Why Vitalik’s Perspective on Self-Sovereign Computing is Misguided

By Gaurav Sharma, CEO ofio.net

Vitalik Buterin recently proclaimed that 2026 should be the year to “regain lost ground in computing self-sovereignty.” He outlined the personal adjustments he has made: substituting Google Docs with Fileverse, Gmail with Proton Mail, and Telegram with Signal, while also testing the operation of large language models locally on his laptop instead of relying on cloud services.

The rationale is valid. Centralized AI infrastructure poses a significant issue. Three corporations – Amazon, Microsoft, and Google – currently dominate 66% of global cloud infrastructure expenditure, a market that reached $102.6 billion in a single quarter last year. When every query traverses this concentrated infrastructure, users relinquish control over data that ought to remain confidential. For those who value digital autonomy, this situation should be perceived as a systemic failure. However, Vitalik’s suggested remedy – hosting AI locally on personal devices – entails a compromise that need not exist. For anyone aiming to develop substantial AI applications, his framework does not provide a viable path forward.

The limitations of local computing

Operating AI on one’s own device is undoubtedly appealing. If the model remains on your laptop, your data does too. No third-party involvement, no surveillance, no reliance on corporate infrastructure. This approach is effective for lightweight applications. An individual performing basic inference or a developer testing a small model can derive value from locally-hosted models. Vitalik recognizes the existing challenges regarding usability and efficiency but presents them as temporary obstacles that will eventually be resolved.

Nonetheless, training models, executing inference at scale, and deploying agents that function continuously require GPU power that personal hardware cannot provide. Even a single AI agent operating overnight necessitates continuous computing. The promise of always-on AI assistants falters the moment you leave your workstation. Enterprise implementations demand thousands of GPU-hours daily. A startup training a specialized model could consume more computing resources in a week than a high-end laptop can offer in a year. An ambitious research team might allocate 80% or more of its budget solely on GPU capacity – resources that could otherwise be invested in talent, R&D, or market growth. Well-funded giants can easily absorb these expenses while others are priced out.

Local hosting does not address this issue and implicitly accepts a binary choice that leaves most developers with limited options: remain small and independent, or scale up and entrust your data to Amazon, Google, or Microsoft.

A misleading binary

The crypto community should be well-positioned to recognize this framing for what it is. Decentralization was never meant to diminish capability to maintain independence; it is about allowing scale and sovereignty to coexist. The same principle applies to computing.

Globally, millions of GPUs remain underutilized in data centers, enterprises, universities, and independent facilities. Today’s most advanced decentralized computing networks consolidate this fragmented hardware into elastic, programmable infrastructure. These networks now extend across over 130 countries, providing enterprise-grade GPUs and specialized edge devices at costs up to 70% lower than traditional hyperscalers.

Developers can access high-performance clusters on demand, sourced from a distributed pool of independent operators rather than a single provider. Pricing is based on usage and competition in real-time, rather than contracts negotiated years in advance. For suppliers, idle hardware can be converted into productive capacity.

Who gains from open compute markets

The implications extend far beyond cost reductions. For the wider market, it signifies a genuine alternative to the oligopoly currently dominating AI. Independent research groups can conduct significant experiments rather than scaling down their ambitions to accommodate hardware limitations. Startups in emerging markets can develop models for local languages, regional healthcare systems, or agricultural applications without needing to secure funding for hyperscaler contracts.

Regional data centers can engage in a global market instead of being excluded by the structure of existing agreements. This is how we can effectively bridge the AI digital divide: not by compelling developers to accept less powerful tools, but by reorganizing how computing resources are delivered to the market. Vitalik is correct that we should resist the centralization of AI infrastructure, but the solution is not to retreat to local hardware. Distributed systems that provide both scale and independence are already in existence.

The true test of crypto’s principles

The crypto community has established decentralization as a core principle. Decentralized computing networks present an opportunity to demonstrate what crypto has always claimed it could: prove that distributed systems can match and surpass centralized alternatives. Lower costs, broader access, and no single point of control or failure. The infrastructure is already available; the question remains whether the industry will utilize it or settle for a form of sovereignty that only functions if one is willing to remain small.

The post Why Vitalik is Wrong About Self-Sovereign Computing appeared first on Cryptonews.