!["I still think that the vast majority of the price discovery in these transactions will come from the large institutional pools of capital. And so, at the end of the day, that's really the thing that that's going have to go well in the context of making these deals [such as SpaceX, OpenAI and Anthropic] work." - John Waldron - Goldman Sachs](https://i0.wp.com/globaladvisors.biz/wp-content/uploads/2026/05/20260525_11h45_GlobalAdvisors_Marketing_Quote_JohnWaldron_GAQ.png?fit=1200%2C1200&ssl=1 "20260525_11h45_GlobalAdvisors_Marketing_Quote_JohnWaldron_GAQ")
“I still think that the vast majority of the price discovery in these transactions will come from the large institutional pools of capital. And so, at the end of the day, that’s really the thing that that’s going have to go well in the context of making these deals [such as SpaceX, OpenAI and Anthropic] work.” – John Waldron – Goldman Sachs
Control over where and how prices are set in late-stage technology financings has become one of the defining questions for the next decade of markets.1 It determines who captures upside from artificial intelligence breakthroughs, how resilient capital structures will be when sentiment turns, and whether the path from private valuation to public listing remains orderly or becomes a series of dislocations. In the case of AI champions such as SpaceX, OpenAI, and Anthropic, the sheer scale of capital required, the concentration of economic power, and the opacity of private rounds make the locus of price discovery a critical systemic issue rather than a technical footnote.1
The underlying tension is simple: AI platforms with global reach need tens of billions in compute, data, and talent investment, yet their equity prices are being inferred from relatively thin slices of secondary trading and bespoke private transactions, not from broad and continuous public markets. The question is whether retail and smaller pools of speculative capital can meaningfully set prices in this environment, or whether large institutional investors will remain the primary mechanism through which valuations are negotiated, disciplined, and transmitted into the wider market. For financial intermediaries that underwrite, structure, and distribute such deals, the answer determines both risk management and revenue opportunity.3
From public markets to private price signals
For most of the twentieth century, price discovery for major corporates was anchored in public exchanges, with day-to-day prices shaped by a mix of institutional flows, retail activity, and specialist dealers. The microstructure was noisy, but there was at least a transparent quote for almost every security at almost every moment. Over the past 20 years, however, late-stage venture and growth equity markets have grown to a scale where the most important technology firms can delay public listings for far longer and can raise 5, 10, or even 20 billion in private capital before an initial public offering. That shift has relocated a substantial part of price discovery from open exchanges to private deal rooms.
In those rooms the dominant actors are not day traders but sovereign wealth funds, large asset managers, pension schemes, insurance companies, and major hedge funds. These institutions write cheques measured in billions, not millions. Their internal models, risk committees, and benchmarking processes determine the ranges at which boards are willing to transact. Even when smaller investors participate via feeder funds or structured vehicles, the indicative price is typically set by a handful of large anchors whose willingness to provide capital at a given valuation is decisive.
In principle, public markets might eventually overwrite these private valuations once a listing occurs. In practice, the initial price discovery at IPO is also heavily shaped by institutional demand. Book-building processes rely on indications of interest from a curated list of long-only funds, hedge funds, and other professional investors. Retail orders, even when meaningful in aggregate, usually follow the range signalled by this group. That means the same large pools of capital that dominated late-stage private rounds often remain central in the first liquid price that the market sees.
AI platforms and the capital intensity of scale
The dynamic is amplified in AI. Training frontier models, building global data centres, and integrating AI into industrial and consumer workflows require extraordinary upfront expenditure. Cloud credits and partnership structures can ease the burden, but for independent or semi-independent entities such as OpenAI or Anthropic, the capital stack must still support long-duration research and infrastructure commitments. SpaceX, while a space and launch company rather than a pure AI play, shares a similar profile: massive capex, long development cycles, and complex regulatory risk.
These companies are therefore engaged in a multi-round capital formation process in which each new raise both funds operations and crystallises an implicit valuation step. When a round prices OpenAI or Anthropic at, say, 60 billion or 80 billion, that number is not a market consensus but a negotiated outcome between management and a relatively small syndicate of investors. It may reference comparable multiples from public AI-exposed companies and discounted cash-flow scenarios for long-run monetisation, but the final figure is fundamentally a deal term. Subsequent secondary trades by employees or early backers tend to cluster near that negotiated level, unless a macro shock or major news event forces a repricing.
Because the needs are so large, the marginal buyer that matters in these rounds is almost always institutional. A single sovereign wealth fund or mega-fund can bridge a financing gap of several billions; thousands of smaller players cannot coordinate quickly enough or accept the same information constraints. That concentration of bargaining power means that price discovery is heavily conditioned by the opportunity cost of capital for these institutions, their internal risk constraints, and their forward view on AI adoption curves. Retail enthusiasm in listed AI-adjacent names may inform sentiment, but it rarely changes the cheque sizes or target returns required by the limited partners who ultimately supply the money.
Who John Waldron is, and why his perspective matters
The speaker of the quote occupies a vantage point that is not merely observational but operational.3 As president and chief operating officer of a major global investment bank, John Waldron oversees a platform that arranges, underwrites, and distributes some of the world’s largest equity and debt transactions.3 His firm advises both issuers such as SpaceX and AI start-ups, and the institutional investors that buy into these deals. It also operates trading, prime brokerage, and asset management businesses that interface with hedge funds, pension funds, and other large pools of capital.3
From this position, Waldron is acutely aware of the difference between headline narratives in media or social platforms and the practical conditions under which multi-billion capital commitments are made.2,3 When he emphasises institutional pools of capital as the primary source of price discovery, he is effectively describing the way his own franchise operates: block trades negotiated with a small number of lead accounts; structured IPO allocations built around core anchor investors; and private rounds in which a single cornerstone order can validate or derail a proposed valuation.1,3
His perspective is also informed by risk. As a senior executive, he bears responsibility for firm-wide balance sheet usage, reputational risk, and client outcomes.3,4 Deals that clear at artificially inflated prices may generate short-term fees, but they also raise the probability of painful down-rounds, post-IPO collapses, and strained relationships with both issuers and investors. Insisting that institutional price discovery go well is therefore not only a prediction; it is a condition for the long-term sustainability of the franchise and, more broadly, for market stability.
The mechanics of institutional price discovery
In the background, price discovery by large institutions combines hard modelling, peer comparables, and soft information from networks and management interaction. Analysts will typically build cash-flow projections under multiple scenarios for adoption, margin evolution, and regulatory constraints. Even when the business is early-stage and cash-flow negative, they attach probabilities to different outcome trees, discount them at hurdle rates that reflect perceived risk, and derive a range of plausible enterprise values. The conversation then becomes one of where in that range the issuer and the investor can agree.
For AI companies, the modelling challenge is acute. Revenue visibility may be limited, with usage-based pricing and platform economics that depend on partner ecosystems still being formed. Compute costs can change rapidly as hardware generations evolve. Regulatory shifts on data usage, safety requirements, and liability could materially alter future profitability. As a result, institutional investors often use scenario-based frameworks. They might consider a base case in which enterprise adoption follows a gradual S-curve, a bull case in which AI rapidly unlocks productivity across multiple sectors, and a bear case in which regulatory or technical setbacks slow deployment.
Within such frameworks, what matters most is not a single point estimate but the sensitivity of valuation to key assumptions. An investor may accept a higher headline valuation if given structural protections such as liquidation preferences, anti-dilution provisions, or governance rights that allow for intervention if execution falters. Conversely, a governance-light structure might require a lower entry price to compensate for the inability to influence outcomes. These trade-offs are rarely visible to public observers yet are central to the effective price paid for risk.
Strategic and technological tensions in AI financing
Price discovery in AI deals is not a purely financial exercise; it is entangled with strategic alliances, sovereignty concerns, and technological competition. Large cloud providers that partner with AI firms may participate in equity financing at valuations that reflect strategic value rather than traditional investment metrics. Governments and quasi-sovereign funds may consider national competitiveness in AI when deciding whether to support domestic champions. These considerations can push prices up relative to what a purely financial analysis would suggest.
For intermediaries, this creates a tension between facilitating strategic capital and maintaining valuation discipline. If one investor is willing to pay a premium to secure preferential access to models or infrastructure, others must decide whether to follow at that price or to walk away and risk being structurally underexposed to a transformative technology. In hot markets, fear of missing out can erode discipline; in cooler periods, risk aversion can overshoot in the other direction. Waldron’s focus on institutional pools as the key site of price discovery implicitly recognises that managing this tension requires actors with both the scale and the governance structures to resist the most extreme swings.
There is also a technological feedback loop. High valuations enable AI companies to raise more capital, hire more researchers, and purchase more compute, reinforcing their lead. However, they also set high expectations for growth and profitability that may be difficult to achieve in practice. If institutional investors collectively overestimate the addressable market or underestimate the regulatory drag, a later correction could be sharp. Conversely, if they are too conservative, they risk slowing down innovation or pushing companies to seek funding from less transparent sources.
Debates and objections: can retail and crypto-style markets do more?
Not everyone accepts the premise that institutional capital should dominate price discovery. The experience of meme stocks, cryptoassets, and retail-driven speculative booms has shown that large swarms of small investors, coordinated through digital platforms, can move prices dramatically. Proponents argue that this democratises finance, surfaces diverse information, and challenges potential conflicts of interest among large intermediaries.
However, the suitability of such mechanisms for financing companies like SpaceX, OpenAI, and Anthropic is contested. These businesses require patient capital willing to tolerate long periods of negative free cash flow, complex technical risk, and binary regulatory outcomes. Retail flows are often momentum-driven and may be less tolerant of prolonged volatility or extended lock-ups. Moreover, the disclosures required for public retail ownership may conflict with the competitive need to keep certain technical and strategic information confidential until a more mature stage.
There are also concerns about systemic risk. If retail-driven price discovery sets valuations at levels far above what institutional fundamentals would justify, the eventual correction could erode trust not only in specific names but in AI as an investable theme. This in turn might complicate financing for more grounded, less glamorous AI applications in industry and public services. Institutional gatekeepers, for all their flaws, provide a buffer between sentiment and capital allocation, absorbing information and adjusting positions in a more measured fashion.
Why the success of institutional price discovery matters
When Waldron says that this mechanism has to go well for the deals to work, he is pointing to several layers of dependency. First, the issuers themselves need valuations that are high enough to fund ambitious plans without excessive dilution, but not so high that every future round becomes an exercise in defending a fragile narrative. Second, investors require returns that compensate them for risk, within mandates that are accountable to pensioners, policyholders, and endowments. Third, intermediaries need both sides to feel fairly treated if they are to maintain long-term client relationships and reputational capital.
If institutional price discovery fails – because models prove systematically over-optimistic, governance proves too weak to correct course, or strategic pressures override financial discipline – a wave of disappointing outcomes could follow. Down-rounds in private markets can demoralise employees whose equity is underwater, prompt litigation, and limit future fund-raising options. Public market disappointments, especially after heavily marketed IPOs of AI leaders, can spark political scrutiny, regulatory tightening, and demands for stricter control over both finance and AI development.
Conversely, if it succeeds, the benefits extend beyond the firms directly involved. A credible valuation structure for AI unicorns provides benchmarks against which smaller companies can be priced, informs M&A activity, and helps public investors calibrate multiples for AI-exposed incumbents. It also gives policymakers clearer signals about where private capital sees sustainable value, which can guide decisions on public research funding, infrastructure investment, and education priorities.
The evolving architecture of capital pools
It is also important to recognise that the category of “large institutional pools of capital” is itself evolving. Traditional pension and insurance funds are being joined by sovereign wealth funds from emerging markets, large family offices with institutional-grade capabilities, and specialist AI or technology funds that raise many billions from global limited partners. Some of these actors operate with longer horizons and higher risk tolerance than classic institutions; others are more constrained by domestic politics or public scrutiny.
The result is a more heterogeneous set of views feeding into price discovery. For AI deals, this diversity can be helpful, as it allows issuers and intermediaries to match specific risk-return propositions with compatible investors. Yet it also means that coordination is harder. A single change in regulatory perception or geopolitical alignment can reprice risk for a subset of investors, shifting demand and creating cross-currents in valuations. Managing such complexity is part of why major investment banks emphasise their role as translators between issuer needs and investor constraints.3
In this context, Waldron’s focus on these pools is both descriptive and aspirational. Descriptive, because they are already the primary providers of bulk capital for AI and space-related deals. Aspirational, because the hope is that their processes, governance, and experience will be robust enough to steer valuations through cycles of hype and disappointment, ensuring that transformative technologies are funded at scales commensurate with their potential without triggering destabilising bubbles.
Implications for future IPOs of SpaceX, OpenAI, and Anthropic
When considering eventual IPOs or other liquidity events for SpaceX, OpenAI, and Anthropic, the present configuration of private price discovery sets the starting conditions. If late-stage rounds have been anchored by rigorous institutional assessment, public market investors may see the transition as a natural extension of existing valuation logic. If, instead, valuations have floated far ahead of fundamentals, the listing event may become a de-rating rather than a celebration.
For SpaceX, questions about launch cadence, satellite broadband economics, and defence and intelligence contracts will shape institutional models. For OpenAI and Anthropic, topics such as model commoditisation, cloud revenue-sharing arrangements, and the balance between safety obligations and monetisation will dominate. In each case, the terms on which institutions are willing to finance the next phase of growth – pre-IPO and at IPO – will reflect how they resolve these uncertainties.
The broader market will watch these processes closely, not only because of the companies’ intrinsic importance but because their deals will serve as reference points for a whole generation of AI-related offerings. If the transactions clear smoothly, with robust books and post-listing performance that broadly matches expectations, the template will reinforce the central role of institutional capital in orchestrating innovation financing. If they misfire, pressure will grow for alternative mechanisms of price discovery, whether through more retail participation, more direct state involvement, or new forms of digital asset-based funding.
In that sense, the quality of institutional price discovery for these AI and space leaders is not just a technical matter for bankers and portfolio managers. It is one of the levers through which societies decide how quickly to scale transformative technologies, how to distribute the economic gains they generate, and how to allocate the risks that accompany them. The stakes extend far beyond any single deal.
References
1. “Goldman’s AI Expectations” – https://www.linkedin.com/pulse/goldmans-ai-expectations-sonali-basak-ixg8e
2. Investment strategist outlines the next phase of AI trade – Fox Business – 2025-10-02 – https://www.foxbusiness.com/video/6380886913112
3. Scaling RIA Growth: The Goldman Sachs AI Playbook – ETF Database – 2026-05-13 – https://etfdb.com/future-etfs-content-hub/goldman-sachs-ai-playbook/
4. John E. Waldron – Goldman Sachs – https://www.goldmansachs.com/our-firm/our-people-and-leadership/leadership/executive-officers/john-waldron
5. John E. Waldron – Atlantic Council – 2026-03-18 – https://www.atlanticcouncil.org/expert/john-e-waldron/
