Why We Are Building the Technology Commons Alliance

by Songyee Yoon · July 20, 2026

The technologies that matter most endure because they are built in the open.

Open ecosystems lower the cost of participation. They invite exceptional talent from everywhere. They force quality through transparency, resilience through scrutiny, and trust through the ability to inspect, challenge, and improve.

Linux is the clearest example. Built openly, it became a foundation of the internet and now powers much of the world’s mobile, cloud, and connected infrastructure. It is trusted not because authority declared it trustworthy, but because generations of builders could examine it, test it, strengthen it, and make it better.

This is the tradition of peer review. It is how science advances. It is how technology compounds. It is how humanity turns individual intelligence into collective progress.

We are greater than the sum of our parts when our systems are open to contribution, correction, and accountability. The same principle underlies democracy itself: progress depends on checks and balances, on transparency, and on the belief that no single actor should hold unchecked power over the future. That principle must guide the next era of technology.

AI is developing on a different trajectory.

In prior technology waves, openness helped broaden participation and compound trust. In AI, the opposite pressure is emerging. The foundational inputs — models, data, compute, deployment infrastructure, and distribution — are increasingly concentrated in the hands of a small number of institutions (Figure 1).

That concentration is not accidental. It reflects the economics of the current frontier. Training and deploying the most capable systems requires capital at a scale few organizations can assemble. Compute clusters, energy commitments, data acquisition, security infrastructure, and elite engineering teams now require billions of dollars and years of accumulated advantage. Most startups, universities, civic institutions, and even national governments cannot realistically compete on those terms.

The result is not simply that a few labs are ahead. It is that the cost of meaningful participation is rising.

Trust reinforces the same dynamic. Many of the strongest open models today are developed across a range of jurisdictions — including China, France, the United Arab Emirates, and elsewhere — under regulatory, security, and data-provenance regimes that enterprises and governments may not have the tools to evaluate. For many institutions, the question is not whether open systems are useful. It is whether they can understand the risks well enough to adopt them responsibly.

So they default to closed systems from familiar providers. That choice is rational. But at the system level, it narrows the AI economy faster than it needs to narrow.

This is the problem we have to solve.

If AI is going to become foundational infrastructure — as operating systems, the internet, and cloud computing did before it — then the conditions of participation must broaden. More people need the ability not only to use AI, but to inspect it, adapt it, govern it, and build on top of it with confidence.

I see two practical paths.

First, we need open systems that are trustworthy by design. That means building the technical and institutional trust layer that allows enterprises, governments, researchers, and civil society to adopt open models without taking on risks they cannot measure or price. Openness alone is not enough. Open systems need evaluation, provenance, security, governance, and accountability.

Second, we need to fund the research and deployment work that tests where smaller, specialized, openly governed systems can carry real economic load. Not every problem requires the largest frontier model. In many domains, the winning architecture may be narrower, cheaper, more transparent, and more aligned with the user’s context. But we will not discover that path unless we invest in it deliberately.

The goal is not to slow down frontier AI. It is to make sure the future of AI is not defined only by those who can afford to build at the frontier.

A durable AI ecosystem should have room for frontier labs, open models, universities, startups, governments, and civic institutions. It should allow trust to be earned through inspection, performance, and accountability — not merely inherited from brand, scale, or market power.

That is how we keep AI from becoming a closed utility controlled by a few actors. And that is how we turn AI into infrastructure that compounds for everyone.

1 · Building a trust layer

Enterprises do not hesitate to deploy open systems because they dislike openness. They hesitate because they are accountable.

They need to know what went into training, whether the data was licensed, whether sensitive information was included, and whether the model can be evaluated for the use case at hand. They need to show a regulator, auditor, board, or customer a clear chain of responsibility when something goes wrong. They need confidence that the model, weights, data, dependencies, and deployment stack have not been tampered with. And they need operational commitments: maintenance, documentation, security updates, compliance support, incident response, and some counterparty willing to stand behind the system.

Today, too much of that burden falls on each enterprise to solve alone.

That is why open AI needs a trust layer: the standards, tooling, evaluation systems, provenance records, security practices, and operational infrastructure that make open systems adoptable at institutional scale. Done well, this layer would make openness practical, not just principled. It would allow enterprises and governments to understand risk, document decisions, and deploy open systems with confidence (Figure 2).

But the builders of this layer fall into a structural gap. They are too infrastructural and long-horizon for traditional venture capital, yet too commercial and operationally complex for traditional grants.

That is the first role of the Technology Commons Alliance: to provide capital that is patient enough for infrastructure and disciplined enough for real companies. This is not charity for open source. It is infrastructure finance for a healthier AI economy.

Open AI will matter only if responsible institutions can use it. Trust has to be earned through evidence, documentation, security, and accountability.

2 · A strategic case for open models

The trust layer is one half of what makes open AI viable at scale. The other half is a more basic strategic question: how much capability do enterprises actually need?

Today, much of the industry is organized around the race to general intelligence. That is an important frontier, and the economic prize is enormous. But it has also shaped the entire field — the capital flows, data-center buildout, energy footprint, talent allocation, and public imagination of what counts as ambitious work.

The risk is that we confuse the frontier with the market.

Much of AI’s near-term economic value will come from specific, high-value applications: clinical decision support, legal research, financial analysis, customer operations, software development, industrial automation. In many of these settings, the best system may not be the largest general model. It may be a smaller, specialized model, tuned to the domain, embedded in company-specific context, cheaper to operate, easier to evaluate, and more reliable for the task.

We do not yet know how much of the enterprise stack truly requires frontier-scale models. That is the question we need to test (Figure 3).

Building the open ecosystem around smaller, specialized, openly governed systems is not a rejection of frontier AI. It is a disciplined hypothesis: that much of the real economic load can be carried by systems that are more transparent, more efficient, and more accountable.

That hypothesis is technically credible. It is also strategically overdue.

The Technology Commons Alliance

TCA is organized around the two paths described above: building the trust layer that allows open AI to be adopted seriously, and funding the research that tests how much of the economic load can be carried by smaller, specialized, openly governed systems.

We operate through three pillars (Figure 4).

First, grants. We support early research and field-building work that is too long-horizon, too speculative, or too clearly a public good to attract commercial capital. This includes academic research, open evaluation projects, data commons, safety and governance tooling, and the open-source maintenance work that quietly holds the ecosystem together.

Second, recoverable investments. We invest in companies building the trust layer enterprises need: provenance and supply-chain attestation, evaluation tooling, runtime monitoring, governance documentation, compliance infrastructure, and the operational reliability required to use open systems safely in production. When these investments succeed, the capital returns to TCA and is redeployed into the next generation of work.

Third, community building. The open AI ecosystem is global, multidisciplinary, and still fragmented. We bring together researchers, founders, operators, policymakers, and civic leaders who would not otherwise be in the same room. We also work in close partnership with scholars whose research has shaped how the field thinks about algorithmic equity, information integrity, human potential, and inclusive innovation.

AI is too important to be left only to market forces or regulatory reaction. This period needs deliberate stewardship. Rules and governance matter, but they are not enough. We also need shared infrastructure, trusted standards, and a community of builders committed to making AI more open, accountable, and broadly useful.

That is the work of TCA: to build a technology commons for AI — the institutional, technical, and human infrastructure that all of us can rely on.

— Songyee Yoon