A DAO building
decentralized AI compute.

Decentralized compute. Side by side.

A frank comparison with the existing decentralized AI and compute networks. We are deliberate about what we do differently and why.

vs Centralized AI clouds (AWS, GCP, Azure)

Trust model	Centralized: trust the cloud provider's audit reports. DAIT: hardware-attested, every call cryptographically verifiable.
Pricing	Centralized: set by the cloud, opaque, contract-gated. DAIT: reverse Dutch auction, transparent, public order book.
Workload control	Centralized: AUP gates, can deplatform. DAIT: permissionless. Hosts opt-in to license rules.
Margin to provider	Centralized: 60-80%. DAIT: 60% to host, 30% to validators, 10% to community/safety.

vs Bittensor (TAO)

Verification	Bittensor: subjective scoring by validators. Vulnerable to weight-copying, addressed by commit-reveal. DAIT: hardware-attested receipts. Validator scoring is verification of cryptographic proofs, not subjective evaluation.
Token supply	Bittensor: 21M cap with halving. Bitcoin-style emission cliff in long term. DAIT: compute-indexed elastic emission. No cliff. Supply expands with demand and contracts with burn.
Subjective work	Bittensor: subnets compete on subjective quality (text, image). Hard to verify. DAIT: only deterministically-verifiable work earns consensus credits. Subjective work is paid via marketplace fees.
Account model	Bittensor: coldkey/hotkey two-key separation. DAIT: same two-key separation, extended to first-class agent accounts that can hire each other.

vs Akash Network (AKT)

Marketplace pattern	Akash: reverse Dutch auction, audited attributes registry, general containerized compute. DAIT: same reverse-auction pattern plus AI-specific verification layer Akash does not have.
Verification	Akash: no native AI verification. Audit attributes are static claims signed by trusted parties. DAIT: hardware attestation per inference call. Cryptographic proof of correct execution.
Workload type	Akash: general containerized compute, not AI-specific. DAIT: AI compute first-class. State channels for high-frequency inference. AI-agent accounts native.
Tokenomics	Akash: uncapped to 388M cap pivot. Uncertain narrative. DAIT: compute-indexed elastic emission, designed once, robust across bull/bear.

vs io.net (IO)

Hardware verification	io.net: initially relied on self-reported specs. Caught with counterfeit GPU claims April 2024. Retrofitting KYC and benchmarks since. DAIT: TEE GPU UUID attestation required at registration. Self-reported specs not accepted. Hard registration check.
Base layer	io.net: Solana. DAIT: sovereign L1 with attestation reporting native to consensus, not bolted on as a contract.
Pricing	io.net: fixed-rate ask-only. DAIT: reverse Dutch auction. Lower clearing prices.

vs Render Network (RENDER) and Aethir (AETH)

Job assignment	Render, Aethir: centralized scheduler. Foundation-controlled tier system. DAIT: reverse Dutch auction with attestation filters. No central scheduler.
Verification	Render: no cryptographic verification, "Proof of Render" is reputational. Aethir: proof-of-capacity (idle benchmark) plus proof-of-delivery. Idle subsidies inflate emissions. DAIT: hardware attestation per inference. No idle subsidies. Pay only for verified work.
Trust source	Render, Aethir: foundation curates tiers and quality scores. DAIT: on-chain audit attribute registry, multiple auditors, governance-managed trust.

vs Centralized AI APIs (OpenAI, Anthropic, Google)

Most AI today is consumed through closed inference APIs. The trade is straightforward: a tenant sends a prompt, the provider returns a completion, and the tenant pays per token. The provider's word is the only evidence that the requested model ran. There is no receipt, no proof, and no recourse if the served model was a quantized variant or a cheaper substitute. DAIT inverts this. Every inference call returns a hardware attestation that names the model hash, the host's GPU UUID, and the input/output digest. A tenant can verify on-chain that the inference paid for is the inference performed.

Reproducibility on centralized APIs is structurally hard. Providers ship silent updates, retire snapshots, and reroute traffic to cheaper backends without notice. DAIT inference is pinned to a content-addressed model hash. The same hash run on attested hardware returns the same result. Audits, regulatory submissions, and scientific work that depends on byte-for-byte reproducibility have a path on DAIT that they do not have on a closed API.

Trust model	Centralized API: trust the provider's word that the requested model ran. DAIT: hardware attestation per call. Model hash and GPU UUID on every receipt.
Reproducibility	Centralized API: providers update models silently. Yesterday's outputs may not be reproducible tomorrow. DAIT: content-addressed model hash. Same hash, same hardware, same output.
Pricing	Centralized API: per-token rate cards set by the provider. Volume discounts gated by enterprise contract. DAIT: reverse Dutch auction. Public order book. Hosts compete on price.
Workload acceptance	Centralized API: AUP-restricted. Whole categories banned. Account closure with no appeal. DAIT: permissionless tenancy. Hosts opt-in to license rules per workload class.
Prompt privacy	Centralized API: prompts and completions visible to provider, retained for abuse review and training where ToS allows. DAIT: inference runs inside a TEE. Prompts decrypted only inside the enclave, never visible to the host operator.
Data residency	Centralized API: traffic routes to whichever region the provider chooses. Regional pinning is contract-only, unverifiable. DAIT: tenants filter on host region attribute, attested by the host's audited registry entry.

vs Bitcoin L2 / sidechain proposals for AI

A handful of projects propose hosting AI workloads on Bitcoin layer-2 systems such as BOB, Stacks, and various sBTC-style sidechains. The pitch is Bitcoin-grade settlement security for AI compute. The reality is harder. Bitcoin's settlement cadence is measured in tens of minutes. Anchoring a sidechain to Bitcoin inherits that latency for any operation that needs final settlement on the base layer. AI inference at a useful tempo cannot wait for Bitcoin confirmations. BTC L2s also lack the chain-level primitives that AI needs: marketplace order books, attestation registries, agent accounts, state-channel settlement. These can be approximated by smart contracts, but bolt-on contracts pay a recurring tax in gas, latency, and audit surface compared with chain-native modules.

DAIT plans to integrate Bitcoin restaking via standard restaking infrastructure once that ecosystem stabilizes. The result is BTC-pooled security where economically appropriate, with AI primitives that remain native to DAIT rather than retrofitted onto a script-limited base layer.

Throughput	BTC L2s: hundreds of TPS in optimistic conditions, lower under contention. DAIT: thousands of TPS at the chain layer, with state channels moving 99 percent of inference traffic off-chain.
Settlement latency	BTC L2s: sidechain finality is fast, but BTC anchoring takes hours. Cross-L2 settlement inherits that delay. DAIT: sub-second pre-confirm, ~6 second finality on the DAIT chain.
Native AI primitives	BTC L2s: none. AI features live in user-deployed contracts. DAIT: nine chain-native domain modules covering attestation, marketplace, agents, payments, governance, slashing, treasury, industrial data lake, and pseudonymous credentialed identity.
Programmability	BTC L2s: Bitcoin script is intentionally restricted. Most BTC L2s add EVM, but lose bridging guarantees in the process. DAIT: EVM at launch with planned DAIT-specific VMs for agent execution and inference policy.
Bitcoin-grade security	BTC L2s: sold as the primary feature, but bridge designs vary widely in honesty. DAIT: planned Bitcoin restaking integration via standard restaking infrastructure. Security parity where useful, AI primitives where Bitcoin scripting cannot reach.
Workload fit	BTC L2s: payments-first. AI is an afterthought. DAIT: AI compute first-class. Payments are one module among nine.

vs zkML rollups (zero-knowledge inference proof systems)

zkML is the research direction that produces zero-knowledge proofs of inference: a tenant gets a succinct proof that some model ran on some input and produced some output, with no need to trust the host. The math is real and the trust model is appealing. The cost is the constraint. Generating a ZK proof of a transformer forward pass is currently 100 to 1,000 times slower than running the inference itself, and on-chain verification of the resulting proof is still expensive enough that zkML is unsuitable for high-throughput consumer inference. Hardware-enclave attestation has roughly 5 percent overhead on confidential-compute-capable GPUs in production today, and the attestation quote is a few kilobytes that can be verified for cents.

DAIT runs TEE attestation as the hot path because that is what the economics support today. We also expose zkML as an opt-in premium tier for high-value workloads where TEE trust is insufficient: regulated healthcare, financial model audits, government work. As zkML hardware and proving systems improve, the cost gap will close, and DAIT's marketplace will route accordingly. The choice is the tenant's, priced by the market.

Inference speed	zkML: 100x to 1000x slower than raw inference today. DAIT TEE path: approximately 5 percent overhead vs bare-metal on confidential-compute-capable GPUs.
Trust assumption	zkML: mathematical. Soundness of the proving system, no hardware trust. DAIT TEE path: hardware vendor honesty plus revocable attestation. Compromised keys can be revoked at the registry layer.
Proof cost	zkML: proofs are expensive to generate, verifying on-chain is non-trivial gas. DAIT TEE path: attestation quotes are kilobytes. Verification is a signature check.
Tooling maturity	zkML: 2026 state is research-grade. Multiple zkML proof-system implementations are active and improving but not yet production-ready for general transformer workloads. DAIT TEE path: CPU and GPU hardware-enclave confidential-compute modes have been in production for years.
Workload fit	zkML: low-throughput, high-value, audit-critical workloads where the speed penalty is acceptable. DAIT TEE path: general-purpose inference at production speed.
DAIT's hybrid	Default: TEE attestation as the hot path. Opt-in premium tier: zkML for workloads that demand it. The tenant chooses, the market prices the choice.