Trump’s latest AI czar has already resigned
The director role for the Center for AI Standards and Innovation (CAISI) has become a revolving door since David Sacks left his position as czar.
Chris Fall, the director of the Center for AI Standards and Innovation (CAISI), has resigned, the agency confirmed to multiple news outlets.
He was appointed just three months ago after the last appointee, Collin Burns, left in less than a week, The Washington Post reported at the time. Burns was reportedly “pushed out” of the job in April because he previously worked for Anthropic and the Trump administration had been battling with the company, sources told the Post.
No reason was given for Fall’s departure. Prior to leading CAISI, Fall was the director of the Department of Energy’s Office of Science during the first Trump administration and had been the acting director of the DOE’s Advanced Research Projects Agency-Energy. He worked in the DOE’s Office of Naval Research (ONR) prior to that.
Before Burns and Fall, the agency was led by venture capitalist David Sacks, whose title at the time was White House AI and crypto czar. Sacks stepped down in March.
CAISI, which operates under the National Institute of Standards and Technology, is the primary organization for developing technical standards and testing methods for AI models as well as assessing cybersecurity risks. Yet it was not the agency at the center of the most recent model-risk brouhaha.
That occurred in June when the U.S. Commerce Department invoked an obscure export control directive that effectively forced Anthropic to pull its Mythos and Fable models from the market. The ban was lifted by the end of the month , when Secretary of Commerce Howard Lutnick said he was satisfied with Anthropic’s safety plans.
Earlier this month, the White House also signed an executive order for a new AI safety oversight program called “Gold Eagle” that creates a clearinghouse for cybersecurity vulnerability coordination. A host of federal organizations were named as part of the program, including the Commerce Department and Department of Homeland Security. But, as CNBC pointed out, CAISI was not among the federal organizations mentioned.
Meanwhile, after Anthropic’s models were freed from the ban, Google DeepMind CEO Demis Hassabis began calling for the creation of an independent, industry-run standards body to regulate frontier AI modeled after FINRA — the same sort of mission that CAISI was formed to tackle.
Fall’s resignation also follows this weekend’s handwringing over Chinese AI lab Moonshot’s new version of its open model Kimi, which performed competitively against flagship frontier models. The administration was weighing efforts to somehow ban Chinese open models, Axios reported . This sparked immediate debate and outrage over the weekend, including from Sacks , who argued that regulations shouldn’t be used as a protectionism strategy for U.S. proprietary AI labs.
While CAISI has released a few reports on the capabilities of Chinese open-weight models Z.ai’s GLM-5.2 and DeepSeek V4 Pro, it hasn’t talked much about its processes for testing. (Open weight means these models can be publicly downloaded and run locally, but its training code and datasets are not available). Since July 9, TechCrunch has sent multiple inquiries to both the DoC and NIST about how its LLM evaluations work and has not received a response.
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Pontos-chave
- A instabilidade na liderança do CAISI pode comprometer a criação de padrões técnicos essenciais para a IA.
- A ausência do CAISI no programa "Gold Eagle" indica uma possível fragmentação na regulamentação da IA nos EUA.
- O apelo por um corpo de padrões independente pode inspirar iniciativas semelhantes no Brasil.
- A competição entre modelos de IA destaca a necessidade de inovação constante no setor tecnológico.
- O Brasil deve observar as dinâmicas globais para se posicionar como líder em regulamentação de IA na América Latina.
Análise editorial
A rápida sucessão de diretores no Center for AI Standards and Innovation (CAISI) reflete um ambiente de incerteza e instabilidade na governança da inteligência artificial nos Estados Unidos, o que pode ter repercussões globais, incluindo no Brasil. A falta de continuidade na liderança pode dificultar a criação de padrões técnicos robustos e a avaliação de riscos de segurança cibernética, áreas críticas para o desenvolvimento responsável da IA. Para o setor tecnológico brasileiro, que busca se integrar ao mercado global de IA, a observação das políticas e diretrizes que emergem desse cenário se torna essencial.
Além disso, a recente criação de um programa de supervisão de segurança em IA pela Casa Branca, denominado "Gold Eagle", sem a participação do CAISI, levanta questões sobre a eficácia e a coordenação entre diferentes agências governamentais. Isso pode impactar a forma como as empresas brasileiras que trabalham com IA se preparam para atender a regulamentações internacionais, especialmente se os EUA adotarem uma abordagem fragmentada para a regulamentação da IA.
O apelo de Demis Hassabis, CEO da Google DeepMind, por um corpo de padrões independente e da indústria, sugere uma crescente pressão para que o setor privado assuma um papel mais ativo na definição de normas para a IA. Isso pode inspirar iniciativas semelhantes no Brasil, onde a colaboração entre empresas, governo e academia é crucial para o avanço da tecnologia de forma ética e segura. O Brasil deve observar essas dinâmicas e considerar como pode se posicionar como um líder em regulamentação de IA na América Latina.
Por fim, a competição acirrada entre modelos de IA, como evidenciado pela performance do modelo Kimi da Moonshot, destaca a necessidade de inovação constante e adaptação no setor. Para o Brasil, isso significa que as empresas devem não apenas acompanhar as tendências globais, mas também investir em pesquisa e desenvolvimento para garantir que possam competir em um mercado cada vez mais saturado e exigente.
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Fonte original:
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Este artigo foi curado e publicado pelo AIDaily como parte da nossa cobertura editorial sobre desenvolvimentos em inteligência artificial. O conteúdo é baseado na fonte original citada abaixo, enriquecido com contexto e análise editorial. Ferramentas automatizadas podem auxiliar tradução e estruturação inicial, mas a decisão de publicar, a revisão factual e o enquadramento de contexto seguem responsabilidade editorial.
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