On September 23, according to Jinse Finance, Stanford University professor Fei-Fei Li openly interrupted the optimistic narrative in the industry that "technology will generate its own rules"—when discussing AI safety, she explicitly stated that such systems cannot rely solely on internal self-assessment and risk control from companies, but must accept further supervision from independent institutions and the public sector. Her definition was quite specific: corporate researchers can certainly assess the performance and safety characteristics of individual models, but this "in-house evaluation" cannot replace the universal standards that must be jointly established by the government, industry organizations, and academia, which are effective across the entire industry. In the current context of confrontation between the "Accelerationists" and the "Decelerationists," her statement effectively pressed the pause button on the trust in platform self-discipline—Fei-Fei Li publicly sided with "third-party regulation," targeting the legitimacy of companies proving their own safety, rather than abstract discussions about the merits and demerits of AI. For internet and crypto platforms that have long relied on self-discipline and internal risk control, which were later incorporated into licensed operations and external audit frameworks, this discourse is not confined to professional debates within the AI circle, but resembles a precursory signal before a new round of regulatory boundary redefining.
Fei-Fei Li sides with the Decelerationists: corporate self-assessment publicly named
Under the current mainstream approach, leading AI companies generally treat safety as an "internal engineering process": research teams first assess the performance and safety of individual models, then organize red team testing to try to trigger unauthorized outputs and biases, and complete an internal compliance review before deployment, ultimately having the company itself "endorse" risk in their prospectuses, white papers, or product descriptions. For a long time, this corporate self-assessment has been packaged as a compromise of "professionalism" and "efficiency," satisfying the strong demands of the capital markets for "accelerated development" of AI while avoiding external scrutiny that could slow down product iterations, and has been viewed as the path of minimal resistance for promoting innovation.
This time, Fei-Fei Li did not follow this consensus. She clearly pointed out the critical flaw: assessments by internal researchers of individual models, no matter how detailed, cannot replace the general standards collaboratively established by government, industry, and academic institutions. In other words, companies can grade their own models, but they do not have the right to define what constitutes the "passing line" itself. When a long-time figure in academia, viewed as a technology promoter rather than a regulatory representative, openly sides with the "Decelerationists" to question the self-assessment model, it essentially challenges the credit structure of the industry—where the market previously defaulted to “companies proving their own safety can continue to accelerate,” this default is now beginning to lose persuasiveness. The question of who will determine if models are sufficiently safe has shifted from technical details to the issue of how regulatory power will be reallocated.
Third-party regulation comes into play: who will grade the models
In Fei-Fei Li's vision, those grading the models will no longer be just the people who write the models. She named "government, industry, and academic institutions" to jointly establish universal safety and performance standards, implying that future graders will include at least three categories: public sectors with enforcement power that can write standards into permits and enforcement lists; business associations and professional organizations familiar with business scenarios that represent industry consensus; and academic teams that master cutting-edge technology and can convert lab tests into actionable metrics. They will not replace internal assessments but will shift internal evaluations from being a “self-talk” reference to a necessary regulatory prerequisite. For crypto platforms and internet companies, who will lead these standards is not merely a battle for discourse power, but also the starting point for future licensing, auditing, and compliance costs.
Once the graders change, the grading logic will also be rewritten. Corporate self-assessment inherently tends toward "going online as soon as possible, capturing the market," with risk preferences and commercial interests closely tied; even with a safety team, the final decision-makers are often still from the business line; third-party assessments must account for systemic risks, public safety, and policy responsibilities, with their incentives and accountability directed toward regulators and the public rather than the revenue curve of a single company. In traditional industries like finance and auditing, this difference in responsibility and authority has already been solidified through mandatory third-party audits and ratings, and internet platforms are also starting to introduce external assessments and compliance consulting agencies in content safety and data protection. Following Fei-Fei Li's logic, if the universal standards for AI models are set and continuously updated by external agencies, the discretionary space for companies in safety evaluations will be systematically compressed: new models will need to pass standardized tests before going live, risk control teams will need to reconstruct processes around external metrics, and once biases or risk control failures emerge, accountability will more swiftly point to "whether universally accepted standards were adhered to" rather than the company's unilateral interpretations. For those crypto and internet platforms using AI for risk control, anti-money laundering, and automated decision-making, the entities they need to persuade will shift from the market and users to third-party evaluators and regulatory bodies that hold the power to interpret standards.
From AI to on-chain: the myth of platform self-discipline rewritten by regulation
When Fei-Fei Li pointed her finger at "corporate self-assessment," the crypto industry had actually traversed a similar trajectory. In the early days, whether centralized trading platforms or on-chain applications, they relied on the story of "we manage ourselves" to navigate regulation: self-built risk control teams, internal review lists, black-box risk control models were packaged as "safer than traditional finance," earning a period of relaxed observation. However, as risk events accumulated, regulatory requirements from multiple countries began to demand platforms to operate under licenses, undergo external audits, and compliance checks; internal risk control transformed from the main character into "basic configurations." What truly determined a platform's survival became whether it could withstand third-party knock, audits, and report-writing.
Once the regulatory paths for AI take shape through "third-party evaluations + universal standards," this line of thinking is unlikely to halt at the models themselves. Today, crypto platforms and DeFi protocols have embedded AI models into risk control, anti-money laundering, user profiling, and trading decisions, where the risks of black-box models are no longer abstract technical issues, but directly affect compliance matters such as account freezes, transaction rejections, and settlement orders. If regulatory bodies adopt the practices of traditional finance and auditing industries, writing "auditable by external parties" and "reproducible by third parties" into the rules, then algorithmic trading engines, on-chain risk control logic, and smart contract security reviews are likely to be required to undergo independent validations like financial reports. By then, the self-discipline narrative that crypto platforms convey to the market will have to be rewritten into compliant stories that regulators and evaluators can understand, and the ability to prove their models are verifiable and accountable may become a prerequisite for platforms to continue operating.
Compliance responsibilities upgrade: AI companies and crypto platforms must prove their reliability
If Fei-Fei Li's call that "one cannot rely solely on corporate self-assessment" translates into a system, the first batch to be illuminated will actually be those AI companies providing models to financial institutions and crypto platforms. In traditionally regulated industries, when introducing key technological components, institutions are already required to maintain technical documentation, audit records, and third-party evaluation reports for inspection; once this logic translates into AI scenarios, model providers will no longer just deliver a piece of code but must also include comprehensive model documentation, training and testing specifications, explainability materials, and even audit interfaces reserved for independent evaluators to prove that the algorithms employed for anti-money laundering, risk control, and user scoring can withstand retrospective review with regard to bias and safety.
On the other hand, crypto exchanges, wallets, and DeFi protocols that introduce AI services will be required to translate the market phrase "we used AI" into a compliance statement of "this model has a clear source, its evaluation process is traceable, and it has undergone external verification." Especially when AI models are embedded within risk control and recommendation decision chains, platforms may need to indicate in compliance documents: who developed the model, how it was tested, and which independent institution provided the evaluation conclusion, and present corresponding records during inspections. This will not only reshape budgets—expanding the roles responsible for model reviews and liaising with third-party organizations from a single risk control team—but will also change organizational structures: technology, legal, and compliance will be forced to reorganize workflows around the goal of "proving reliability to the outside," whoever can effectively navigate this proof mechanism will qualify to continue discussing "innovation" within regulatory boundaries.
The debate on deceleration is not over: regulatory pathways and market narratives are still in tension
The dispute between the Decelerationists and the Accelerationists is far from reaching a conclusion. According to Jinse Finance, Fei-Fei Li's opposition to the idea of "corporate self-assessment solves everything" is itself a symbol: the mainstream academia is no longer satisfied with relegating safety to internal platform processes, but is starting to publicly seek discourse power for third-party standards and public regulations. Although there currently lacks legislative texts and timelines directly corresponding to this statement, her position has shifted the focus of regulatory routes from "whether to decelerate" to "who will evaluate and by what standards to decelerate or accelerate." Under this new framework, the future path is more likely to be a mixed model of "corporate self-assessment + independent institution evaluation + public policy constraints," rather than a simple choice between braking and accelerating; the balance among safety, innovation, and competition will likely be collectively delineated by third-party regulatory agencies in both system design and actual implementation. For crypto platforms that are embedding models within risk control, anti-money laundering, and automated decision-making, as well as for on-chain projects attempting to drive transactions and user services with models, this debate has ceased to be a peripheral issue: technological integration means that AI regulation naturally extends to on-chain finance and platform governance; it will directly determine how the market for third-party certifications will take shape, how cross-border standards will be coordinated, and how regulatory boundaries for algorithm transparency and model accountability will be drawn. Those who can first understand and adapt to this new rule set will have the opportunity to maintain their narrative power and survival space in the next round of regulatory restructuring.
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