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

URL: https://bitcoin.ne.jp/en/learn/computing-futures
URL role: Self-canonical for this English representation
Japanese counterpart URL: https://bitcoin.ne.jp/learn/computing-futures
Slug: computing-futures
Language: en
First published: 2026-08-24
Last updated: 2026-09-03
Last verified: 2026-08-24
Description: Five- and ten-year computing scenarios with labeled confidence, why AI capability is task-specific while responsibility stays human, and what blockchain, identity, payments, and audit can and cannot do for AI agents.

No single product roadmap decides the future. Computing infrastructure that ties together CPUs, accelerators, memory, networks, and power, AI whose capabilities are uneven, and institutions built for software agents that work across organizations all constrain one another. This article keeps three things apart: the evidence available on 24 August 2026, conditional scenarios, and our own interpretation. It treats 2031 and 2036 as revisable hypotheses, not prophecy.

Reuse terms: https://bitcoin.ne.jp/licenses#content-reuse
Third-party sources retain their own rights. Cite the canonical HTML URL or its section fragment.
Sources below are article-level references; they do not establish support for every sentence. Check the original source and its date before quoting time-sensitive claims.
This is educational material, not investment or legal advice. Dates describe the editorial text; this representation excludes browser tools and live market/network values.

### A 30-second entry

The paths to 2031 and 2036 are not one straight line. They branch on which of AI, power, memory, quantum technology, and institutions moves first, and which one becomes the binding constraint.

Mental model: Read a forecast as a branching map whose route changes with the conditions, not as a timetable with one promised arrival.
Where the analogy stops: Scenarios are not prophecies. Task-level AI capability is not the whole of human wisdom, and a blockchain does not automatically prove real-world truth or the correctness of an AI output.
Reading journey: [2031 meets physical limits](https://bitcoin.ne.jp/en/learn/computing-futures#five-years-2031) → [Define the human position](https://bitcoin.ne.jp/en/learn/computing-futures#human-position) → [Find the ledger boundary](https://bitcoin.ne.jp/en/learn/computing-futures#proof-boundary)
What you will understand: You will separate AI capability from purpose and responsibility, then decide where a blockchain helps an agent economy and where it does not.

### The 30-second view: what is established and what is not

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#key-facts

| Question | What the evidence supports in 2026 |

| --- | --- |

| Computing in five years | Optimization is already shifting toward systems that combine heterogeneous processors, memory, interconnect, and power |

| Computing in ten years | Collaboration among AI, HPC, and quantum is already visible, but capability, cost, and the timing of adoption remain deeply uncertain |

| Will AI surpass humanity? | On some well-defined tasks it already matches or exceeds human experts. The evidence does not support a single ranking for intelligence or wisdom as a whole |

| What remains a human role? | Setting goals, granting authority, deciding risk tolerance, weighing rights, carrying accountability, and providing routes for appeal |

| Was blockchain made for AI? | As a claim about its origin, no. As a future role in selected identity, settlement, and audit functions, possibly |

“Confidence” here is not a numerical probability. It is an editorial class of evidence: high for structures already deployed, standardized, or drawing sustained investment; medium when it depends on current trends continuing; and low when several unsolved problems all have to be cleared.

[Figure: computing-futures-scenarios — The future is not one deterministic timeline. This stack separates current observations, higher-confidence directions around 2031, branching scenarios around 2036, human responsibility, and the limited roles blockchain might play. The dates are review horizons, not prophecy deadlines.]

### Keep facts, conditional scenarios, and editorial judgment apart

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#evidence-layers

The fact layer holds 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 one specific algorithmic step. That shows collaboration working in practice; it does not show that quantum computing has replaced classical computing.

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

An editorial perspective reads which direction the primary evidence makes more plausible. It does not fix an arrival year, a product, an asset price, an employment total, or a date for a quantum attack. When the evidence changes, the scenario has to change with it.

### Around 2031: heterogeneous systems meet physical constraints

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#five-years-2031

| Outlook | Confidence | Evidence and condition |

| --- | --- | --- |

| Workloads combine CPUs, GPUs, NPUs, ASICs, HBM, and networks | High | DOE programs already target specialized and heterogeneous hardware, AI memory, and integration of HPC, QIS, and AI |

| Agents handle longer, well-specified digital workflows | Medium | Task horizons are growing, but reliability and recovery fall off as work gets longer and environments get real |

| QPUs act as co-processors at some HPC facilities | Medium | Hybrid access exists, but advantage, error, cost, and utilization all remain application-specific |

| Many complete occupations run without human oversight | Low | The ILO points to tasks being transformed rather than occupations disappearing wholesale, and current agents cannot handle the complexity of an entire job |

The IEA’s 2026 central projection has global data-center electricity consumption rising from 485 TWh in 2025 to 950 TWh in 2030, with AI-focused data centers roughly tripling their use over the same period. The same report names HBM, chip manufacturing, grid connections, electrical equipment, and capital as bottlenecks. Competition over the next five years will therefore turn on memory supply, networking, cooling, siting, power procurement, and software efficiency, not on peak arithmetic alone.

None of this means demand has to reach the projection. Efficiency, utilization, the mix of agentic, video, and reasoning workloads, returns on investment, policy, and local acceptance all move together.

### Around 2036: three scenarios are more honest than one forecast

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#ten-years-2036

| Scenario | Conditions | Signals to watch |

| --- | --- | --- |

| Constraints set the pace | Energy, memory, manufacturing, data, and reliability improve more slowly than demand | Delayed facilities, utilization prices, agent failure rates, and grid queues |

| Collaborative evolution | Small specialist models and large models are combined with CPUs, accelerators, HPC, and narrow QPUs, task by task | Whole-system benchmarks, hybrid schedulers, and reproducible scientific results |

| Automation accelerates | AI materially speeds up AI research and system design while long-horizon reliability improves | Independently evaluated project completion, external validity, and auditable deployments |

The International AI Safety Report 2026 leaves slowdown, continuation, and sharp acceleration all open even through 2030. The evidence for pinning a single capability level to 2036 is weaker still. A ten-year scenario earns its place by making it possible to diagnose, later, what the forecast missed.

The proposition we hold with the most confidence is not that everything converges on one universal processor, but that coordinating heterogeneous systems keeps getting harder. Each additional mode of computing adds work for compilers, schedulers, data placement, security, provenance, and human approval.

### “Beyond humanity” is not a single score

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#capability-vector

The International AI Safety Report 2026 records leading systems matching or exceeding human experts on standardized mathematics, coding, and science evaluations, while describing capability as jagged. A system can solve a hard problem and still fail at simple counting, at recovering from an error midway through a long workflow, at an unfamiliar interface, or in a physical environment. Winning a benchmark and reliably finishing real work are different claims.

The OECD AI Capability Indicators split capability into nine dimensions: language; social interaction; problem solving; creativity; metacognition and critical thinking; knowledge, learning and memory; vision; manipulation; and robotic intelligence. The framework asks what a system can do on a given task and what is still missing; it does not produce a single “AI versus humanity” rank.

METR measured fast historical growth in the human-equivalent duration of software and research tasks an AI could complete at a stated success rate. The same study names shifts in the future trend, and external validity, as major uncertainties. Its historical doubling pattern must not be turned into a date for AGI by extending a line to 2031 or 2036.

### Capability can shift without purpose or responsibility shifting with it

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#human-position

Even as machines beat human performance on more tasks, a benchmark cannot decide what to optimize for, whose interests count, or which risks are acceptable. How fast an answer arrives is a separate question from whether the purpose is legitimate, how competing rights are reconciled, and who is responsible after harm.

The NIST AI RMF asks for defined roles in human-AI configurations, human oversight, and accountability at the executive level. That does not mean a person has to approve every output line by line. It means designing limits on authority, stopping conditions, audits, appeals, independent evaluation, and named responsibility in proportion to the risk.

The ILO’s 2025 analysis places roughly one in four workers worldwide in occupations with some generative-AI exposure, while concluding that because human input is still needed, tasks are more likely to be transformed than whole occupations made redundant. Human work may shift from out-calculating AI toward choosing problems worth solving, questioning the evidence, and taking responsibility for outcomes. That last sentence is our interpretation.

### Four layers for agents that operate across organizations

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#agent-stack

| Layer | Question | Examples | Is blockchain required? |

| --- | --- | --- | --- |

| Communication and tools | What can the system do, how is work requested, and how is a result returned? | A2A, MCP, HTTP APIs | No |

| Identity and authority | Which operator stands behind the agent, and what is it allowed to do? | PKI, DIDs, Verifiable Credentials, access control | No |

| Payment and escrow | Who pays what, and up to what limit? | Banks, cards, invoices, x402, on-chain payment | No |

| Audit and provenance | Who approved an action, and was data or an artifact changed? | Signed logs, C2PA, databases, shared ledgers | No |

An “AI agent” is a software system that an operator configures with goals, tools, credentials, and a budget. No protocol turns it into a legal person or the owner of an asset. Holding a key, being technically able to pay, and having contractual authority are three different things.

A2A standardizes capability discovery and task collaboration among different agents; MCP standardizes connections between AI applications, data, and tools. Being able to communicate does not establish trust, and knowing an identity does not prove a result correct. Keeping the layers apart shows where a blockchain helps and where it adds nothing.

### Was blockchain created for AI? Not in its origins

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#blockchain-origin

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

A technology can pick up uses far from its origin. If software agents cross corporate or national boundaries to transfer assets and share signed records with no single administrator in common, a permissionless ledger or a smart contract is one option. That supports “maybe for selected future uses,” not “required by all AI.”

### x402 and ERC-8004: tell a working protocol apart from a draft

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#emerging-protocols

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

ERC-8004 is still 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 says 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. Rolling identity, reputation, validation, and payment into one generic “AI blockchain function” hides which piece is still a draft, whom it trusts, and what it actually verifies.

### What a ledger can prove, and what it cannot

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#proof-boundary

| Easier to verify with cryptography and a ledger | Not established by that evidence alone |

| --- | --- |

| A signature made by the key corresponding to an account | Whether the signer had legitimate legal authority |

| Transaction order and finalized state | Whether an event in the physical world is true |

| A state transition that follows public rules | Whether an AI judgment is accurate, fair, or safe |

| That data matching a hash has not been altered | Whether the original data or claim was true |

As Ethereum’s own documentation explains, a smart contract cannot reach off-chain information by default; an oracle is what imports external data. Using several oracles and signatures can reduce tampering and single points of failure, but choosing the sources, catching false reports, and avoiding a shared dependence on the same mistaken source are still open problems. Consensus does not manufacture truth about the world.

Putting a hash of an AI answer on-chain can prove that the recorded version was not changed afterwards. 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 stay separate.

### Trust infrastructure does not always need a blockchain

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#non-chain-alternatives

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

C2PA 2.4 binds content to hashes, signed claims, credentials, and provenance. The specification draws a boundary: it validates the association and the tamper evidence; it does not judge whether a claim is good or bad. It shows a signature-and-trust-list route to provenance for AI content that needs no blockchain.

Inside a single organization, an ordinary database, an append-only log, PKI, OAuth, a hardware security module, and accounting controls may do better on privacy, revocation, cost, and accountable ownership. The reason to reach for a shared ledger is not that one participant is an AI; it is that several parties need common state without handing it to a single administrator.

### Agent-economy risks: oracles, Sybils, privacy, keys, and responsibility

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#risk-boundaries

| Risk | Why blockchain alone does not solve it | Needed design |

| --- | --- | --- |

| Oracle | A false off-chain input becomes a faithfully recorded falsehood | Source diversity, timestamps, appeals, and stopping conditions |

| Sybil and reputation manipulation | One actor can create many identities and rate itself | Reviewer selection, stake, credentials, and external audit |

| Privacy | Public, durable metadata can reveal relationships and behavior | Data minimization, off-chain storage, and selective disclosure |

| Key or authority misuse | A valid signature does not show that an action was appropriate | Spending limits, allowlists, multisig, revocation, and human approval |

| Accountability gaps | Responsibility is split across agent, model, protocol, and operator | Named owners, deployers, auditors, and remedy procedures |

Automated payment should not wire a model output straight to an unlimited key. Cryptography accepts a formally valid signature even when the key was stolen or the authority behind it was too broad. AI safety and wallet security are different problems, and the policy engine should stay separable from the settlement layer.

### Editorial perspective: humans set purpose, AI explores, and ledgers mark boundaries

Section URL: https://bitcoin.ne.jp/en/learn/computing-futures#editorial-view

This section is our interpretation. We expect computing in the 2030s 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. The scarce inputs will be computation, yes, but also trustworthy data, energy, verification time, and institutions willing to carry responsibility.

As AI explores more broadly and faster than people do, the central human task may move from working out every answer toward choosing questions worth asking, setting boundaries that include minority rights, and stopping and repairing harmful deployments. A gap in capability does not by itself settle who rules whom; the human position is decided by choices about deployment and institutions.

Blockchain was not born for AI. In an era when software actors transact across the boundaries of human organizations, though, it may become a narrow public rail for sharing who signed what, under which rules value moved, and what can be audited. That role only comes into focus once a ledger stops being called a “truth machine” and communication, identity, payment, provenance, and real-world accountability stay distinct.

### Primary Sources

- IEA — Key Questions on Energy and AI (2026): https://www.iea.org/reports/key-questions-on-energy-and-ai/executive-summary
- International AI Safety Report 2026: https://internationalaisafetyreport.org/publication/international-ai-safety-report-2026
- U.S. Department of Energy — Advanced Scientific Computing Research FY 2026 Congressional Justification: https://www.energy.gov/sites/default/files/2025-07/FY2026-PresidentsRequest-ASCR.pdf
- EuroHPC JU — Quantum Computing & Access: https://www.eurohpc-ju.europa.eu/quantum-technologies/quantum-computing-access_en
- NIST — AI Risk Management Framework Core: https://airc.nist.gov/airmf-resources/airmf/5-sec-core/
- OECD — Introducing the OECD AI Capability Indicators: https://www.oecd.org/en/publications/introducing-the-oecd-ai-capability-indicators_be745f04-en.html
- ILO — Generative AI and Jobs: A 2025 Update: https://www.ilo.org/publications/generative-ai-and-jobs-2025-update
- METR — Measuring AI Ability to Complete Long Software Tasks: https://metr.org/blog/2025-03-19-measuring-ai-ability-to-complete-long-tasks/
- Satoshi Nakamoto — Bitcoin: A Peer-to-Peer Electronic Cash System: https://bitcoin.org/bitcoin.pdf
- x402 Foundation — x402 protocol: https://github.com/x402-foundation/x402
- Ethereum Improvement Proposals — ERC-8004: Trustless Agents (Draft): https://eips.ethereum.org/EIPS/eip-8004
- A2A Project — Agent2Agent Protocol specification: https://a2a-protocol.org/v0.3.0/specification/
- Model Context Protocol — Specification 2026-07-28: https://modelcontextprotocol.io/specification/2026-07-28
- W3C — Decentralized Identifiers (DIDs) v1.0: https://www.w3.org/TR/did/
- W3C — Verifiable Credentials Data Model v2.0: https://www.w3.org/TR/vc-data-model-2.0/
- C2PA — Content Credentials Technical Specification 2.4: https://spec.c2pa.org/specifications/specifications/2.4/specs/C2PA_Specification.html
- Ethereum.org — Oracles: https://ethereum.org/developers/docs/oracles/