The secondaries market for AI startups sits at a crossroads of exuberant AI adoption, heightened scrutiny of business models, and evolving liquidity constraints. Pricing in AI-focused secondary transactions remains distinctly bifurcated: assets with proven data networks, disciplined unit economics, and credible path to revenue frequently command pricing closer to the last private round, while early-stage, data-limited AI ventures with uncertain unit economics trade at meaningful discounts to reflect elevated execution risk and longer horizons to material milestones. Across the cohort, secondary price discovery is increasingly nuanced, balancing a push for liquidity with the premium demanded by investors who must account for model risk, compute cost trajectories, data governance, and regulatory developments. Historically observed secondary discounts to prior round prices typically range from roughly 15% to 40%, with the bulk clustering near the mid-teens to mid-twenties for high-quality assets. In practice, top-tier AI assets with strong moat signals and visible revenue drag—even if still private—may fetch pricing nearer 0.8x to 0.95x last-round valuations, while lower-quality or data-constrained assets trade toward 0.6x–0.75x. Time to liquidity generally spans 12 to 36 months, with variations driven by the asset’s data network effects, customer concentration, and enterprise adoption velocity. The investment thesis for secondary buyers remains anchored in the ability to price risk via data-driven due diligence, robust due diligence on model maturity and training data, and clear alignment of incentives through bespoke deal terms. Looking forward, the trajectory of AI affordability, compute efficiency, and enterprise AI deployment will be the primary determinants of how aggressively secondary pricing tightens or widens, with macro volatility and regulatory clarity acting as meaningful accelerants or dampeners.
The market is increasingly informed by cross-asset signals: public AI stock performance, private round liquidity pressures, and the emergence of AI-focused evergreen and managed secondary vehicles. These dynamics shape not only pricing levels but also structuring choices around liquidity preferences, ratchets, and governance rights. For investors, the implication is clear: core value lies in buying into AI startups whose data assets and productized AI capabilities translate into durable unit economics, rather than chasing broad hype. In this environment, sophisticated buyers employ scenario-based pricing, discount rate adjustments for burn and run-rate risk, and explicit sensitivity to compute cost curves, customer concentration risk, and the potential for platform-agnostic AI models to erode defensible moats.
Overall, the secondaries market for AI startups offers meaningful liquidity albeit with dispersion across these assets. Investors should anticipate continued price dispersion, punctuated by episodic re-pricing around notable AI product launches, regulatory clarity, or shifts in enterprise AI procurement. For portfolio managers, the prudent path combines selective participation in high-quality AI platforms with rigorous risk-adjusted pricing frameworks and a disciplined approach to capital deployment in assets with demonstrable data advantages and predictable monetization pathways.
The AI sector remains characterized by rapid technology turnover, capital intensity, and asymmetric information between private holders and potential buyers. Secondary pricing in AI startups reflects both the momentum of AI adoption and the discipline of venture markets to recalibrate expectations in light of evidentiary milestones. In recent cycles, AI startups with defensible data assets—where data rights, data networks, and model performance translate into durable customer lock-in—have tended to command relatively tighter discounts to last round valuations, supported by clearer revenue visibility and lower model risk. Conversely, AI ventures centered on novel architectures or unproven data acquisition strategies must contend with higher dislocations between expected and realized value, manifesting as steeper discounts and longer time to liquidity.
The pricing dynamic is further influenced by stage mix within secondary programs. Later-stage AI startups with recurring revenue, enterprise adoption, and multi-year contracts attract buyers who price in lower risk and greater certainty around cash flows, often narrowing the discount to a fraction of the last round price. Early-stage AI bets, by contrast, are priced with substantial risk premia tied to product-market fit, data acquisition velocity, regulatory headwinds, and the evolution of compute costs. In both cases, the structure of the deal—liquidity preferences, governance rights, pro rata protections, and potential co-investment terms—plays a decisive role in translating price into expected return.
Another salient factor is the evolving regulatory environment surrounding AI data usage, IP ownership, and model transparency. Buyers increasingly emphasize robust data governance provisions, clear data provenance, and defined rights to use training data post-transaction. These elements typically act as price modifiers: assets with well-documented data ecosystems, compliant data handling, and transparent training datasets can command narrower discounts and tighter price ranges. In contrast, assets lacking explicit data rights or facing regulatory ambiguity tend to trade with wider price bands to compensate for the heightened risk of value erosion over the investment horizon. Currency volatility, cross-border investment frictions, and local tax considerations also shape pricing dynamics in global secondary markets for AI startups.
Supply-side factors are equally influential. The proliferation of AI-focused secondary funds, evergreen vehicles, and SPV-based liquidity channels has increased competition for high-quality assets, partially compressing discounts in favorable liquidity windows. However, supply constraints persist for truly differentiated AI platforms with operational moat, leaving room for selective outperformance where buyers can articulate a credible path to scale and a defendable data advantage. Overall, the market context implies a continued bifurcation in pricing, driven by asset quality, data advantages, and the clarity of monetization pathways—rather than a single, uniform re-pricing across the AI startup landscape.
Core Insights
Pricing in AI startup secondaries is best understood through four interlinked forces: data moat durability, monetization pace, governance and control considerations, and external market dynamics. First, data moat durability—defined as the extent to which data assets, data networks, and model access create defensible advantages—acts as a critical pricing anchor. AI startups that can demonstrate compounding data advantages, high customer retention, and low marginal cost of model improvement tend to trade at tighter discounts, as buyers anticipate faster revenue realization and higher re-use of existing data assets. Conversely, ventures whose competitive differentiators rely on proprietary compute infrastructure, unproven data collection approaches, or uncertain data rights experience wider spreads, as pricing must incorporate both the possibility of demonstration risk and the potential depreciation of data advantages over time.
Second, monetization pace remains central. Secondaries pricing weighs heavily on run-rate clarity, gross margin trajectory, and contract visibility. Mature AI startups with diversified revenue streams, multi-year customer contracts, and visible cross-sell opportunities offer buyers a clearer path to profitability, supporting more favorable pricing relative to last-round valuations. In contrast, early-stage AI ventures with limited revenue traction, high customer concentration, or long sales cycles face steeper discounts to compensate for the time-to-meaningful cash generation and the risk of further dilution. Buyers increasingly stress evidence of product-market fit, repeatable sales cycles, and customer churn metrics as inputs to a pricing framework that translates into expected IRR thresholds.
Third, governance and control influence price discovery. Secondary structures frequently include governance provisions that protect downside risk, such as liquidity preferences or anti-dilution protections, and may offer prospective co-investment rights. The presence or absence of board seats, observer rights, and veto powers on major strategic decisions materially affects price negotiation. Assets offering stronger governance alignment—e.g., investors who maintain a meaningful influence over strategic direction or data strategy—often secure tighter pricing ranges, while assets with looser control rights typically trade at larger discounts to reflect the potential dispersion of value in a contested future.
Fourth, external market dynamics—macro liquidity, equity risk premia, and AI sector sentiment—shape the price environment. When liquidity is abundant and private markets are receptive to AI momentum, discounts compress and the price-to-last-round metrics converge toward parity or a modest premium for select assets. During periods of liquidity stress or heightened risk aversion, buyers demand steeper discounts, particularly for AI startups lacking defensible data moats or with limited path to sustainable profits. In addition, the availability of co-investment opportunities and the emergence of AI-focused evergreen and GP-led blue-chip secondaries influence the pricing complexion by providing alternative exit routes and longer-duration liquidity, thereby stabilizing or slightly elevating average pricing in certain segments.
Beyond pricing, deal terms are increasingly used as value levers. The availability of data rights protections, restrictions on competing offerings, and dedicated data collaboration arrangements with original founders can enhance post-deal value, effectively reducing the net price required to achieve desired risk-adjusted returns. In parallel, buyers incorporate scenario-based adjustments for potential regulatory changes, energy and compute cost volatility, and the evolution of AI governance standards, ensuring that pricing remains forward-looking rather than a backward-looking arithmetic exercise. Taken together, these insights indicate a market that prices AI startups not merely on current revenue or user bases, but on a sophisticated synthesis of data assets, monetization trajectory, governance alignment, and external risk factors.
Investment Outlook
The investment outlook for secondaries in AI startups remains favorable for investors who can fuse rigorous, data-driven due diligence with disciplined pricing discipline. The next 12 to 24 months are likely to see notable differentiation between “data-first” AI platforms—those with defensible datasets, high data cadence, and strong enterprise adoption—and more speculative ventures whose AI propositions depend on unproven data networks or on early-stage platform dynamics. For data-first AI platforms, secondary pricing will increasingly reflect revenue visibility, long-term contractual commitments, and the defensibility of data rights, leading to tighter discounts and potentially near-parity pricing for late-stage assets with clear monetization channels. For speculative AI bets, expect persistent dispersion in pricing, with larger discounts to compensate for the risk of delayed or inconsistent monetization and potential data governance challenges. Across both segments, the dispersion in pricing will be shaped by the quality of the data asset, the trajectory of AI compute costs, and the clarity of the path to sustainable profitability.
In the near term, buyers will place greater emphasis on three metrics: data asset quality, contract-backed revenue visibility, and the strength of data governance. The first metric—data asset quality—encompasses the scope, provenance, and licensing rights of datasets used to train and fine-tune models, as well as the ease with which those data assets can be monetized or leveraged to improve product performance. The second metric—revenue visibility—centers on the predictability of ARR, contracted revenue, and renewal rates, with preference given to assets displaying multi-year enterprise commitments and a credible plan for expanding the addressable market. The third metric—data governance—addresses compliance, data lineage, and risk controls that mitigate regulatory exposure, including privacy considerations and model governance. When these factors align, secondary buyers are likely to accept tighter discounts and potentially more favorable deal dynamics, reducing the likelihood of protracted negotiations and enabling faster liquidity.
From a portfolio perspective, GP-led secondary programs and evergreen vehicles will continue to play an important role in AI asset pricing by offering structured liquidity channels and risk-sharing arrangements. The presence of such vehicles often compresses price volatility by providing a credible, longer-horizon liquidity option, which in turn stabilizes price discovery for high-quality AI startups. LPs seeking exposure to AI platforms tend to favor assets with demonstrable data advantages and clear monetization paths, as these assets deliver more predictable IRRs and better protection against sharp drawdowns in a volatile AI cycle. Overall, the outlook supports a cautious, quality-driven approach to secondaries investing in AI startups, with pricing anchored in durable data assets, revenue visibility, and robust governance, rather than purely on hype or model novelty.
Future Scenarios
In a base-case scenario, continued AI deployment across enterprise segments supports steady progress in data asset formation, customer adoption, and recurring revenue generation. Secondary pricing in AI startups tightens modestly as data advantages become more widely recognized and as co-investment and GP-led liquidity channels proliferate. In this environment, pricing for late-stage AI platforms could converge toward last round valuations with discounts in the low-to-mid teens for top-tier assets, while mid-tier and early-stage ventures still command meaningful discounts in the mid- to high-teens range. Time to liquidity would stabilize around 18 to 30 months as market participants gain greater confidence in near-term monetization pathways and regulatory clarity improves risk pricing.
A bullish scenario emerges if enterprise AI adoption accelerates more rapidly than anticipated, driven by breakthrough improvements in model efficiency, data governance frameworks, and a broad-based commercialization of AI-enabled workflows. In such a scenario, demand for AI data assets intensifies, and selective AI platforms with deep moat signals may trade at tighter discounts or even near parity with last-round prices, particularly where data rights and monetization pipelines are unambiguously proven. Liquidity channels would expand, with more GP-led programs and direct co-investments, compressing volatility and shortening time to liquidity to the 12–24 month window. Valuation supports would come from consistent renewal of enterprise contracts, higher gross margins, and evidence of durable network effects that create compounding value over time.
A downside scenario accompanies a sustained deceleration in enterprise AI adoption, continued compute cost pressures, or a regulatory tightening that constrains data usage and model deployment. In this case, secondary pricing would likely move to deeper discounts, particularly for early-stage or data-constrained assets. Pricing might settle in the 0.6x–0.75x range of last-round values for riskier assets, with time to liquidity extending toward 36 months or longer if buyers require tighter risk mitigation terms. Seller appetite for near-term liquidity could intensify, but the volatility of enterprise demand would complicate price discovery and could reduce the incidence of GP-led stitching that otherwise provides liquidity and valuation support.
Finally, an extreme stress scenario—triggered by a material AI governance failure, a major data breach, or an abrupt shift in regulatory stance—could precipitate a broad repricing across the AI secondary market, with wide discounts, longer holding periods, and a re-pricing of risk premia across the asset quality spectrum. While unlikely to characterize the baseline, investors must incorporate such tail risk into sensitivity analyses, particularly for assets with lean governance structures and uncertain data provenance.
Conclusion
The secondaries market for AI startups remains a pivotal liquidity channel that aligns patient capital with high-conviction AI platforms. Pricing in this market is not a mechanical function of last-round valuations but a layered assessment of data assets, revenue visibility, governance rights, and macro- and regulatory risk. The strongest value propositions come from AI startups that can demonstrate durable data moats, repeatable revenue trajectories, and robust data governance constructs. In such cases, secondary buyers can justify tighter discounts, enable faster liquidity, and potentially achieve attractive risk-adjusted returns through disciplined diligence and structured deal terms. Conversely, assets lacking a credible path to monetization, or those subject to regulatory and data governance uncertainties, will continue to trade with meaningful discounts that reflect the risk embedded in future milestones. Investors who succeed in this market will deploy a disciplined framework that (1) evaluates data asset quality and governance as primary risk-adjusted pricing inputs, (2) cross-checks monetization assumptions with observable customer engagement and renewal dynamics, and (3) leverages diverse liquidity channels—including GP-led programs and evergreen vehicles—to optimize timing and certainty of exits. As AI technologies mature and enterprise adoption deepens, the secondaries market is likely to evolve toward more precise pricing anchored in demonstrable data-driven value creation, with pricing convergence for the most durable assets and selective expansion of liquidity options for high-quality AI platforms.