Author: Ahmad Shadid, Founder of O.xyz and Co-founder of IO.ne
New artificial intelligence startups emerge from stealth mode almost every week.
With sleek interfaces, clever prompts, and OpenAI keys powering the backend, these companies often boast seed round valuations that would make seasoned chip designers blush.
However, behind the glossy exterior lies "prompt arbitrage." Most so-called "AI companies" today are merely thin wrappers without defensible technology.
Companies spend a few cents querying proprietary models, then charge users a few dollars for the same answers, profiting from the difference. This profit can only last until the platform decides to limit traffic, raise prices, or change terms of service.
This fragility is invisible to most users but is fatal to the long-term credibility of the industry. When the gatekeepers turn, thousands of copy-paste applications will vanish overnight, taking investor capital and customer data with them.
What follows will be a reckoning: by 2027, we will witness a "mass API cleansing," the moment when platform landlords reclaim their territory. They will implement tenfold price increases and stringent usage quotas, wiping out 70% of today's AI startups overnight.
The only businesses that will survive are those built on the foundation of decentralized infrastructure.
An industry based on rented computing power cannot call itself infrastructure—it is merely user experience theater.
Dependence on centralized APIs introduces several systemic risks. The first is cost volatility: a sudden spike in fees for GPT-4 endpoints can easily double the operating costs for certain projects.
Second is supply risk: GPU shortages have forced several leading suppliers to limit throughput for small customers during peak demand periods. Finally, licenses can be revoked. A simple policy update can ban entire categories of content, turning once-viable writing tools into blank screens.
Each risk traces back to the same bottleneck: control over the inference pipeline. This bottleneck reflects the early days of online payments when Visa and PayPal could freeze accounts at will. The financial industry solved this problem with Bitcoin in 2009. AI now faces its own Satoshi moment.
Bitcoin separates currency from any single issuer by distributing consensus across thousands of nodes. A decentralized AI stack can do the same for computing power, models, and data. Applications will leverage multiple model pools instead of a single API key.
Execution jumps to the fastest and cheapest GPU clusters for processing jobs. In this new paradigm, model APIs are treated as interchangeable commodities. Model checkpoints are stored on persistent storage like the InterPlanetary File System or Arweave; parameter updates are propagated through verifiable proofs. The result is a antifragile grid where no vendor can lock the door.
This shift is already visible. Some networks auction idle GPU cycles to the highest bidder, while other projects design agents that can migrate between models without rewriting code. If the largest suppliers go offline, workloads are rerouted, just as Bitcoin rebalances computing power after a mining pool collapse.
Web3 provides the incentive layer that Web2 lacks. Tokens measure computing power and data, prove certification results, and on-chain payments coordinate thousands of independent GPU operators, model curators, and data stewards without a central landlord. Anti-censorship storage combined with validator-checked execution keeps weights, prompts, and agent states accessible even when cloud regions or jurisdictions go offline.
Equally important, smart contract governance allows stakeholders to vote on new security rules or replace underperforming models without seeking permission from the platform. Any stack reliant on software-as-a-service keys will succumb to the next adjustment in terms of service, while stacks embedding value, logic, and upgrades on-chain can continue to operate after today's packaged applications disappear.
Repricing will be brutal. Startups valued based on user interface appeal will trade at a discount once capital realizes their profits depend on others' server farms. In contrast, tokens and equity associated with verifiable computing networks, licensed data cooperatives, and agent runtimes will command a premium.
Institutional demand is already shifting. Asset management firms are prioritizing resilience and fee acquisition as key investment philosophies. Meanwhile, large language model providers seek guaranteed content rights.
Shutterstock's partnership with OpenAI demonstrates that clean data is worth real money; decentralized tokenized licensing extends this logic to every blogger and podcaster on the network.
Bitcoin teaches a fundamental lesson for the digital age: lasting value is built on resilience. Industries that ignore this do so at their peril, creating the illusion of infrastructure on a foundation that landlords can revoke at any time.
Thus, the enduring projects of the AI era will be governed by code rather than contracts. These projects are not designed for the current funding cycle but for the subsequent crashes and the next infrastructure shift. To succeed, they will be model-agnostic, diversified in computing power, and owned by their communities. They will be those who understand that the future of intelligence cannot be rented. It must be built, and its keys must belong to its builders.
Author: Ahmad Shadid, Founder of O.xyz and Co-founder of IO.ne.
Related: Jia Yueting's crypto gamble: Can Faraday Future's "digital treasury" support its car-making dream?
This article is for general informational purposes only and is not intended to be, nor should it be construed as, legal or investment advice. The views, thoughts, and opinions expressed here are solely those of the author and do not necessarily reflect or represent the views and opinions of Cointelegraph.
Original: “Opinion: Bitcoin (BTC) Shows the Way, Decentralized AI Must Ditch Rented Compute”
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