The Future of Computing and Intelligence: 2031, 2036, and an Agentic Society

“Confidence” here is not a numerical probability. It is an editorial evidence class: high for structures already deployed, standardized, or receiving sustained investment; medium when continuation of current trends is required; and low when several unresolved problems must all be crossed.

The fact layer contains deployed systems, standards, measurements, and formal reports. EuroHPC already couples quantum processors to supercomputers and offers access to hybrid workflows in which a QPU handles a specific algorithmic step. That is evidence of implemented collaboration, not evidence that quantum computing has replaced classical computing.

A conditional scenario states its premises: “if current investment and technical trends continue” or “if energy, data, and manufacturing bottlenecks can be relieved.” The IEA’s 2030 electricity numbers are a central projection, not contracted consumption or a physical law.

An editorial perspective interprets which direction the primary evidence makes more plausible. It does not fix an arrival year, product, asset price, employment total, or quantum-attack date. A scenario must change when its evidence changes.

The IEA’s 2026 central projection has global data-centre electricity consumption rising from 485 TWh in 2025 to 950 TWh in 2030, while AI-focused data centres roughly triple their use over that period. The same report identifies HBM, chip manufacturing, grid connections, electrical equipment, and capital as bottlenecks. Five-year competition will therefore concern memory supply, networking, cooling, location, power procurement, and software efficiency—not only peak arithmetic.

This does not mean demand must reach the projection. Efficiency, utilization, the mix of agentic, video, and reasoning workloads, returns on investment, policy, and local acceptance all change together.

The International AI Safety Report 2026 does not rule out slowdown, continuation, or sharp acceleration even through 2030. Evidence for assigning a single capability level to 2036 is weaker still. A ten-year scenario should make it possible to diagnose what the forecast missed.

Our highest-confidence proposition is not convergence on one universal processor but growing difficulty in coordinating heterogeneous systems. Every added computing mode creates more work for compilers, schedulers, data placement, security, provenance, and human approval.

The International AI Safety Report 2026 records leading systems reaching or exceeding human-expert performance on standardized mathematics, coding, and science evaluations, yet describes capability as jagged. A system can solve a hard problem and still fail at simple counting, error recovery in a long workflow, an unfamiliar interface, or a physical environment. Winning a benchmark and reliably completing real work are different claims.

The OECD AI Capability Indicators separate language; social interaction; problem solving; creativity; metacognition and critical thinking; knowledge, learning and memory; vision; manipulation; and robotic intelligence into nine dimensions. The framework asks what a system can do on a task and what remains absent; it does not produce one “AI versus humanity” rank.

METR measured rapid historical growth in the human-equivalent duration of software and research tasks that an AI could complete at a specified success rate. The study also identifies changes in the future trend and external validity as major uncertainties. Its historical doubling pattern must not be converted into a date for AGI by simply extending a line to 2031 or 2036.

Even as machines exceed human performance on more tasks, a benchmark cannot decide what to optimize, whose interests count, or which risks are acceptable. Speed of answering is separate from legitimacy of purpose, reconciliation of rights, and responsibility after harm.

The NIST AI RMF calls for defined roles in human-AI configurations, human oversight, and executive accountability. This does not mean that a person must approve every output line by line. It means designing authority limits, stopping conditions, audits, appeals, independent evaluation, and named responsibility in proportion to risk.

The ILO’s 2025 analysis places roughly one in four workers worldwide in occupations with some generative-AI exposure, while concluding that continued human input makes task transformation more likely than wholesale occupational redundancy. Human work may shift from beating AI at every calculation toward choosing worthy problems, interrogating evidence, and accepting responsibility for outcomes. That final sentence is our interpretation.

An “AI agent” is a software system configured by an operator with goals, tools, credentials, and a budget. A protocol does not make it a legal person or owner of an asset. Possessing a key, being technically able to pay, and having contractual authority are not the same condition.

A2A standardizes capability discovery and task collaboration among different agents; MCP standardizes connections between AI applications, data, and tools. Communication does not establish trust, and identity does not prove that a result is correct. Separating the layers reveals where a blockchain is useful and where it is unnecessary.

The Bitcoin whitepaper proposed peer-to-peer electronic cash for sending online payments without a financial institution and a public history that prevents double spending. It did not describe AI agents, model training, or machine identity as its purpose. “Blockchain was originally a technology for AI” is therefore not a historical fact.

Technology can acquire uses that differ from its origin. If software agents cross corporate or national boundaries to transfer assets and share signed records without one common administrator, a permissionless ledger or smart contract can be an option. That supports “maybe for selected future uses,” not “required by all AI.”

The x402 Foundation protocol defines a flow in which an HTTP 402 response and signed payment payload let a resource server verify and settle payment before returning a resource. It maps naturally to an agent buying an API call, but the specification is network-, token-, and currency-agnostic and anticipates both on-chain and fiat schemes. It is not evidence that agent payments can exist only on blockchains.

ERC-8004 remains a Draft as of 24 August 2026. It proposes identity, reputation, and validation registries for discovering agents across organizational boundaries, adding shared trust signals outside MCP and A2A. The document itself warns about Sybil attacks and states that cryptography cannot guarantee that a registered capability works or is benign. It must not be described as an adopted, deployed Ethereum-wide standard.

Payments are explicitly orthogonal to ERC-8004. Combining identity, reputation, validation, and payment into one generic “AI blockchain function” hides which component is draft, whom it trusts, and what it verifies.

As Ethereum’s official documentation explains, a smart contract cannot access off-chain information by default; an oracle imports external data. Multiple oracles and signatures can reduce tampering and single points of failure, but source selection, false reports, and dependence on the same mistaken source remain. Consensus does not manufacture real-world truth.

Putting a hash of an AI answer on-chain can prove that the recorded version was not later changed. It does not prove the answer correct, establish rights to its training data, or show that a decision was non-discriminatory. Provenance and truth must remain separate.

W3C DIDs define a data model for resolving an identifier to verification methods and services. A verifiable data registry can be implemented with distributed ledgers, databases, decentralized file systems, peer-to-peer networks, and other systems. Verifiable Credentials carry tamper-evident credentials among issuers, holders, and verifiers. “Some implementations use a blockchain” is different from “the standards require one.”

C2PA 2.4 binds content to hashes, signed claims, credentials, and provenance. The specification draws a boundary: it validates association and tamper evidence rather than judging whether a claim is good or bad. It demonstrates a signature- and trust-list path for AI-content provenance that does not require a blockchain.

Within one organization, an ordinary database, append-only log, PKI, OAuth, hardware security module, and accounting controls may provide better privacy, revocation, cost, and accountable ownership. The reason to select a shared ledger is not that a participant is AI, but that multiple parties need common state without delegating it to one administrator.

Automated payment should not connect a model output directly to an unlimited key. Cryptography accepts a formally valid signature even when the key was stolen or its authority was excessive. AI safety and wallet security are distinct problems; a policy engine and settlement layer should remain separable.

This section is our interpretation. We expect 2030s computing to divide work among people, AI models, agents, CPUs, accelerators, QPUs, private systems, and public networks rather than converge on one superintelligence or one chip. Scarce inputs will include not only computation but trustworthy data, energy, verification time, and institutions willing to carry responsibility.

As AI explores more broadly and quickly than people, the central human task may move from calculating every answer toward choosing worthy questions, setting boundaries, including minority rights, and stopping and remedying harmful deployments. A capability gap does not automatically determine a relation of rule; deployment and institutional choices determine the human position.

Blockchain was not born for AI. In an era when software actors transact across human organizational boundaries, however, it may become a narrow public rail for sharing who signed what, under which rules value moved, and what can be audited. That role becomes clear only when a ledger is not called a “truth machine” and communication, identity, payment, provenance, and real-world accountability remain distinct.