We didn't see a single line of code. We didn't see benchmark scores. We didn't see the names of the engineers behind the project. Yet Thinking Machines emerged from 18 months of secrecy to announce Inkling, a supposedly 'open' AI model that they claim marks a turning point for decentralized AI. The press release, published on Crypto Briefing, is a masterclass in narrative-driven hype over substance. In a market starved for the next big thing in AI-crypto, this model might as well be a ghost.
The decentralized AI narrative has been a boon for speculators since 2024. Projects like Bittensor, Render Network, and Oraichain promised to democratize AI training and inference using blockchain incentives. But the promise has rarely matched the delivery. Most 'decentralized AI' projects remain PowerPoints with tokens, not working models that rival OpenAI or Meta's LLaMA. The community is growing cynical. Into this void steps Thinking Machines, claiming their Inkling model is 'open' and will 'shift the trajectory' of the space. The only problem? There is nothing to verify.
Technical evaluation: a black box with no buttons. The article provides zero technical details. No model architecture, no parameter count, no training data source, no license type, and no comparison against existing open models like LLaMA 3, Mistral, or DeepSeek. 'Open model' is a meaningless term without a specific open-source license—does it mean open weights, open code, or just an open API? Each has wildly different implications for trust and decentralization. In my cybersecurity days reverse-engineering early ZK-rollup papers, I learned that the first step to credibility is disclosure. Inkling offers none. The 18-month development cycle could be a sign of deep work, or it could be a small team just now building. The risk score here is high—not because we know something is wrong, but because we know nothing is right.
Tokenomics: the ghost asset. There is no token, no economic model, no value capture mechanism. That alone makes Inkling irrelevant to the crypto trading community. Crypto Briefing readers looking for the next AI token find nothing to ape. But this also raises a strategic question: why announce on a crypto outlet if there is no token? Possibly to pre-sell a future token, or to attract developer attention for a yet-unrevealed decentralized compute network. Still, without any incentive structure, the model stands alone—and alone, it cannot bootstrap its own ecosystem.
Market impact: zero. This news has no direct price impact because there is no tradable asset. The broader decentralized AI sector might see a brief emotional bump, but without confirmable data, the hype will fizzle in hours. The announcement came during a sideways market—April 2025 sees crypto chop, and investors are directionless. Inkling is a flag planted in mud, not a signal to trade.
Team and governance: anonymity in the wrong direction. The article names no team members, no advisors, no funders. Thinking Machines is a ghost company. In blockchain, anonymity can be a feature for privacy-focused protocols, but for an AI model claiming to advance decentralization, it's a liability. Who do you trust to not insert backdoors? Who ensures the model weights are not poisoned? From my experience auditing the Aura Finance protocol, I caught a reentrancy bug that major firms missed. That was possible because the code was public. Here, there is no code, no team, no audit. The governance is entirely centralized—the anonymous team holds all keys. If they disappear, the model becomes abandonware.
Contrarian angle: the blank slate. Perhaps the lack of details is strategic. By not locking into a specific architecture or token, Thinking Machines retains flexibility to adapt to fast-moving AI advancements. They could integrate with existing ecosystems like Bittensor or build a new incentive layer later. In a market where every project overshares vaporware, under-promising on details might seem cautious. But this ignores one critical fact: the burden of proof is on the issuer. Without any proof, the only narrative is absence. If Uniswap V4's hooks scare off 90% of developers due to complexity, what does a model with no documentation do? It ensures only the most optimistic speculators engage. Layer2 sequencers are centralized, but at least they have running code. Bitcoin's hash power concentrates after halving, but you can see the pools. Here, we have a black box with nothing to show.
Regulation didn't require Thinking Machines to disclose their training data or compliance with the EU AI Act. But that doesn't mean copyright risks are absent. If Inkling was trained on copyrighted data, the project could face legal challenges that kill it overnight. The silence on data provenance is a red flag.
Takeaway: wait for the commit. The only signal that matters is a public repository with real code, real benchmarks, and a real license. Until Thinking Machines publishes that, treat Inkling as a footnote in the long history of AI vaporware. The next move is theirs—but the burden of proof is heavy. I'd only start paying attention when I see a GitHub commit hash in their next press release.