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Nov 16, 2025 · 1h 19m · lennys-podcast

The Godmother of AI on jobs, robots & why world models are next | Dr. Fei-Fei Li

Dr. Fei-Fei Li · 56m spoken Lenny Rachitsky · 16m spoken
0:00 / 0:00
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In this in-depth interview with Lenny Rachitsky, AI pioneer Dr. Fei-Fei Li traces the evolution of machine learning from ImageNet to 3D spatial world models, advocating for human-centered AI governance, grounded scientific realism over hype, and purposeful technological innovation.

How this conversation actually went

Every chapter scored 0–10 on four independent dynamics. Hover any point for the reasoning behind the score. Lenny holds 23.2% of the talking time here. How this is scored →

Lenny as informed peer 2.9 Guest teaching 4.4 Guest disagreement 0.8 Lenny pushing back 0.6
05100:0020:0040:001:00:005:31–9:36 · Lenny as informed peer 3/10 Human-Centered AI and the Philosophy of Technology Lenny frames Fei-Fei as an unabashed AI optimist who thinks it will not take jobs, which Fei-Fei immediately gently clarifies by asserting she is a humanist rather than a utopian. Lenny accepts her framing and cites her congressional testimony respectfully.9:37–20:15 · Lenny as informed peer 2/10 The Evolution of Machine Learning and the ImageNet Breakthrough Fei-Fei delivers a comprehensive masterclass on the history of AI from the 1950s Dartmouth workshop through the AI winter to the pivotal role of ImageNet in 2012. Lenny serves primarily as an enthusiastic listener prompting her narrative.20:16–23:53 · Lenny as informed peer 4/10 Industry Stigma, Cultural Shifts, and Collective Progress in AI Lenny connects the ImageNet breakthrough to modern data companies like Scale and Surge, demonstrating industry knowledge. Fei-Fei emphasizes collective scientific effort over Silicon Valley's tendency to glorify solitary figures.23:55–29:49 · Lenny as informed peer 4/10 Demystifying AGI and Recognizing Current AI Deficiencies When Lenny asks how close we are to AGI, Fei-Fei rejects AGI as an ill-defined marketing term rather than a scientific one. Lenny presses further on whether current scaling paradigms suffice, prompting Fei-Fei to detail current AI deficiencies in spatial reasoning and scientific deduction.29:51–39:32 · Lenny as informed peer 3/10 Expanding Beyond Language to Spatial Intelligence and World Models Lenny asks for simplified conceptual frameworks around world models, which Fei-Fei expands to include interaction, spatial reasoning, and embodiment beyond text generation. Fei-Fei uses historical examples like the discovery of DNA to show why spatial intelligence matters.39:33–48:02 · Lenny as informed peer 4/10 Sponsor Message: Cinch After an ad break, Lenny conveys Ben Horowitz's question regarding why the bitter lesson alone will not solve robotics. Fei-Fei provides an in-depth technical explanation highlighting the mismatch between video training data and physical 3D interaction.48:03–1:01:01 · Lenny as informed peer 3/10 Introducing Marble: Generating Persistent 3D Interactive Worlds Lenny shares his user impressions of World Labs' new product Marble, noting specific rendering details. Fei-Fei explains the product's underlying frontier model architecture and diverse industrial and clinical use cases.1:01:02–1:10:23 · Lenny as informed peer 2/10 Building World Labs and Advice for Aspiring AI Talent Lenny explores Fei-Fei's decision-making across Stanford, Google Cloud, and startup founding. Fei-Fei gives guidance to early-career researchers about maintaining intellectual courage and focusing on mission over short-term optimization.1:10:23–1:14:24 · Lenny as informed peer 2/10 Stanford HAI, Interdisciplinary Research, and Global AI Policy Lenny asks about Fei-Fei's work at Stanford HAI (accidentally calling it HCI, which she politely corrects). Fei-Fei explains the interdisciplinary policy, research, and governance mission of the institute.1:14:25–1:19:06 · Lenny as informed peer 2/10 Preserving Human Dignity and Finding Your Role in the AI Era Fei-Fei delivers an impassioned closing message arguing that every profession maintains human dignity and agency in the AI era. Lenny wraps up the interview on a collaborative and appreciative note.5:31–9:36 · Guest teaching 4/10 Human-Centered AI and the Philosophy of Technology Lenny frames Fei-Fei as an unabashed AI optimist who thinks it will not take jobs, which Fei-Fei immediately gently clarifies by asserting she is a humanist rather than a utopian. Lenny accepts her framing and cites her congressional testimony respectfully.9:37–20:15 · Guest teaching 6/10 The Evolution of Machine Learning and the ImageNet Breakthrough Fei-Fei delivers a comprehensive masterclass on the history of AI from the 1950s Dartmouth workshop through the AI winter to the pivotal role of ImageNet in 2012. Lenny serves primarily as an enthusiastic listener prompting her narrative.20:16–23:53 · Guest teaching 3/10 Industry Stigma, Cultural Shifts, and Collective Progress in AI Lenny connects the ImageNet breakthrough to modern data companies like Scale and Surge, demonstrating industry knowledge. Fei-Fei emphasizes collective scientific effort over Silicon Valley's tendency to glorify solitary figures.23:55–29:49 · Guest teaching 6/10 Demystifying AGI and Recognizing Current AI Deficiencies When Lenny asks how close we are to AGI, Fei-Fei rejects AGI as an ill-defined marketing term rather than a scientific one. Lenny presses further on whether current scaling paradigms suffice, prompting Fei-Fei to detail current AI deficiencies in spatial reasoning and scientific deduction.29:51–39:32 · Guest teaching 5/10 Expanding Beyond Language to Spatial Intelligence and World Models Lenny asks for simplified conceptual frameworks around world models, which Fei-Fei expands to include interaction, spatial reasoning, and embodiment beyond text generation. Fei-Fei uses historical examples like the discovery of DNA to show why spatial intelligence matters.39:33–48:02 · Guest teaching 5/10 Sponsor Message: Cinch After an ad break, Lenny conveys Ben Horowitz's question regarding why the bitter lesson alone will not solve robotics. Fei-Fei provides an in-depth technical explanation highlighting the mismatch between video training data and physical 3D interaction.48:03–1:01:01 · Guest teaching 4/10 Introducing Marble: Generating Persistent 3D Interactive Worlds Lenny shares his user impressions of World Labs' new product Marble, noting specific rendering details. Fei-Fei explains the product's underlying frontier model architecture and diverse industrial and clinical use cases.1:01:02–1:10:23 · Guest teaching 4/10 Building World Labs and Advice for Aspiring AI Talent Lenny explores Fei-Fei's decision-making across Stanford, Google Cloud, and startup founding. Fei-Fei gives guidance to early-career researchers about maintaining intellectual courage and focusing on mission over short-term optimization.1:10:23–1:14:24 · Guest teaching 4/10 Stanford HAI, Interdisciplinary Research, and Global AI Policy Lenny asks about Fei-Fei's work at Stanford HAI (accidentally calling it HCI, which she politely corrects). Fei-Fei explains the interdisciplinary policy, research, and governance mission of the institute.1:14:25–1:19:06 · Guest teaching 3/10 Preserving Human Dignity and Finding Your Role in the AI Era Fei-Fei delivers an impassioned closing message arguing that every profession maintains human dignity and agency in the AI era. Lenny wraps up the interview on a collaborative and appreciative note.5:31–9:36 · Guest disagreement 2/10 Human-Centered AI and the Philosophy of Technology Lenny frames Fei-Fei as an unabashed AI optimist who thinks it will not take jobs, which Fei-Fei immediately gently clarifies by asserting she is a humanist rather than a utopian. Lenny accepts her framing and cites her congressional testimony respectfully.9:37–20:15 · Guest disagreement 0/10 The Evolution of Machine Learning and the ImageNet Breakthrough Fei-Fei delivers a comprehensive masterclass on the history of AI from the 1950s Dartmouth workshop through the AI winter to the pivotal role of ImageNet in 2012. Lenny serves primarily as an enthusiastic listener prompting her narrative.20:16–23:53 · Guest disagreement 1/10 Industry Stigma, Cultural Shifts, and Collective Progress in AI Lenny connects the ImageNet breakthrough to modern data companies like Scale and Surge, demonstrating industry knowledge. Fei-Fei emphasizes collective scientific effort over Silicon Valley's tendency to glorify solitary figures.23:55–29:49 · Guest disagreement 3/10 Demystifying AGI and Recognizing Current AI Deficiencies When Lenny asks how close we are to AGI, Fei-Fei rejects AGI as an ill-defined marketing term rather than a scientific one. Lenny presses further on whether current scaling paradigms suffice, prompting Fei-Fei to detail current AI deficiencies in spatial reasoning and scientific deduction.29:51–39:32 · Guest disagreement 1/10 Expanding Beyond Language to Spatial Intelligence and World Models Lenny asks for simplified conceptual frameworks around world models, which Fei-Fei expands to include interaction, spatial reasoning, and embodiment beyond text generation. Fei-Fei uses historical examples like the discovery of DNA to show why spatial intelligence matters.39:33–48:02 · Guest disagreement 1/10 Sponsor Message: Cinch After an ad break, Lenny conveys Ben Horowitz's question regarding why the bitter lesson alone will not solve robotics. Fei-Fei provides an in-depth technical explanation highlighting the mismatch between video training data and physical 3D interaction.48:03–1:01:01 · Guest disagreement 0/10 Introducing Marble: Generating Persistent 3D Interactive Worlds Lenny shares his user impressions of World Labs' new product Marble, noting specific rendering details. Fei-Fei explains the product's underlying frontier model architecture and diverse industrial and clinical use cases.1:01:02–1:10:23 · Guest disagreement 0/10 Building World Labs and Advice for Aspiring AI Talent Lenny explores Fei-Fei's decision-making across Stanford, Google Cloud, and startup founding. Fei-Fei gives guidance to early-career researchers about maintaining intellectual courage and focusing on mission over short-term optimization.1:10:23–1:14:24 · Guest disagreement 0/10 Stanford HAI, Interdisciplinary Research, and Global AI Policy Lenny asks about Fei-Fei's work at Stanford HAI (accidentally calling it HCI, which she politely corrects). Fei-Fei explains the interdisciplinary policy, research, and governance mission of the institute.1:14:25–1:19:06 · Guest disagreement 0/10 Preserving Human Dignity and Finding Your Role in the AI Era Fei-Fei delivers an impassioned closing message arguing that every profession maintains human dignity and agency in the AI era. Lenny wraps up the interview on a collaborative and appreciative note.5:31–9:36 · Lenny pushing back 1/10 Human-Centered AI and the Philosophy of Technology Lenny frames Fei-Fei as an unabashed AI optimist who thinks it will not take jobs, which Fei-Fei immediately gently clarifies by asserting she is a humanist rather than a utopian. Lenny accepts her framing and cites her congressional testimony respectfully.9:37–20:15 · Lenny pushing back 0/10 The Evolution of Machine Learning and the ImageNet Breakthrough Fei-Fei delivers a comprehensive masterclass on the history of AI from the 1950s Dartmouth workshop through the AI winter to the pivotal role of ImageNet in 2012. Lenny serves primarily as an enthusiastic listener prompting her narrative.20:16–23:53 · Lenny pushing back 1/10 Industry Stigma, Cultural Shifts, and Collective Progress in AI Lenny connects the ImageNet breakthrough to modern data companies like Scale and Surge, demonstrating industry knowledge. Fei-Fei emphasizes collective scientific effort over Silicon Valley's tendency to glorify solitary figures.23:55–29:49 · Lenny pushing back 2/10 Demystifying AGI and Recognizing Current AI Deficiencies When Lenny asks how close we are to AGI, Fei-Fei rejects AGI as an ill-defined marketing term rather than a scientific one. Lenny presses further on whether current scaling paradigms suffice, prompting Fei-Fei to detail current AI deficiencies in spatial reasoning and scientific deduction.29:51–39:32 · Lenny pushing back 1/10 Expanding Beyond Language to Spatial Intelligence and World Models Lenny asks for simplified conceptual frameworks around world models, which Fei-Fei expands to include interaction, spatial reasoning, and embodiment beyond text generation. Fei-Fei uses historical examples like the discovery of DNA to show why spatial intelligence matters.39:33–48:02 · Lenny pushing back 1/10 Sponsor Message: Cinch After an ad break, Lenny conveys Ben Horowitz's question regarding why the bitter lesson alone will not solve robotics. Fei-Fei provides an in-depth technical explanation highlighting the mismatch between video training data and physical 3D interaction.48:03–1:01:01 · Lenny pushing back 0/10 Introducing Marble: Generating Persistent 3D Interactive Worlds Lenny shares his user impressions of World Labs' new product Marble, noting specific rendering details. Fei-Fei explains the product's underlying frontier model architecture and diverse industrial and clinical use cases.1:01:02–1:10:23 · Lenny pushing back 0/10 Building World Labs and Advice for Aspiring AI Talent Lenny explores Fei-Fei's decision-making across Stanford, Google Cloud, and startup founding. Fei-Fei gives guidance to early-career researchers about maintaining intellectual courage and focusing on mission over short-term optimization.1:10:23–1:14:24 · Lenny pushing back 0/10 Stanford HAI, Interdisciplinary Research, and Global AI Policy Lenny asks about Fei-Fei's work at Stanford HAI (accidentally calling it HCI, which she politely corrects). Fei-Fei explains the interdisciplinary policy, research, and governance mission of the institute.1:14:25–1:19:06 · Lenny pushing back 0/10 Preserving Human Dignity and Finding Your Role in the AI Era Fei-Fei delivers an impassioned closing message arguing that every profession maintains human dignity and agency in the AI era. Lenny wraps up the interview on a collaborative and appreciative note.

speaking balance: gold is Lenny, purple is the guest (3 minute bins)

0:00 · Lenny 58.8% · guest 41.2%0:00 · Lenny 58.8% · guest 41.2%3:00 · Lenny 99.2% · guest 0.8%3:00 · Lenny 99.2% · guest 0.8%6:00 · Lenny 31.7% · guest 68.3%6:00 · Lenny 31.7% · guest 68.3%9:00 · Lenny 34.8% · guest 65.2%9:00 · Lenny 34.8% · guest 65.2%12:00 · Lenny 0% · guest 100%12:00 · Lenny 0% · guest 100%15:00 · Lenny 0% · guest 100%15:00 · Lenny 0% · guest 100%18:00 · Lenny 19.4% · guest 80.6%18:00 · Lenny 19.4% · guest 80.6%21:00 · Lenny 23.2% · guest 76.8%21:00 · Lenny 23.2% · guest 76.8%24:00 · Lenny 29% · guest 71%24:00 · Lenny 29% · guest 71%27:00 · Lenny 18.1% · guest 81.9%27:00 · Lenny 18.1% · guest 81.9%30:00 · Lenny 18.4% · guest 81.6%30:00 · Lenny 18.4% · guest 81.6%33:00 · Lenny 8.1% · guest 91.9%33:00 · Lenny 8.1% · guest 91.9%36:00 · Lenny 22.6% · guest 77.4%36:00 · Lenny 22.6% · guest 77.4%39:00 · Lenny 67.5% · guest 32.5%39:00 · Lenny 67.5% · guest 32.5%42:00 · Lenny 0% · guest 100%42:00 · Lenny 0% · guest 100%45:00 · Lenny 8.6% · guest 91.4%45:00 · Lenny 8.6% · guest 91.4%48:00 · Lenny 5.9% · guest 94.1%48:00 · Lenny 5.9% · guest 94.1%51:00 · Lenny 31.5% · guest 68.5%51:00 · Lenny 31.5% · guest 68.5%54:00 · Lenny 11.6% · guest 88.4%54:00 · Lenny 11.6% · guest 88.4%57:00 · Lenny 19.3% · guest 80.7%57:00 · Lenny 19.3% · guest 80.7%1:00:00 · Lenny 23.8% · guest 76.2%1:00:00 · Lenny 23.8% · guest 76.2%1:03:00 · Lenny 24.6% · guest 75.4%1:03:00 · Lenny 24.6% · guest 75.4%1:06:00 · Lenny 4.3% · guest 95.7%1:06:00 · Lenny 4.3% · guest 95.7%1:09:00 · Lenny 16% · guest 84%1:09:00 · Lenny 16% · guest 84%1:12:00 · Lenny 14.9% · guest 85.1%1:12:00 · Lenny 14.9% · guest 85.1%1:15:00 · Lenny 0% · guest 100%1:15:00 · Lenny 0% · guest 100%1:18:00 · Lenny 49.8% · guest 50.2%1:18:00 · Lenny 49.8% · guest 50.2%
Sharpest disagreement ▶ 24:13 Rejection of the term AGI

Fei-Fei dismisses the premise of debating AGI timelines, categorizing AGI as a marketing gimmick rather than a rigorous scientific concept.

 Hardest push from Lenny ▶ 26:44 Pressing on model scaling versus new breakthroughs

Lenny challenges the assumption that scaling existing transformers with compute and data will be sufficient to achieve major cognitive leaps.

 Biggest teaching moment ▶ 42:00 The fundamental mismatch in robotics training data

Fei-Fei breaks down why text models have aligned objectives while robotics lacks 3D action data, educating the audience on the physical constraints of embodied AI.

 Lenny holds their own ▶ 29:00 Citing Demis Hassabis's Einstein benchmark

Lenny demonstrates deep subject mastery by referencing Demis Hassabis's benchmark of whether models can independently deduce groundbreaking physics laws.
the scores for every segment, with the reasoning behind each
ChapterTopicLenny as informed peerGuest teachingGuest disagreementLenny pushing backWhy
Human-Centered AI and the Philosophy of Technology 3421 Lenny frames Fei-Fei as an unabashed AI optimist who thinks it will not take jobs, which Fei-Fei immediately gently clarifies by asserting she is a humanist rather than a utopian. Lenny accepts her framing and cites her congressional testimony respectfully.
The Evolution of Machine Learning and the ImageNet Breakthrough 2600 Fei-Fei delivers a comprehensive masterclass on the history of AI from the 1950s Dartmouth workshop through the AI winter to the pivotal role of ImageNet in 2012. Lenny serves primarily as an enthusiastic listener prompting her narrative.
Industry Stigma, Cultural Shifts, and Collective Progress in AI 4311 Lenny connects the ImageNet breakthrough to modern data companies like Scale and Surge, demonstrating industry knowledge. Fei-Fei emphasizes collective scientific effort over Silicon Valley's tendency to glorify solitary figures.
Demystifying AGI and Recognizing Current AI Deficiencies 4632 When Lenny asks how close we are to AGI, Fei-Fei rejects AGI as an ill-defined marketing term rather than a scientific one. Lenny presses further on whether current scaling paradigms suffice, prompting Fei-Fei to detail current AI deficiencies in spatial reasoning and scientific deduction.
Expanding Beyond Language to Spatial Intelligence and World Models 3511 Lenny asks for simplified conceptual frameworks around world models, which Fei-Fei expands to include interaction, spatial reasoning, and embodiment beyond text generation. Fei-Fei uses historical examples like the discovery of DNA to show why spatial intelligence matters.
Sponsor Message: Cinch 4511 After an ad break, Lenny conveys Ben Horowitz's question regarding why the bitter lesson alone will not solve robotics. Fei-Fei provides an in-depth technical explanation highlighting the mismatch between video training data and physical 3D interaction.
Introducing Marble: Generating Persistent 3D Interactive Worlds 3400 Lenny shares his user impressions of World Labs' new product Marble, noting specific rendering details. Fei-Fei explains the product's underlying frontier model architecture and diverse industrial and clinical use cases.
Building World Labs and Advice for Aspiring AI Talent 2400 Lenny explores Fei-Fei's decision-making across Stanford, Google Cloud, and startup founding. Fei-Fei gives guidance to early-career researchers about maintaining intellectual courage and focusing on mission over short-term optimization.
Stanford HAI, Interdisciplinary Research, and Global AI Policy 2400 Lenny asks about Fei-Fei's work at Stanford HAI (accidentally calling it HCI, which she politely corrects). Fei-Fei explains the interdisciplinary policy, research, and governance mission of the institute.
Preserving Human Dignity and Finding Your Role in the AI Era 2300 Fei-Fei delivers an impassioned closing message arguing that every profession maintains human dignity and agency in the AI era. Lenny wraps up the interview on a collaborative and appreciative note.

Statements from this episode (16)

Opinion
Li: Modern AI has met Alan Turing's standard for a thinking machine
“And of course he has a specific way of testing this concept of thinking machine, which is a conversational chatbot, which to his standard, we now have a thinking machine”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 11:49
Assertion Supported
Li: ImageNet curated 15 million images across 22,000 concepts
“We curated very carefully, fifteen million images on the internet, created a taxonomy of 22,000 concepts, borrowing other researchers' work, like linguists' work on WordNet, and it's a particular way of dictionary words.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 18:08
Insight
Li: Big data, neural networks, and GPUs remain core to modern AI
“If you look at the ingredients of what brought chat GPT to the world, technically is still use these three ingredients. Now it's internet scale data. Mostly texts is a much more complex neural network architecture than. But it's still neural network and a lot …”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 19:42
Assertion Not checkable as stated
Li: Major tech companies avoided the term AI in 2015 and 2016
“Like, I do remember the companies, the tech companies, I, I'm not gonna name names, but I will, I was in a conversation in one of the early days, I think it is in the middle of 2015, middle of 20 16 some tech companies avoid using the word AI because they were…”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 21:28
Opinion
Li: AGI is more of a marketing term than a scientific one
“I feel AGI is more a marketing term than a scientific term. As a scientist and technologist, AI is my North Star, is my field's North Star, and I'm happy people call it whatever name they want to call it.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 25:48
Opinion
Dr. Fei-Fei Li: Scaling laws alone are not enough; AI needs innovation
“I think scaling laws of more data, more GPUs and bigger current model architecture is there's still a lot to be done there, but I absolutely think we need to innovate more.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 26:48
Assertion Supported
Li: Current AI models fail to count chairs in simple office videos
“Today, you take a model And run it through a video of a couple of office rooms and ask the model to count the number of chairs. And this is something a toddler could do, or maybe, maybe a elementary school kid could do. And AI could not do that, right?”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 27:29
Assertion Supported
Li: Current AI cannot independently derive Newton's 17th-century laws of motion
“Let's give AI all the data, including modern instruments data of celestial bodies, which Newton did not have, and give it to that, and just ask AI to create the sixth, 17th century set of equations on the laws of bodily movements. Today's AI cannot do that.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 29:26
Opinion
Li: World models and spatial intelligence are essential for embodied AI
“I think world modeling and spatial intelligence Is a key missing piece of embodied AI.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 36:25
Insight
Li: Robotics trails LLMs because training data lacks 3D physical actions
“In in language, because they have this perfect setup where their training data are in words, eventually tokens, and then they produce a model that outputs words. So you have this perfect alignment between what you hope to get, which we call objective function,…”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 43:21
Insight
Li: Self-driving cars on 2D surfaces are simpler than 3D robotics
“And self-driving cars are much simpler robots. They're just metal boxes running on two D surfaces. And the goal is not to touch anything. Robot is three D things running in three D world. And the goal is to touch things.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 46:22
Opinion
Li: Spatial intelligence and world models are as important as LLMs
“We believe that spatial intelligence and world modeling is As important, if not more, to language models and complementary to language models.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 48:43
Assertion Not checkable as stated
Li: World Labs created the first generative model outputting genuine 3D worlds
“We've spent a year and plus Building the world's first generative model that can output genuinely three D worlds.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 49:16
Assertion Not checkable as stated
Li: Virtual production directors reported Marble reduced production time by 40x
“We were collaborating with those technical artists and directors, and they were saying this has cut our production time by 40 X.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 53:46
Assertion Contradicted
Li: World Labs runs real-time 3D video generation on a single H100
“Just a couple of weeks ago, we rolled out the world's first real-time demoable real-time video generation on a single H 100 GPU.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 59:41
Assertion Not checkable as stated
Li: Silicon Valley had no dialogue with Washington or Brussels in 2018
“When we started HAI, I realized that Silicon Valley did not talk to Washington, DC and, or Brussels or other parts of the world.”
Dr. Fei-Fei Li Nov 16, 2025 ▶ 1:13:15
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