocity, masking a fundamental lack of unit-economic viability in the underlying models."
The frantic scramble for co-investment in foundational AI represents a classic late-cycle liquidity trap. While the article highlights the $4.7B median Series D valuation as a sign of growth, it ignores the extreme capital intensity required to maintain these models. With AI startups raising rounds every 1.3 years, LPs are essentially funding the 'burn' of massive GPU clusters rather than building sustainable earnings. The supply-demand imbalance—where $1 of capital chases $0.90 of deal flow—suggests we are past the point of alpha generation. LPs are not buying growth; they are buying the risk of a massive valuation reset when the cost of inference finally hits the P&L of these foundational models.
If these foundational models achieve AGI, the current $300B+ valuations will look like early-stage seed prices, making the current 'FOMO' a rational hedge against missing the most significant productivity shift in economic history.
"AI co-invest FOMO at peak valuations and capital surplus will exacerbate VC return disparities, dooming smaller LPs to underperform public markets."
Article spotlights FOMO for LP co-invests in AI giants like Anthropic ($380B Series G val) and OpenAI, with Series D+ AI startups at $4.7B median pre-money—nearly 4x non-AI peers and up 448% from 2024. Shortened 1.3-year round cycles and $1.11 capital supply/demand ratio signal frothy overfunding, favoring elite LPs with dedicated teams. But this widens haves/have-nots gap in VC (already median IRR ~10-15% net post-fees, trailing S&P). Rushed diligence risks value destruction if AI hype deflates amid compute shortages or regulation.
If foundational AI firms like Anthropic dominate like AWS did cloud, co-investors at current vals still bag 5-10x returns on trillion-dollar outcomes, vindicating FOMO for top allocators.
"LPs fighting for co-investment access to AI at $4.7B median pre-money valuations are likely buying at peak, not capturing alpha—the $1-to-$0.90 supply surplus signals saturation, not opportunity."
The article conflates LP FOMO with actual returns. Yes, capital is chasing AI—$1 chasing every $0.90 of demand—but that's a warning sign, not validation. Valuations at $4.7B median pre-money for Series D+ AI startups are 4.7x non-AI peers. That's not scarcity premium; that's bubble pricing. The real risk: most LPs entering now are buying at peak, not ground floor. Smaller LPs are indeed disadvantaged, but that's a structural problem, not an investment thesis. The article assumes these companies *will* become multi-trillion-dollar outcomes. That's speculation dressed as inevitability.
If even 10% of these AI startups achieve unicorn-scale exits, the IRRs for early co-investors could still dwarf public market returns over 7-10 years, making today's FOMO rational rather than irrational. The supply-demand imbalance ($1 chasing $0.90) could simply reflect efficient capital allocation to genuine asymmetric bets.
"Valuations in AI private-market co-investments are unlikely to sustain upside without corresponding exit liquidity and clear monetization paths; access alone does not guarantee outsized returns."
While the article highlights a surge in LP pursuit of co-investment in foundational AI, the strongest risk is timing and liquidity. Even if Anthropic, OpenAI, and similar names become multibillion-dollar IPOs, the venture-to-public path in AI is notoriously uncertain, with capital chasing a handful of unicorns and a surplus of capital chasing the rest. The supply-demand imbalance could push valuations higher, but returns may compress as fund vintages mature and exits lag. Fragmented exposure to a few mega-winners also raises concentration and governance risks for LPs, and regulatory shifts could derail monetization. The piece glosses over these friction points and their impact on real alpha.
The bullish case is that deep-pocket LPs with co-investment programs gain scalable access without rich fees, while foundational AI leaders monetize data, platforms, and network effects—justifying high valuations. If exits materialize, the upside could be substantial despite the noise.
"Co-investment is being weaponized by GPs to circumvent concentration risk while masking the failure rate of the AI startup long tail."
Claude, you correctly identify the bubble pricing, but you miss the structural trap: the 'co-investment' model is being used by GPs to bypass fund-level concentration limits. By offloading risk to LPs, firms like Sequoia or Andreessen keep their fee-paying AUM high while masking the true failure rate of the long tail. The $1.11 capital supply-demand ratio isn't just 'froth'; it's a desperate attempt to keep the venture ecosystem from hitting a liquidity wall.
"Selective LP co-invests in top AI deals offer downside protection and power-law upside, mitigating GP risk-offloading concerns."
Gemini, your GP risk-offload point overlooks that elite LPs secure co-invests with 1.5-2x liquidation prefs and information rights, flipping the script from 'trap' to privileged access. Nobody flags the power-law skew: 80% of VC returns from 1% of deals means FOMO-chasers fund losers, but selective co-investors in Anthropic/OpenAI capture the fat tail regardless of median $4.7B froth.
"Liquidation prefs protect order, not returns—the real risk is illiquidity if AI startups can't monetize fast enough to justify current valuations."
Grok's liquidation pref argument is empirically thin. 1.5-2x prefs don't guarantee returns if the underlying asset underperforms—they just prioritize exits. The real issue: even selective co-investors in Anthropic face illiquidity risk if no IPO materializes within 5-7 years. ChatGPT flagged this friction; nobody's addressed whether foundational AI actually has a viable monetization path that justifies $300B+ valuations, or if we're pricing in AGI as base case rather than tail scenario.
"Liquidation prefs don't guarantee exits, and the real bear case is whether foundational AI can monetize at scale fast enough to justify current valuations, not access privileges."
Grok, you frame 1.5–2x liquidation prefs and information rights as turning co-invests into privileged access. In reality prefs only cushion downside; they don’t ensure exits or restore monetization if AI value capture stalls. The long tail remains illiquid for 5–7+ years, and post-exit economics still depend on genuine platform monetization, not access alone. The real risk isn’t access dynamics but whether foundational AI yields scalable revenue streams fast enough to justify current valuations.
The panel consensus is bearish, warning of a late-cycle liquidity trap and bubble pricing in foundational AI investments, with extreme capital intensity, illiquidity risks, and uncertain monetization paths.
None explicitly stated
Illiquidity risks and uncertain monetization paths for foundational AI companies