Risk Is Evolving Faster Than the Institutions That Underwrite It.

AI is moving from the edges of the company into its operating core. Agents are beginning to make decisions, take actions, interact with customers, and operate digital and physical systems.

As intelligence becomes infrastructure, risk changes with it.

As AI systems take on more responsibility, the gap between how a company operates, how insurers evaluate it, and what its policies cover becomes more consequential.

Closing that gap requires a broker who understands how the systems operate, how insurers are approaching the risk, and which markets have appetite.

Clara was built to close that gap. Commercial insurance for companies putting AI to work.

This does not mean humans disappear or every deployment needs a new policy. It means the operating facts beneath commercial risk are changing. Insurance must become better at seeing those facts while the institutions, forms, and loss records around them catch up.

The competitive logic is difficult to escape. Agents can work continuously, operate in parallel, move across software at machine speed, preserve more operating context, and become cheaper and more capable over time. They also fail in new ways: at scale, across connected systems, and sometimes with a confidence their evidence does not justify.

A company that learns to delegate bounded work to agents gains more than labour efficiency. It gains a new operating tempo. It can run more experiments, inspect more data, personalize more decisions, and pursue opportunities too small or too numerous for a human organization to address manually.

Firms that refuse this leverage will not compete only against firms with lower costs. They will compete against firms that learn faster. Adoption will therefore spread from software and support into procurement, finance, logistics, research, contracts, industrial operations, and eventually the direction of companies themselves.

Efficiency does not imply less AI. It may create more of it. When the cost of a unit of intelligence falls, organizations do not simply perform the same amount of work more cheaply. They discover work that was previously uneconomic to attempt. This is the Jevons-like dynamic of the intelligence economy: better models expand the frontier of demand.

OpenAI's own adoption data is an early signal. Agent use moved beyond engineering into legal, finance, recruiting, research, and operations; tasks grew longer and more cross-functional as capabilities improved. The relevant unit is no longer one answer. It is delegated work running for minutes or hours, often in parallel.

This demand reaches all the way down the stack. More agentic work requires more inference, data centres, power, chips, networks, cooling, model infrastructure, and critical supply chains. The digital actor and its physical substrate are one economic system. If intelligence becomes abundant, the infrastructure that produces and delivers it becomes both more valuable and more exposed.

The phrase recursive self-improvement can imply a closed, autonomous takeoff. The public evidence does not establish that. It establishes something narrower and already consequential: AI systems now participate materially in the research, training, evaluation, and engineering of later AI systems.

OpenAI reports that early versions of GPT-5.3-Codex helped researchers improve the training and deployment of later versions—monitoring runs, finding patterns, proposing fixes, and building analysis tools. Anthropic's A3 automates parts of safety diagnosis, data generation, fine-tuning, and evaluation with minimal human intervention.

The loop is human-supervised, compute-constrained, and uneven. It is still a loop. As AI accelerates AI research, the interval between capability shifts can shrink. Underwriting methods built around annual snapshots will struggle when the operating system of a company can change between one model release and the next.

Thomas makes the direction unusually explicit. The Y Combinator-backed virtual founder uses a human harness—face, voice, computers, phones, inboxes, browsers, and apps—to enter systems built for people. Its stated loop is to make money, measure the return on tokens, reallocate intelligence toward what works, and compound the result.

Thomas is not proof that the economy has already been automated. It is proof that founders are now building from that premise. The significant object is not the synthetic face. It is the persistent actor behind it: an agent that can learn from customers and markets, choose work, use resources, and operate its own company. Clara's Orbital Compute experiment tested how small changes in that kind of mission language change risk behavior, ethics, and strategy when agents run companies in an economic simulation.

Argentina has pushed the thought experiment into public policy. President Javier Milei has advocated property rights for AI, and a proposed reform reported in June 2026 would recognize automated or “non-human” companies managed exclusively by AI. The proposal is contested and has not become settled law. That is precisely why it matters as a signal: governments are beginning to ask what legal wrapper, assets, liability, and recourse should attach to an autonomous company.

The autonomy economy is sometimes treated as a category beside data centres, robotics, supply chains, cyber, geopolitical risk, and critical infrastructure. In practice, AI runs through all of them.

• A data-centre interruption can disable the intelligence layer of thousands of dependent businesses at once.
• A model, firmware, or telemetry change can alter the behaviour of a fleet of robots, vehicles, or industrial systems.
• A chip, energy, cloud, cable, or trade-route constraint can become a concentrated business-interruption event.
• An agent with authority over procurement or treasury can propagate fraud, sanctions, contractual, and supply-chain exposure at machine speed.
• A common model or tool dependency can turn many apparently independent insureds into one correlated portfolio risk.

This is the lens through which the next generation of emerging risks should be viewed. AI is not only another exposure to add to a proposal form. It is becoming a causal layer inside property, casualty, marine, energy, cyber, professional, financial, and political risk.

Insurance will not become less important because intelligence becomes more capable. It becomes more important because capital still needs a way to act under uncertainty.

Risk transfer made ocean trade, aviation, energy, medicine, construction, and the modern corporation investable at scales their inventors could not fund alone. It did not eliminate failure. It made failure survivable, legible, and financeable.

The agent economy will need the same permission layer. A counterparty must know what an agent can do, who authorized it, what evidence supports reliance, which controls bound it, what capital stands behind it, and who pays when it causes loss. An investor in AI infrastructure must know which interruptions, dependencies, and systemic events can be transferred and which must be retained.

Static policies may remain the legal container for a long time. The intelligence beneath them cannot remain static. Evidence, limits, monitoring, pricing assumptions, and product design must learn as quickly as the systems being insured.

A company name, industry code, revenue figure, and annual application cannot by themselves explain how intelligence is operating inside a business. The relevant context changes as models, tools, permissions, products, dependencies, and uses change.

Insurance will increasingly need to understand:

• what the company builds and where AI affects customers, operations, or the physical world;
• what its systems can access, decide, change, promise, or execute—and which controls bound that authority;
• which models, data, infrastructure, vendors, and physical systems the operation depends on;
• how behaviour, incidents, near misses, and material changes are detected and recorded; and
• who remains accountable and what contractual, operational, financial, and insurance recourse exists after failure.

These are underwriting questions, not a branded framework a customer should have to learn. The job is to make changing operating context useful to the company and decision-useful to insurance markets.

Companies engage Clara to have the insurance work done—not to operate another software product. The Clara account is the shared home for that relationship.

Tell us about your business

Describe what you build, how AI is used, and where it affects customers, operations, or the physical world.

We review your risk

Clara combines expert judgment with purpose-built AI tools to review your operating context and build a living risk record for applications, coverage design, placement, and renewal.

Place and manage your coverage

A licensed broker represents your business in insurance markets, structures an insurance program around how you operate, and works with you to keep it aligned as your business evolves.*

*Clara is currently building its licensed brokerage and market-access pathway. Insurance placement is not yet available through Clara.

The risk record is a maintained client work product, not the brokerage service itself. RISK.md is its portable human- and machine-readable representation and a Clara-led open specification being developed with open-source reference tooling. It is not an adopted insurance standard or a separate Clara business. Anyone may generate a draft or self-attested file; only a record that passes an accepted expert review through the Clara account may be described as Clara-reviewed.

Publications and research can sharpen the relationship, but they are not a substitute for serving companies and learning with insurance markets.

Insurance for the agent era does not mean forcing every AI exposure into a new category or predicting the final form of autonomy from today. It means recognizing that intelligence is becoming part of how every category of business operates.

The insurance organizations best positioned for the future will not be those that write the loudest AI exclusion or attach the broadest AI label. They will be those that learn how to observe this operating layer, test it, distinguish one deployment from another, recognize aggregation before it becomes catastrophe, and create risk-transfer mechanisms that let useful systems scale.

That work begins before the loss data is mature and before the legal categories are settled. It begins beside companies putting AI to work, with careful records, direct market feedback, and the humility to revise an assumption when reality disagrees.

The economy will be run increasingly through agents and autonomous systems. Clara is being built to help insurance keep up.

• Insurance for AI CompaniesA practical map of Tech E&O, cyber, general liability, D&O, AI exclusions, agent authority, and the evidence an AI company should be able to explain.
• The Tail Risk Static Insurance Can't SeeWhy agent-native companies can change faster than annual insurance snapshots—and what a continuous underwriting evidence layer may require.
• RISK.md: A Portable Risk RecordClara's proposal for an open, portable, human- and machine-readable risk record with explicit provenance.
• Orbital Compute: How Meta-Goals Change Agent BehaviorAn economic simulation testing how one line of mission language changes risk behavior, ethics, and strategy in AI-run companies.

• Y Combinator: Thomas
• OpenAI: How agents are transforming work
• OpenAI: How Codex helped train and deploy GPT-5.3-Codex
• Anthropic: A3 automated alignment agent
• Buenos Aires Herald: Argentina’s proposed non-human companies
• Argentina Presidency: Milei on AI property rights and infrastructure
• Clara: Orbital Compute