Editing Openai/68de3c2d-a7fc-800f-bc1a-9b1928a73b37 (section)

==== Recurring Critiques and Concerns in Crypto × AI Theses ====

Despite the excitement, investors frequently acknowledge key challenges and risks at the intersection of AI and crypto. Several recurring critiques appear across multiple theses:
* Unclear Token Utility & Speculative Froth: A fundamental concern is that many early Crypto×AI tokens lack real utility, deriving value mainly from hype. The user’s thesis points out that “agentic token value is more speculative rather than based on utility... tokenomics don’t include how the agent’s usage drives value”, leading to post-sale price crashes. This critique is echoed by investors observing the 2023–24 wave of AI-themed tokens. Vitalik Buterin, for instance, noted that plenty of investors have thrown money at fledgling AI coins, but most of these efforts are barely scratching the surface of real utilitybankless.com<ref>{{cite web|title=bankless.com|url=https://www.bankless.com/vitalik-ai-in-crypto#:~:text=|publisher=bankless.com|access-date=2025-11-25}}</ref>. Bankless writers warned that the signal of genuine innovation will be drowned out by a flood of speculative “AI coins” in the next bull cyclebankless.com<ref>{{cite web|title=bankless.com|url=https://www.bankless.com/this-is-the-ai-x-crypto-cycle#:~:text=The%20AI%20x%20Crypto%20overlap,by%20total%20vaporware%20and%20shitcoinery|publisher=bankless.com|access-date=2025-11-25}}</ref>. Examples abound: many so-called “AI tokens” (FETCH, AGIX, etc.) saw dramatic pumps largely due to the AI hype (ChatGPT effect) rather than any change in fundamentals of those protocols. In late 2023, numerous micro-cap tokens branded with AI or GPT sprang up with meme-like fervor. Investors worry this speculative mania could tarnish the sector’s credibility or distract from serious builders. Response in theses: Some suggest stricter valuation frameworks – e.g., treating “on-chain AI models as commodities” that can be valued based on computational work put in (as cyber•Fund argues)cyber.fund<ref>{{cite web|title=cyber.fund|url=https://cyber.fund/content/crypto-ai-investment-thesis-2025#:~:text=AI%20models%20convert%20electrical%20power,perpetually%20tied%20to%20the%20blockchain|publisher=cyber.fund|access-date=2025-11-25}}</ref>cyber.fund<ref>{{cite web|title=cyber.fund|url=https://cyber.fund/content/crypto-ai-investment-thesis-2025#:~:text=We%20invest%20in%20technologies%20and,that%20accelerate%20commoditization%20of%20AI|publisher=cyber.fund|access-date=2025-11-25}}</ref>. Others urge new token designs where an AI service token accrues fees or usage value (beyond simple buyback-and-burn Ponziomics that some agent tokens tried). Overall, aligning token value to actual AI utility (compute consumed, useful work done, demand for the model’s output) is seen as an unresolved challenge. This directly mirrors the user thesis’s observation that current agent tokens often only resort to buyback/burn mechanisms and hype, rather than tying value to the agent’s performance, causing price declines after launch. Both investors and the user are effectively calling for better tokenomic models in the next generation of AI-crypto projects.
* Regulatory and Ethical Risks: Though not always front-and-center, many investors note potential regulatory scrutiny when crypto and AI converge. For example, could tokenized AI models be deemed securities? Will data privacy laws conflict with crowdsourced data marketplaces? There’s also the ethical dimension – e.g., concerns about AI agents making financial decisions or participating in governance. Vitalik categorized “AI as DAO governors or smart contract rule-enforcers” as a highly risky prospect, since a buggy or exploitable AI could wreak havoc if given too much controlbankless.com<ref>{{cite web|title=bankless.com|url=https://www.bankless.com/vitalik-ai-in-crypto#:~:text=3,of%20the%20Game|publisher=bankless.com|access-date=2025-11-25}}</ref>bankless.com<ref>{{cite web|title=bankless.com|url=https://www.bankless.com/vitalik-ai-in-crypto#:~:text=The%20riskiest%20category,its%20training%20data%20is%20transparent|publisher=bankless.com|access-date=2025-11-25}}</ref>. Theses sometimes debate AI safety: some argue decentralizing AI development (via blockchain) might improve safety by avoiding single points of failure and enabling on-chain “kill switches” for rogue AIsbankless.com<ref>{{cite web|title=bankless.com|url=https://www.bankless.com/vitalik-ai-in-crypto#:~:text=4,of%20the%20Game|publisher=bankless.com|access-date=2025-11-25}}</ref>. But others fear that permissionless AI networks might be harder to monitor for misuse. This remains an open question, often raised but not resolved in writings.
* Technical Maturity & Infrastructure Gaps: Across the board, there’s recognition that Crypto×AI is early and faces technical hurdles. One such hurdle is scalability – current blockchain throughput and cost may not handle AI-scale activity (e.g. millions of micropayments or constant agent interactions) without Layer-2s or new L1 designs. This is partly why the idea of AI-specific chains (app-chains) is floated. Another gap is the lack of established standards: until recently, there was no common identity or communication standard for agents. This is exactly what efforts like x402 and ATXP aim to fix. A16z’s thesis noted that “most of today’s AI agents operate in siloed ecosystems with closed APIs and lack standardization – blockchains can help establish open, interoperable standards”a16zcrypto.com<ref>{{cite web|title=a16zcrypto.com|url=https://a16zcrypto.com/posts/article/ai-crypto-crossovers/#:~:text=Today%20there%20are%20no%20established%2C,calls%20as%20an%20internal%20functionality|publisher=a16zcrypto.com|access-date=2025-11-25}}</ref>a16zcrypto.com<ref>{{cite web|title=a16zcrypto.com|url=https://a16zcrypto.com/posts/article/ai-crypto-crossovers/#:~:text=More%20broadly%2C%20most%20of%20today%E2%80%99s,often%20more%20easily%20upgradeable%20architectures|publisher=a16zcrypto.com|access-date=2025-11-25}}</ref>. The flurry of protocol-building now (as detailed) is essentially patching these early gaps. Privacy is another infrastructure challenge: many AI applications (especially involving user data or proprietary models) require confidentiality, which is hard to achieve on public ledgers. A common approach has been using Trusted Execution Environments (TEEs) or secure enclaves to run AI models in a protected off-chain environment while still logging hashes or payments on-chain. However, as the user’s thesis flags, recent failures in TEE technologies (e.g. exploits in Intel SGX) have cast doubt on this approach. Indeed, some agentic AI projects that relied on TEEs for privacy encountered setbacks when those TEEs were shown to be vulnerable. This has been a recurring concern: without TEEs, how can we perform private AI computation in a decentralized context? Alternatives like zero-knowledge proofs for ML or multiparty computation are being explored, but are not yet production-ready at scale. Thus, investors acknowledge a risk that the tech stack for private, secure AI (compute, storage, identity) is still immature. In the words of the user’s thesis: “These factors still tell us we are early in Crypto×AI, be it in terms of infra or consumer level.” – a statement essentially echoed by many VCs who caution that it’s Day 1 for this fusion of fields.
* Human vs. AI Distinction (Proof-of-Humanity): A subtler but important concern is how to distinguish AI agents from humans in decentralized networks. As AI-generated content, bots, and agents proliferate, there’s worry about sybils and fake participants overrunning networks or skewing governance and markets. A16z’s team noted that proof-of-personhood (PoP) solutions aren’t just about stopping bots in social media – they might become essential for drawing “clear boundaries between AI agents and networks of humans” to ensure fair, authentic digital experiencesa16zcrypto.com<ref>{{cite web|title=a16zcrypto.com|url=https://a16zcrypto.com/posts/article/ai-crypto-crossovers/#:~:text=Proof%20of%20personhood%20isn%E2%80%99t%20just,and%20more%20authentic%20digital%20experiences|publisher=a16zcrypto.com|access-date=2025-11-25}}</ref>. This is a rationale behind investments like Worldcoin, and why Blockchains may incorporate identity layers. Some theses predict a “verified humanity” badge could be attached to accounts for certain interactions, with unverified (potential AI) actors getting limited access. The critique here is if Crypto×AI grows without such measures, the line between human and AI could blur in undesirable ways, eroding trust in on-chain governance or user-generated datasets. The flip side is an opportunity: projects solving this (via biometrics, social attestations, or AI-detection algorithms) could become key infrastructure.
* AI Limits and Overpromising: Finally, a few investors caution not to overestimate AI capabilities in the near term. While generative AI is impressive, trusting autonomous agents with complex tasks (or funds) is still experimental. Some theses mention that current AI agents are relatively primitive (scripted bots with an LLM API, in many cases). The excitement is that crypto can augment them, but a concern is overpromising what “AI agents” can do today. For instance, early “on-chain AI” projects often just call an OpenAI API from a smart contract – useful, but not some self-contained sentient agent on Ethereum. If hyped incorrectly, this could lead to disillusionment. Investors like Placeholder’s Chris Burniske have urged pragmatism, reminding that Web3 should leverage AI where it truly adds value (automation, personalization) but not slap AI onto everything as a buzzword. This tempered approach is less a specific thesis point and more a cultural reminder in VC discussions.