Experts Say Zk Proofs Give DePINs an Edge as AI Trust Demands Rise

Goldman Sachs’ baseline forecast of $7.6 trillion in artificial intelligence (AI) capital spending ultimately depends on how long AI‑specific silicon remains useful. Decentralized networks promise major cost efficiencies but continue to battle latency issues, and experts argue their long‑term viability will hinge on prioritizing verifiability over raw performance.

This longevity is seen as the most critical factor because rapid innovation could make standard chips—which typically last four to six years—obsolete in three years, causing costs to skyrocket. Conversely, a “tiered model” where older chips are reused for simpler tasks, such as inference, could stabilize costs.

Data center complexity and the elasticity of compute demand are other variables likely to affect how much capital is expended on AI infrastructure in the next five years. Shortages in power grid capacity, specialized labor, and electrical equipment are also seen as factors elongating the build-out.

“The big cloud providers can do [fast work] because their GPUs sit next to each other in one building, connected by special cables that move data in microseconds,” Taszycki said. He explained that decentralized networks, which stitch together GPUs across different countries via the public internet, add milliseconds of delay. This latency makes decentralized orchestration competitive for batch jobs and fine-tuning but unsuitable for serving high-scale, live chatbots where user experience depends on near-instant responses.

Leo Fan, founder of Cysic, echoed these sentiments, insisting that decentralized inference is unsuitable for low-latency workloads. Fan argued, however, that latency is the wrong benchmark for comparing decentralized platforms and hyperscalers like AWS.

“The hard problem isn’t distributed compute but discovery, scheduling, and attestation. The wedge isn’t price-per-token; it’s verifiability,” Fan said. He noted that trusted execution environments (TEEs) and zero-knowledge (ZK) attestations allow decentralized networks to compete in sectors where trust and verification matter more than “tail latency.”

Beyond compute, the focus is shifting to how these capital-intensive projects are funded. While traditional private credit has ample capital, it often overlooks smaller or non-standard deals. Onchain credit offers distinct advantages, such as allowing retail investors to participate in data center revenue that was previously restricted to institutional limited partners. Furthermore, platforms like Maple and Centrifuge can syndicate loans in the $5 million to $50 million range—a bracket often ignored by firms like Apollo due to high underwriting costs relative to fees.

Finally, onchain credit enables novel “pay-per-inference” models, where revenue fluctuates with GPU usage. Such models fit more naturally into tokenized revenue-share structures than rigid 20-year traditional leases.

The consensus suggests a realistic timeline of 12 to 24 months for mid-sized syndicated deals to gain traction onchain, with majority-onchain mezzanine debt likely three to five years away. The first breakthroughs will likely come from Tier 2 operators rather than industry leaders like Coreweave.