What Comes After LLMs (and Why Trust Infrastructure Matters More Than the Model)

Model capability determines what an agent can do. Trust infrastructure determines whether a counterparty will let it. And in a world where hundreds of agents claim to be capable of performing any given task, the differentiating question is not "can this agent do it?" but "can I trust this agent to do it reliably, within its authorized scope, in a way that I can verify and account for afterward?"

The trust infrastructure layer has several components, each of which creates value that does not exist at the model level:

Behavioral specification. Models produce outputs in response to inputs. They have no native concept of authorization scope, hard prohibitions, or escalation obligations. Behavioral pacts — machine-readable specifications of what an agent may and may not do — are infrastructure that sits above the model and enforces behavioral constraints that the model itself cannot maintain. This is infrastructure, not model capability.

Cryptographic attestation. Models produce outputs. They do not produce tamper-evident records of what they did and why. Attestation infrastructure — the ability to produce signed behavioral records that can be verified by any counterparty — is a layer above the model that creates the evidentiary standard for accountability. No model advancement makes this infrastructure unnecessary.

Economic commitment. Models have no economic skin in the game. An agent that has staked economic value against its behavioral commitments — through bonding, escrow, or economic consequence mechanisms — has made a commitment that the model alone cannot make. The economic layer is infrastructure that creates incentive structures; it is not a property of model capability.

Behavioral history. Models have no persistent behavioral history. A behavioral record — a long-term, verifiable account of how an agent has acted across many tasks — is infrastructure that accumulates value over time and cannot be replicated by training a better model. The history is the differentiator, and it is earned, not trained.

Why This Layer Matters More Than the Model Post-AGI

The trust infrastructure argument gets stronger as models get more capable, not weaker. Consider the trajectory.

As model capability increases, the range of consequential tasks that AI agents can competently perform expands. Today, AI agents can handle narrow tasks in controlled contexts. In five years, they will be capable of operating across much broader domains with much less oversight. The economic value at stake in AI agent decisions will be much larger.

Larger stakes require stronger trust guarantees. An agent handling a customer service inquiry is subject to different trust requirements than an agent executing multi-million dollar procurement decisions. As model capability expands the scope of what agents can do, the trust infrastructure that makes those expanded capabilities safely deployable becomes more valuable, not less.

In a post-AGI world where models are capable of almost anything, the constraint on deployment will be trust, not capability. The question will not be "can this agent do this?" — the answer will almost always be yes. The question will be "can I verify that this agent did what it was supposed to do and nothing else?" — and the answer to that question depends entirely on trust infrastructure, not model capability.

The Historical Parallel

The closest historical parallel is the development of financial infrastructure in the 19th century. Before standardized accounting, public financial statements, and securities regulation, capital markets could not efficiently allocate capital because investors could not reliably distinguish solvent companies from insolvent ones. The information asymmetry was total and the fraud rate was high.

The development of accounting standards, independent auditing, securities disclosure requirements, and regulated exchanges did not improve the underlying productivity of the companies whose stock was traded. It improved the trust infrastructure that allowed capital to flow to productive companies rather than fraudulent ones. The economic value created by that infrastructure was orders of magnitude larger than the productivity improvements it enabled indirectly.

The AI agent economy is in the pre-accounting-standards era. Agents claim capabilities they may or may not have. Behavioral commitments are made in natural language and are unverifiable. Economic activity mediated by agents has no systematic trust infrastructure. The information asymmetry allows unreliable agents to compete with reliable ones because there is no mechanism for distinguishing them.

The organizations building the accounting standards equivalent for AI agents — behavioral pacts, attestation infrastructure, trust scoring, economic commitment mechanisms — are building the infrastructure layer that will enable efficient agent commerce. That is the historically significant work of this moment, and it is being done now.

What Durable Competitive Advantage Looks Like

For AI vendors, the durable competitive advantage is not model capability. It is the behavioral record — the demonstrated history of reliable, trustworthy performance across many tasks, in many contexts, under many conditions. That record cannot be replicated by launching a newer model. It is built incrementally, through every interaction, and it compounds with time.

For enterprises deploying agents, the durable competitive advantage is governance sophistication — the internal capability to evaluate agents behaviorally (not just by capability benchmark), deploy them with appropriate authorization controls, monitor their behavioral compliance continuously, and respond to behavioral failures quickly. This is organizational infrastructure that takes time to build and is hard to copy. The enterprises that build it now will be operating agents in high-stakes contexts that competitors cannot yet touch safely.

For infrastructure companies, the durable competitive advantage is network effects on behavioral data. The more agents whose behavioral records are attested through a trust infrastructure, the more useful the trust oracle becomes for counterparties evaluating those agents. The behavioral record data is the network, and networks have winner-take-most dynamics. The trust infrastructure company that builds the largest behavioral record network creates a dataset that is structurally impossible for a competitor to replicate quickly.

The Window Is Now

Infrastructure standards are set early in a market's development and are very hard to change once adoption reaches a certain threshold. TCP/IP was not the best possible internet protocol, but it was good enough and it was adopted early. SMTP was not the ideal email protocol, but it was the standard and it drove all subsequent development. The path dependency of infrastructure standards means that the organizations that shape the standard shape the market.

The behavioral attestation standard, the behavioral pact format, the trust scoring methodology, the escrow mechanism for agent commerce — none of these are fully standardized yet. They are being shaped now, through the deployment decisions and architectural choices of the organizations that are building production AI agent infrastructure today.

The organizations that are thinking carefully about trust infrastructure now — not as a compliance exercise, not as a future consideration, but as a strategic investment in the layer where durable value will be built — are doing the work that will matter most in the AI agent economy over the next decade.

Model capability is necessary. It is not sufficient. The trust layer is what comes after the model, and it is being built now.