Fetch.ai

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Beyond Bitcoin, FET's 90-day return correlations to the other two largest layer-1 references help characterize whether the asset moves as part of a crypto-market beta complex or as something more idiosyncratic. The Pearson correlations across daily log returns:

• Bitcoin reference: +0.45 — a moderate positive relationship.
• Ethereum reference: +0.52 — a moderate positive relationship.
• Solana reference: +0.62 — a strong positive relationship.

High correlation across several references usually means the asset is mostly crypto-market beta: when BTC, ETH and SOL move, it moves too, and independent alpha is hard to find. Low or negative readings point to its own drivers — project-specific news, intra-crypto sector rotation or narrative shifts — that can decouple it from the broad market.

Fetch.ai (FET) is a cryptocurrency, traded on global digital-asset markets. It trades at $0.1595 as of the latest update, with a 24-hour move down 2.39%, placing it at rank #41 by market capitalisation among all listed digital assets. Fetch.ai's current market cap stands at $358.47M, a figure used by traders, analysts and institutional desks to gauge relative liquidity and risk exposure across the crypto market.

On this page you'll find a live, daily-refreshed dashboard tracking Fetch.ai across multiple data sources — price history going back several years, on-chain activity where available, fundamentals like circulating supply and dilution, top exchanges by volume, technical analysis using moving averages and RSI, and an algorithmic short, mid and long-term forecast. All figures are pulled from public APIs and cached locally; nothing here is investment advice.

Fetch.ai is part of the AI×crypto sector — networks attempting to capture value from the explosion in AI demand by providing the substrate on which AI services run (compute, data, identity). This is a narrative-heavy category, and prices have historically tracked AI-sector sentiment as much as any underlying network metric.

Fetch.ai sits at the intersection of artificial intelligence and crypto economics. The specific architecture varies: some networks pay token rewards to GPU providers contributing compute, some run a decentralized inference market, and some build proof-of-personhood / identity systems for the AI era. The unifying thread is using crypto economics to coordinate compute, data, or identity at scale outside of any single AI company's control.

In practice the token is used to buy compute, reward hardware providers, settle inference, stake on data quality, or attest identity. For most projects in the category, measurable on-chain usage is still small next to market cap, so the assets trade largely on narrative for now.

Fetch.ai's tokenomics combine its supply schedule, current circulating supply, and the relationship between circulating market cap and fully-diluted valuation. At 82.8% of maximum supply already issued, the remaining issuance is a relatively small fraction of total potential supply. Sell pressure from future issuance is therefore limited. The fully diluted valuation (FDV) of $0.4 billion stands at 1.2× the current circulating market cap of $0.4 billion. The wider this ratio, the more issuance overhang the asset faces — a structural headwind on valuation that mature assets like Bitcoin do not face but that early-cycle assets typically do.

The question that matters for a longer-term view is simple: does value accrual (burns, staking-yield reinvestment, deflationary mechanics, growing ecosystem TVL) run ahead of new issuance, or behind it? Ahead, and price can compound quietly; behind, and it leans on continuous fresh demand to soak up the supply.

At the macro level, the variables that move this asset class most are the direction of US interest rates, the supply of dollar liquidity, and the state of global risk appetite. Crypto has behaved like a long-duration, high-beta risk asset: it tends to rally when real yields fall and the dollar softens, and to sell off when liquidity tightens.

To read crypto in a wider context, keep an eye on four markets — the dollar, where strength typically pressures risk assets; long-dated Treasury yields, which set the discount rate on future cash flows; gold, sometimes a parallel hard-money bet; and US equities, frequently correlated with crypto during liquidity-driven swings despite the popular "uncorrelated" label.

For Fetch.ai specifically, the macro variable that has empirically led price most often is global crypto-market liquidity — proxied by stablecoin total supply, futures open interest, and exchange volumes. When these expand, FET tends to follow; when they contract, the relationship typically reverses. Given the 90-day BTC correlation of +0.45, FET's near-term macro sensitivity will be similar to Bitcoin's — driven primarily by the same liquidity conditions and risk-asset flows.

The hazards are distinctive: prices lean on AI-sector sentiment more than on actual usage, identity-focused projects carry extra regulatory exposure, centralized AI providers compete with far better unit economics, and it remains unproven how much AI compute genuinely needs to be decentralized. Expect volatility above the broad alt-coin average.