AI is expanding what individuals and businesses can accomplish. Alongside these productivity gains, an extraordinary investment boom is unfolding across the AI value chain, from data centres and chips to foundation models and startups.
These equity investments allow founders and investors to share in the economic surplus generated by AI-driven productivity gains.
As an AI agent hiring marketplace, OpenMercury introduced transaction capital support in August for AI-native startups and individual service providers. Deployed through CapitalX to support our BaoX and DaiX programs, it complements equity funding without taking an ownership stake (non-equity-dilutive). Our goal is to support high-value uses of AI and help founders and early investors get more growth from the capital already invested.
This article covers the following insights behind our program:
- Productivity needs high-value use to convert into investment returns.
- Growth capital shifts from scaling headcount to building capability.
- Smaller teams can build more businesses, but most fall outside institutional investment thresholds.
- Higher capacity in smaller teams can increase delivery risk for clients, delaying purchases and making higher prices harder to justify.
- Pay-for-result contracts help small AI-native service providers build client trust, but add risk for their equity investors.
- Transaction capital bridges this gap for clients and providers, supporting founders and investors on both sides.
What happened when productivity surged in the past?
Productivity growth is not the same as investment return. Take U.S. corn production between 1950 and 2000 as an example. Average yields increased from 38.2 to 136.9 bushels/acre, approximately 3.6x the original yield. Yet the average corn price received by farmers increased only from $1.52 to $1.85 per bushel.
Over the same period, U.S. consumer prices rose to 7.15x their original level. Adjusted for inflation, the gross value of the U.S. corn crop fell 39% in real terms, despite the substantial increase in physical output. The resulting return on assets (ROA) from the Corn Belt, a major agricultural region in the U.S. Midwest, was 3.13% versus 10.5% for the S&P 500.
High-value use turns surplus into returns. Ethanol gave corn a new market. Higher energy prices and expanding biofuel production boosted demand, helping corn prices rise from under $2.00 to $5.18/bushel between 2005 and 2010.
Returns of your AI equity investment
For an investor, or a founder thinking about the value of their stake, the relationship can be expressed as:
MOIC (Multiple on Invested Capital) measures the value of your stake as a multiple of what you invested. A 3x MOIC means a $100,000 investment is worth $300,000. The formula says the return depends on three things:
AI changes what equity capital builds
Growth capital has historically supported both revenue growth and higher valuation multiples. This matters. Growing revenue often required more employees. Building expertise, relationships and delivery capacity raised barriers to entry, creating a moat that made revenue more defensible and supported higher valuation multiples. Growth in both ratios could outweigh the dilution. The table below shows how revenue and headcount grew together before AI.
| Sector | Revenue growth | Headcount growth |
|---|---|---|
| All surveyed organisations | 10.6% | 9.1% |
| IT consulting | 12.4% | 12.2% |
| Management consulting | 9.1% | 7.3% |
| SaaS professional-services teams | 13.2% | 12.0% |
AI weakens the link between headcount and revenue. AI agents can perform workflows previously handled by people. We are seeing revenue growing faster than headcount across companies of different sizes:
| Sample | Revenue growth | Headcount growth |
|---|---|---|
| 59 early-stage B2B software companies in Haatch’s portfolio | ~50% | 0% |
| Microsoft, Alphabet, Amazon and Meta | 22.5% | 1.7% |
Equally, competitors can now access the same capabilities without investing as much capital in hiring and training. This weakens the moat that once supported the flywheel of revenue growth and higher valuation multiples.
For many sectors, lowering the barriers can translate into lower valuation multiples, and it is happening. Between late October 2025 and early February 2026, the median EV/NTM revenue multiple for cloud software fell from 5.1x to 3.6x, its lowest level in more than a decade. The decline is attributed to fears of AI agents’ potential displacement of established software.
The use of equity capital shifts from scaling headcount to building capability. In 2021, the average SaaS startup needed 20–25 employees to reach $1M ARR, while in 2025, this number fell to 6–8 employees. Besides employee numbers, the nature of the work changes.
Ever-increasing AI capacity keeps raising the bar for what can command higher prices. From January 2024 to April 2026, the quality-adjusted language-model price index fell nearly 80%. Companies are forced to build defences by continually adapting their expertise and research capability to the pace of AI.
The change in capital use makes traditional valuation and forecasting frameworks less reliable. Longer-term investment decisions become substantially more difficult, increasing investors’ preference for later-stage companies with more observable operating data and proven commercial traction. Nevertheless, companies do need capital support to navigate the changing technology.
More economic activity falls outside institutional investment
This is a fact, and is getting worse with AI. Below is the share of U.S. VC dollars going into $100M+ mega-rounds in recent years.
This reflects what passes the threshold for good return potential under traditional institutional forecasting. But it does not imply that smaller businesses are economically insignificant. At OpenMercury, we see this as a significant investment opportunity.
Smaller teams can build businesses requiring less equity capital. From 2021 to February 2026, the median headcount of seed-stage companies fell by ~50%, and by ~35% for Series A companies. As the trend continues, their funding needs may increasingly fall below the practical investment sizes of VC and institutional funds. Meanwhile, we expect growing transaction volumes to increase demand for upfront delivery funding, which need not come from equity raising.
Clients are accepting innovations and spreading across more providers. Across 70,000+ businesses, 52% were paying for OpenAI and Anthropic by summer 2026, one year earlier, this number was 8%. On OpenRouter, Anthropic handled more than 60% of programming-related spend for most of 2025. While in September 2026, top model only takes 15.7% with 7 separate models each hold more than 5% share.
As more providers compete for customer spending, individual businesses may offer smaller exit opportunities than institutional investors require. Nevertheless, their services can still generate meaningful transactions worth financing.
Converting capacity into revenue requires trust
While companies race against AI for innovations, the perceived technology uncertainty is creating trust gaps. It is structural.
Higher capacity in smaller teams can put more delivery risk on the buyer. As small AI-native teams take on more and larger outcomes, buyers face greater exposure to delivery and counterparty risk. In a 2026 survey, 46% of planned AI investment was paused, on average, due to trust concerns. Lack of trust can delay purchases and make higher prices harder to justify. In a healthcare scenario experiment with 248 U.S. adults, participants choosing AI eye screening expressed a preference for human reconfirmation, particularly after abnormal results. In many cases, clients are purchasing for a peace of mind over technology excitement.
Pay-for-result contracting helps turn capacity into commitment. Buyers pay for agreed results rather than effort or promised capability, reducing their exposure to uncertainty around new technology. 2026 research showed that 76% of enterprise customer-experience clients surveyed and interviewed were open to outcome-based or gainshare pricing, yet only 5% had adopted it. Clear deliverables, measurable outcomes and agreed acceptance criteria give both sides a basis for committing. OpenMercury has made such contract as the default agent commercial agreement.
This may add needs for risk capital. Under pay-for-result contract, providers must fund compute, people and execution before receiving payment, while absorbing the cost if they fail to deliver. It implicitly adds barrier to entry based on risk capital rather than capacity. Smaller AI-native teams may have less financial capacity to absorb such uncertainties.
But performance-based contract adds additional uncertainty to cashflow projection. Established investors usually would not favour this. Across industries, there’s little evidence to support performance-based compensation may help boost performance, excpept only for ranking. For example, in 2025, 79% of active US large-cap equity funds underperformed the S&P 500. Another example is UK government’s outcomes-based projects. Among 14 completed projects, the median net capital-return multiple was 1.04x. Therefore funding such contracts require support outside traditional venture investing.
OpenMercury therefore introduced transaction capital and user support programs, helping smaller teams hire or deliver higher-quality services while sharing the risk.
Fund delivery without giving away more equity
OpenMercury seeks to offer complementary support for the founding team and their early investors, such that they can benefit more from the pay-for-result contracting.
Our buyer protection program, BaoX, gives eligible clients deal-specific protections against failed delivery. This is to sharing the risk of hiring someone after only a few exchanges which the client’s time and opportunity at risk, even when payment depends on results. On one hand, our discretionary compensation may support buyers delegating higher-value task externally, also our protection quote may help users to plan objective against candidates pools. Helping clients navigate towards the outcomes they need.
Our flexible fee structure, DaiX, gives eligible providers the option to receive model credits to execute a contract. There is no liability attached. As more work can be performed through AI agents, we expect this scheme to elevate some burdens from the smaller team with upfront execution cost, as they start to take on more transactions.
On the transaction legality, we will continue to work with our providers to enhance our platform compliance support, ComplianceX. We are continuing adding support for more jurisdictions and service categories, so that your capital can be more concentrated on building capacity.
Learn more about the RenX marketplace and explore our products.
Important information. This article provides general information about OpenMercury’s services and economic views. It is not a recommendation to buy or sell any investment and does not take account of your individual financial circumstances. Investment returns are not guaranteed. BaoX, DaiX, and CapitalX remain subject to their applicable terms, eligibility requirements, limitations, and availability.
