Deep tech has long been defined by liquidity scarcity: decade-long development cycles, capital-heavy milestones, and exits that depend on either industrial consolidation or public markets with a high tolerance for technical complexity. Yet the most surprising shift in today’s market is that this scarcity is being alleviated not through a resurgence of traditional exits, but through the rapid evolution of secondary markets. What once functioned as an occasional release valve has become a structural component of venture liquidity, reshaping how capital flows into and through deep tech companies.
This shift is amplified by the concentration of capital within AI infrastructure. Billions are flowing into a narrow set of compute, model, and data platform categories, creating both unprecedented liquidity opportunities and valuation distortions that challenge conventional underwriting. Secondary markets are increasingly where these tensions play out: where early investors seek partial liquidity, where new capital looks for de-risked exposure, and where pricing reflects both conviction and speculative pressure.
The dynamics diverge sharply from software-driven venture markets, where recurring revenue and lightweight distribution models enable shorter cycles and predictable exit pathways. In deep tech, the stakes are larger, timelines longer, and the link between valuation and realized performance far more complex. Understanding the emerging role of secondaries is now essential for constructing resilient portfolios and forming realistic expectations about liquidity in capital-intensive technology sectors.
At first glance, it seems counterintuitive that rising capital availability would accelerate secondary activity. Conventional wisdom suggests that when funds are well-capitalized, they hold positions longer and rely less on partial liquidity. But deep tech does not follow conventional patterns. The concentration of capital into a limited number of AI infrastructure categories—compute, model development, and hyperscale data operations—has created structural bottlenecks. While more money is chasing the same set of companies, the exit pathways have not scaled to match the inflows.
As a result, secondary markets absorb the friction created by abundant capital meeting limited deployment opportunities. Investors competing for access to breakout deep tech companies often find themselves crowded out of primary rounds, which are either closed early or dominated by strategic investors with privileged insight into infrastructure demand cycles. For newcomers or later-stage funds seeking exposure to category leaders, secondary transactions become the most reliable entry point.
Meanwhile, traditional exit routes—M&A and IPOs—have stabilized but not expanded enough to offset longer private holding periods. Instead of replacing these pathways, the secondary market has become the third and increasingly essential leg of the liquidity model. M&A remains unpredictable in deep tech; IPOs require revenue scale that many infrastructure companies only reach late in their lifecycle. Secondaries provide a flexible middle ground where investors can rebalance portfolios, address duration risk, and meet distribution obligations.
This interaction is reshaping the composition of market participants. Early-stage funds, facing pressure to demonstrate DPI in older vintages, are more open to selling. At the same time, sovereign wealth funds, corporate strategics, and crossover investors are eager to gain pre-exit exposure to companies that have validated their technical feasibility and line of sight to commercial scale. This meeting point—liquidity-seeking sellers and capital-intensive buyers—forms the backbone of today’s secondary deep tech ecosystem.
The implications ripple through fund construction. Managers are increasingly modeling their return strategies with the expectation that partial liquidity events occur before traditional exits. This influences reserve planning, follow-on pacing, and even initial position sizing. Secondaries are no longer opportunistic; they are embedded in how capital allocators structure their participation in deep tech cycles.
Secondary investing in deep tech diverges sharply from conventional secondary strategies. Traditional secondary buyers focus on companies with predictable cash flow, proven revenue, and a two- to three-year timeline to liquidity. The goal is to purchase positions at a discount relative to future realizations. Deep tech secondaries, however, are driven by very different motivations—chiefly, strategic positioning and exposure to market-defining infrastructure.
AI infrastructure illustrates the point most vividly. The scramble for training and inference capacity has produced a secondary environment where investors value optionality more than fundamentals. Instead of underwriting based on financial metrics, buyers often focus on a company’s potential placement within a future market structure: Will this model platform become one of the few that matters? Will this hardware layer become indispensable as compute demand expands? These questions shape pricing more than near-term revenue or margin outlook.
The market structure of large language models reinforces this behavior. The prevailing view that the sector will consolidate around two or three dominant platforms drives investors to secure exposure early, even if valuation multiples reflect future dominance rather than current performance. This creates a “feeding frenzy” at the upper tiers of the stack, where buyers assume that leadership today implies enduring relevance across multiple product cycles.
But this dynamic introduces significant risk. When secondary prices reflect positioning rather than unit economics, the gap between valuation and fundamentals can widen quickly. Deep tech companies, even those experiencing strong demand, often face long deployment cycles, integration complexity, and infrastructure upgrades that delay revenue realization. As a result, investors must distinguish between secondary opportunities grounded in durable advantage and those inflated by competitive pressure for access.
The most disciplined strategies focus on structural advantages—such as proprietary data, defensible hardware IP, or geographic positioning within supply-constrained markets—rather than headline valuation trends. This discipline becomes increasingly important as infrastructure spending normalizes and the market begins to reward execution rather than potential.
The rapid buildout of AI infrastructure has introduced a potential overcapacity challenge that could reshape secondary valuations over the next several years. The industry is deploying compute and data infrastructure at a historic pace, fueled by the belief that demand for training and inference will scale exponentially. Yet demand growth is uneven across enterprises, sectors, and geographies, creating a mismatch between supply built today and usage that may materialize later—or not at all.
This provisioning cycle is typical of early-stage infrastructure booms. Supply comes online faster than adoption; capacity is built based on expectations rather than confirmed utilization; and smaller players are often left with imbalance risks that hyperscalers can absorb. Well-capitalized hyperscale and neocloud providers can navigate temporary overcapacity because they operate with diversified customer bases and long-term infrastructure roadmaps. Smaller infrastructure providers, however, may struggle to finance the gap between buildout and revenue realization.
The timing challenge intensifies the uncertainty. While development costs for certain AI products have fallen and commercialization cycles have compressed, scaling revenue still requires customer adoption to keep pace with the underlying infrastructure. This creates periods where infrastructure operators have sunk cost commitments but insufficient throughput to reach efficient utilization rates.
For secondary investors, the key is identifying which companies are positioned to withstand these cycles. The strongest opportunities typically involve: alignment with hyperscaler demand curves, differentiated infrastructure architectures that cannot be easily replicated, or favorable cost structures that allow flexible pricing during utilization dips. By contrast, companies overly dependent on speculative demand or narrow customer segments face higher downside risk if overcapacity materializes.
Evaluating secondary exposure through this lens is critical. Investors need to distinguish between companies that are strategically well-positioned within the broader AI and data ecosystem, and those that have scaled ahead of demand without sufficient diversification or defensibility.
While AI attracts the largest share of attention and capital, deep tech encompasses a broader spectrum of investable categories. Understanding these layers provides a framework for assessing secondary liquidity and balancing portfolio exposure across technologies with different capital requirements and exit profiles.
Layer One — Physical Infrastructure. This includes hardware systems, data center buildout, sensing platforms, advanced materials, and emerging intelligence infrastructure that extends beyond narrow AI applications. These businesses tend to require significant upfront capital, extended development timelines, and specialized expertise. Their valuation cycles move more slowly than AI software but often produce durable competitive moats once scale is achieved. Secondary markets in this layer remain selective but can offer strong opportunities when companies achieve technical validation and begin moving toward commercialization.
Layer Two — Intelligent Software Systems. These are AI-native software applications, developer tooling platforms, and vertical implementations across industries such as healthcare, logistics, manufacturing, and financial services. Compared with infrastructure, these companies exhibit lighter capital intensity and more predictable revenue models. Secondary buyers often favor this layer for its clearer path to scale and lower technical risk. Liquidity tends to emerge earlier, driven by strategic acquirers and late-stage investors looking for exposure to applied AI rather than foundational infrastructure.
Layer Three — Bio-convergence. Robotics, advanced surgical systems, miniaturized diagnostics, and connected medical devices represent a distinct set of opportunities with specialized risk-return dynamics. Regulatory pathways, clinical validation cycles, and hardware integration create unique barriers to entry. Secondary liquidity exists but behaves differently: investors prioritize data defensibility, regulatory milestones, and platform potential rather than market-share narratives. This layer’s long development cycles mean secondary transactions often occur around key inflection points rather than financial performance.
Mapping these layers clarifies where secondary liquidity is most likely to emerge and how investors can diversify exposure across the deep tech landscape. Each layer offers different catalysts, valuation patterns, and resilience profiles, enabling sophisticated capital allocators to build balanced portfolios that reflect both current market conditions and long-term innovation trends.
The lengthening of private market holding periods has triggered behavioral shifts across the venture ecosystem. Limited partners are increasingly vocal about the need for distributions, especially as strong public market performance highlights the opportunity cost of capital locked in illiquid positions. This DPI pressure is particularly acute for funds with large allocations to capital-intensive deep tech companies, where the timeline from Series A to exit can span a decade or more.
General partners are responding by engaging more proactively in secondary markets, both to return capital and to manage duration risk. The practice of facilitating structured secondary rounds has become normalized. Rather than signaling distress, liquidity programs are now viewed as a pragmatic tool to align incentives across investors and founders while extending runway for the company.
For founders, secondary liquidity reshapes cap table management and company-building strategy. The ability to take modest personal liquidity reduces pressure to pursue premature exits or high-risk financing structures. However, it also raises expectations: investors expect founders who benefit from early liquidity to remain focused on long-term value creation rather than short-term valuation targets. In deep tech, where capital intensity can stretch founder horizons, this balance is especially important.
Across the ecosystem, fundamentals are making a comeback. Even in sectors dominated by hype, investors increasingly expect clear revenue trajectories, disciplined margin expansion, and credible paths to profitability. This reset reinforces the importance of connecting technical milestones to economic outcomes, particularly for companies operating within infrastructure or regulated environments.
The rise of deep tech secondaries requires a deliberate and structured investment approach. Sophisticated investors need frameworks that separate durable opportunities from momentum-driven activity and support balanced portfolio construction across long-horizon technologies.
An effective evaluation framework involves three core assessments:
Portfolio strategy should balance primary and secondary participation. Secondary entry can provide lower technical risk and exposure to validated companies, but it also carries valuation and duration risks if timed poorly. The most effective strategies reserve capital for both early technical breakthroughs and later-stage consolidation opportunities.
Fundamentals remain central. Revenue growth, margin trajectory, customer retention, and cost structure still define long-term outcomes, even in frontier categories. For infrastructure-heavy companies, evaluating these fundamentals requires understanding utilization cycles, supply constraints, and the interplay between hardware and software layers.
Looking forward, secondary markets in deep tech will continue to mature. As the current AI cycle stabilizes and potential overcapacity is digested, liquidity mechanisms will expand to support other emerging sectors such as robotics, bioengineering, and next-generation materials. Investors who build flexible strategies around both technological progression and market structure evolution will be well positioned to capture value as deep tech continues its shift from niche to central within global innovation markets.
