Gary Marcus
Founder & Executive Chairman, Robust AI. On 2 shows, 5 appearances. The Shows tab opens the full record on each.
scientistauthoracademicfounderexecutive@garymarcus ↗garymarcus.com ↗Wikipedia ↗
Gary Marcus is a cognitive scientist and entrepreneur widely known for his critiques of large language model scaling limits and advocacy for neuro-symbolic hybrid architectures. He founded Geometric Intelligence, which was acquired by Uber in 2016, and Robust AI, and is the author of books including Rebooting AI and Taming Silicon Valley.
2shows
5appearances
83statements
13resolved
13supported
0contradicted
100%fully supported
18said about them ↓ Everything Gary Marcus said on any show that made the record, most notable first. Each card names its show and opens the statement there.
Marcus: AI is not close to fooling experts in hour-long conversations
“Well, I don't think we're that close to a system that's going to be able to, you know, if I have an hour with it, let's say just to be conservative. I don't think we're that close to a victory there.”
Marcus: AI deployment will not pause unless Congress steps in
“The bottom line is what's driving it, and it's not that likely, unless Congress steps in, that there will be a pause.”
Marcus: Generative AI Art Is Fully Derivative Sampling on Steroids
“And what these systems are doing is kind of like amazing enhanced version of sampling where you don't even recognize what the samples are anymore. It's all derivative.”
Marcus: Google's LaMDA Has No Sensors Perceiving the Physical World
“Lambda actually has fewer sensors than my watch. My watch has a lot of sensors and lamb doesn't really have anything sensing the real world, except for its linguistic input.”
Marcus: Building sentient machines is possible, but we do not know how
“I am in no way arguing that it is not possible to build a sentient machine. I don't think we know how to do it, and I don't think we're clear enough on what it would consist of, but I'm not making the argument that it's impossible.”
Marcus: Future AGI systems will require internal world models
“So I'm pretty confident that we'll have models of the world, internal ideas about how the world works. I don't see how to build an AI without it.”
Marcus: LLM-powered misinformation as a service will soon be widespread
“If what you want to do is to put out like 10,000 versions of something on Twitter, something untrue and find one that sticks, then misinformation as a service, which is how they might call it in the tech industry is a pretty damn powerful technique. And if it …”
Marcus: No companies using GPT technology are breakout successes
“So yeah, there's a ton of companies that are trying to use GPT's technology. I don't know any of them that are You know, breakout successes.”
Marcus: Generative image software is relatively easy to copy
“The issue there is the software itself is relatively easy to copy.”
Marcus: Deep learning achievements are limited to perceptual classification
“These successes are examples of one thing. They're all examples of what a cognitive psychologist would call perceptual classification, and that's part of what we do as intelligent human beings, but it's not all that we do.”
Marcus: Deep learning relies primarily on texture, not shape, for recognition
“Deep learning, it mostly cares about texture.”
Marcus: Building human-level AI requires studying cognitive development in children
“If we want to build machines that are as smart as people, we should start by studying small people.”
Marcus predicts deeply neurally inspired AI models are 50 years away
“50 years from now, we will know how to build really deeply neurally inspired models, but right now we have to settle for cognitive science, for psychology, linguistics, fields like that, where we can actually get some insight now before we unravel the brain.”
Marcus: Autonomous driverless cars can drive in Palo Alto but not Manhattan
“In fact, they can drive in Palo Alto, but they can't drive in Manhattan.”
Marcus: AI has made little progress toward strategic general game players
“There's still been very little progress in building a general game player that can play games that require strategy or insight, complex three-dimensional graphics.”
Marcus: AI is not close to achieving strong, human-level intelligence
“But we're not there yet. We're not even close.”
Google's Peter Norvig estimated strong AI is worth up to $2T annually
“I asked Peter Norvik, who at the time was director of research at Google I said, you know, how much money would it really be worth if you could have strong AI? And he wrote back about five minutes later, I'm suddenly a much louder, I think. And estimated somet…”
Marcus: Deep learning models struggle with low-frequency edge cases
“People get very excited every time there's a new deep learning result. But it's always the case of doing better on the high frequency data than the low frequency data.”
Marcus: AI researchers should look to child cognitive development for AGI clues
“So, where I think we should start looking for clues, in a nutshell, is children.”
Marcus: Information retrieval fails at state updates that human-level AI requires
“If you have human-level intelligence, you can make sense of that, but if you just memorize stuff off the web and do information retrieval, you could actually get stuck there.”
Gary Marcus: No great machine learning technique exists for natural language
“And so, as a result, there is no great machine learning technique in natural language.”
Marcus: Facebook M relies mostly on human operators rather than AI
“Facebook's new M service, which they have not rolled out at scale has humans on the back end. There's a little bit of AI in there, but it's mostly, ah, human beings, which is why they haven't rolled it out for a billion customers. They don't have enough human …”
Marcus: Vals AI benchmark shows LLM accuracy under 10% on financial charts
“Where they looked at things like, can you pull out a chart based on a series of financial statements, SEC statements from a bunch of companies and these systems all claimed to do it, but accuracy was under 10%. And overall on this new benchmark, accuracy was a…”
Marcus: AI Language Models Produce Convincing Text That Is Often Untrue
“If you talk about another domain, like language synthesis, it turns out that current systems can make very convincing language, but it's often bullshit. That doesn't matter in the same way in art. So in, if you have a system make up a news story, even if it's …”
The other half of the tape: Gary Marcus's own voice is left out of every number here. Other people bring the name up 18 times in 10 episodes across the shows. every mention, with the transcript →
Who brings them up most Alex Kantrowitz 9Ranjan Roy 3George Davis 3Matt Turck 1Geoffrey Hinton 1Ed Zitron 1
Every mention by year
tap a year for its mentions
Big Technology 14
the MAD Podcast 4
2026 8 mentions in 4 episodes 2 per episode
- OpenAI’s User Growth Miss, Musk vs. Altman In Court, Prediction Market Ban
- SpaceX’s IPO Triumph, Anthropic’s Fable Fumble, OpenAI’s Price War
- AI Pioneer Geoffrey Hinton: AI Is Conscious, Superintelligence is Coming, And We Should Be Worried
- AI Backlash Intensifies, Nvidia GTC Preview, Meta’s Embarrassing Delay
- every mention in 2026, scene by scene →
2025 6 mentions in 4 episodes 2 per episode
- Are AI's Economics Unsustainable? — With Ed Zitron
- Sam Altman’s Gentle Singularity, Zuck’s AI Power Play, Burning Of The Waymos
- The ‘AI-First Company,’ Big Tech Mega Earnings, NVIDIA vs. Anthropic
- Is Anthropic’s Claude AI Conscious, Shopping in ChatGPT, Systrom vs. Zuck
- every mention in 2025, scene by scene →
2023 1 mention in 1 episode
One line per show, most statements first. The link opens Gary's full record on that show: the calibration, argument clarity, speaking style and every statement made there.
| Show | Role there | Eps | Statements | Record | |
|---|---|---|---|---|---|
| BIG TECHNOLOGYLEDGER | Founder & Executive Chairman, Robust AI | 3 | 45 | 100% 9/9 | full record on Big Technology → |
| MADLEDGER | Founder & Executive Chairman, Robust AI | 2 | 38 | 100% 4/4 | full record on the MAD Podcast → |