Back in 2022, I was helping a blockchain startup hire for an “AI + Web3” hybrid role—something that barely existed back then. The founder was adamant: “We don’t want to be OpenAI’s competitor—we want to decentralise intelligence itself.” It sounded visionary. But two years later, they’d pivoted to building yet another NFT marketplace. Why Web3‑AI efforts like theirs stalled? Funding dried up. Talent moved on. And centralised AI platforms? They skyrocketed.
So let’s get into it: Why Web3‑AI is losing the race to centralised AI—and what that means for those of us who’ve been neck-deep in crypto hiring since the ICO days.
Talent Isn’t Waiting Around
Here’s the brutal truth: if you’re a top-tier AI engineer, you’re probably getting six inbound offers a week—from Google DeepMind, OpenAI, Anthropic, or one of the VC-backed infra startups promising the moon.
Web3 can’t compete with that yet. The pitch is usually idealistic—“We’re building open-source, decentralised AI agents that respect privacy and avoid centralised control!” Sounds great on paper. But when the pay is half, the project roadmap is vague, and the runway’s 6 months? It’s a hard sell.
I’ve tried placing brilliant ML engineers into Web3-AI hybrids. More often than not, they ghost the interview loop after reading the whitepaper. One candidate told me, “It’s cool tech, but I’m not betting my career on ‘maybe one day decentralised LLMs will scale’.”
Centralised AI Has First-Mover Advantage—and It’s Massive
Why Web3‑AI is struggling isn’t just about funding or talent—it’s timing. OpenAI, Meta, Google—all had a head start, and they used it well. Closed-source models are faster, more powerful, and integrated into consumer products we use daily.
Meanwhile, Web3-AI projects are stuck proving that decentralised inference is even feasible at scale. You’ve got brilliant minds working on it—like Bittensor, Gensyn, or Ritual—but they’re still in the “network bootstrap” phase. In recruitment terms, that’s like hiring before you’ve even built the office.
The most promising decentralised AI companies I’ve seen are running lean and fighting uphill battles—on infra, on market education, on simply getting noticed. Even when they are making technical progress, they’re not breaking through the noise. Why? They lack the marketing engines that centralised firms have.
The Web3 Brand Is Still Recovering
Let’s not ignore the elephant in the room: crypto’s reputation hasn’t done Why Web3‑AI any favours.
After the FTX collapse and a wave of rug pulls, a lot of serious engineers started treating “Web3” like a dirty word. Even when the project is legit, I’ve seen AI folks hesitate simply because of the label. One ex-Google NLP expert told me, “I don’t want to explain to my parents that I’m joining a blockchain project right now. Feels risky.”
That stigma bleeds into hiring, partnerships, and fundraising. I’ve seen solid Web3-AI startups lose out on deals because the other side assumed “AI + crypto” was just another buzzword blend. It doesn’t help that a few bad actors have tried exactly that—grabbing attention with flashy whitepapers and zero technical depth.
The irony? Many of these decentralised AI ideas do solve real problems: censorship, model transparency, compute monopolies. But the Web3 baggage weighs them down.
The Community Is There—But It’s Fragmented
One thing I love about this space? The passion. Go on Twitter (sorry, X), jump into a Discord, and you’ll find incredibly smart people obsessing over decentralised compute, zkML, and peer-to-peer inference. But here’s the problem: they’re scattered.
You’ve got one camp building AI agents on-chain. Another working on LLM training incentives. Yet another focused on decentralised GPUs. And while diversity is a strength, it also makes collaboration harder. There’s no cohesive narrative.
Compare that to centralised AI: the messaging is tight, the demo videos go viral, and the products are shipped fast. In Web3, I’ve seen projects spend months just debating governance models before even testing an MVP.
As someone who’s helped recruit across dozens of these teams, I’ve often felt like I’m watching 10 brilliant builders try to dig a tunnel—but from different sides of the mountain, and no one’s quite aligned on where they’re meeting in the middle.
So… Is It Game Over for Web3-AI?
Not at all. But it is time for a reality check.
If decentralised AI is going to compete, it needs more than cool tech. It needs:
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Clear use cases beyond “we’re not OpenAI.”
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Better compensation to attract top-tier engineers.
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Storytelling that resonates with mainstream users—not just crypto-native devs.
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Real product velocity, not just ambitious whitepapers.
I’ve seen some green shoots. Gensyn is building an incentive layer for decentralised compute that actually works. Bittensor has a community that feels like early Ethereum—full of weird geniuses and die-hard believers. And Ritual recently raised funding to bring on-chain AI closer to real-world usage.
But we’re still early. And the centralised giants aren’t slowing down.
Final Thoughts (Over Coffee, Obviously)
I’ve spent years recruiting for crypto. I’ve watched hype cycles come and go. From ICOs to DAOs to NFTs to DePIN. This Web3-AI movement? It feels different—but it also faces a tougher fight than most.
So when someone asks me why Web3‑AI is losing to centralised AI, I don’t scoff. I nod. And then I say: “Yeah. But it’s not over. We just need to build smarter—and louder.”
Because let’s be honest: decentralisation isn’t dead. It’s just quieter than ChatGPT right now.
