
Artificial Intelligence, though more than a half-century old as a technology, has occupied front-of-mind cultural consciousness for only a few years, since ChatGPT. Yet AI is already profoundly disliked and distrusted in both media meme and apparently in public opinion.
The techlash against the internet and then social media took at least two decades to ferment, while the AI backlash feels more like an instant reflex. Fifty-seven percent of Americans say AI’s risks outweigh its benefits, making it less popular even than ICE! Half fret about its ill effects on creativity, meaningful relationships, news, and elections. In rare consensus, Republicans and Democrats alike are NIMBYing data centers across the nation. A German digital conference offers a workshop in poisoning AI data. The FT declares “a new Luddite movement.” And commencement speakers daring to utter the letters A and I are sure to be booed.
It is, however, difficult to discern true public opinion from performative public opinion polls, as polls reflect media’s self-fulfilled prophesies. In reality, even as views of AI sour, the public’s use of the tools soars: more than half now employ it to, for example, research topics of interest, a thirty-eight percent increase in a year.
Who or what is to blame for AI’s bad rep? In The Web We Weave, I assert that the problem is less the AI than certain AI boys who make up the newest oligopoly. Look no farther than the now-finished OpenAI trial: technology’s Godzilla v. Mothra, unlikeable Musk v. unreliable Altman. Now add the creepy dramatis potens of West Coast billionaires (before they all dodge taxes and move to Miami) — Thiel, Andreessen, Karp, Zuck, Ellison, Bezos — plus the moral entrepreneurs trying to pump and exploit technofear (Tristan Harris, Eliezer Yudkowsky, Nick Bostrom, William MacAskill, Dustin Moskovitz) — and it’s no wonder people profess dislike for their algorithmic progeny. And it doesn’t help relations when they dismiss us humans as “meat computers.”
Yet AI is amazing, constantly wowing even the skeptical with its feats and tricks. Every week on the two (yes, two) AI podcasts I co-host — AI Inside and Intelligent Machines — I both both marvel at and fault the capabilities and implications of this new and powerful technology. Because I am also editing a new Bloomsbury book series about AI and humanity, I am constantly trying to get my head around how AI works, what it can and cannot do, what its benefits and perils might be, and what its creation, use, and product says about us as humans.
To learn more about AI, I do not turn to the boys above. I turn to critics, yes; there are many. But to understand AI’s innards I find it best to look to a few of the field’s master communicators and teachers, notably Jensen Huang, founder and CEO of Nvidia, and Yann LeCun, NYU professor and founder of the new AMI Labs. Unlike AI’s blind proselytizers, they do not market superheated claims of superintelligence a day away, nor in the doomsters’ dire warnings of AI-combusted human extinction.
Below are videos of each; dip in and you will learn more about AI.
Each man has clear economic interest in the success of AI; Huang leads the world’s largest corporation by market valuation and LeCun has raised $1 billion to envision a next generation of AI, past LLMs. Each tries to present realistic and credible perspectives on the present and near-future state of the art and technic of AI. Each calls bullshit on others when warranted. The most important reason I watch these two is because in their public appearances, each educates, addressing multilayered constituencies: customers, partners, investors, employees, policymakers, journalists, students, and the public.
I am fascinated by their presentation skills because, as a journalist, we’re supposed to be communicators. They also interest me because I am a visiting professor at Stony Brook University, where I work with the Alan Alda Center for Communicating Science (see this Wall Street Journal report explaining their work). I am trying to dissect these technologists’ skills at communication to see how others might communicate technology more effectively and honestly for more informed and necessary public discourse.
All this is why I became a loyal connoisseur of Huang’s keynotes. They are bravura solo performances: two hours or longer on stage before appreciative industry audiences, presenting new product announcements while holding up and showing off chips and boards that are, in truth, as enigmatic in their appearance and ways as an obelisk to a 2001 ape. All the while, he explains Nvidia’s accomplishments and position in the field and the market. It shouldn’t be mesmerizing, but to me, it is.
As a student of Huang’s addresses, I soon learned that in each, he has a new lesson to impart, in addition to promoting a new chip or software or deal. The first talk that really hooked me was his Nvidia developers’ conference keynote of March, 2024. In it, he helped me begin to grasp the scale of AI technology.
For a next book project on the birth and death of mass media, I’ve been reading up on the invention of the the triode vacuum tube and amplifier in 1906, which enabled radio as well as the loudspeaker. Four decades later, the tube was superseded by the transistor and eventually, in 1958, the chip. (BTW, if you’re interested in that history, I recommend Conquering the Electron: The Geniuses, Visionaries, Egomaniacs, and Scoundrels Who Built Our Electronic Age by Derek Cheung and Eric Brach.)
On stage in 2024, Huang announces a new, bigger chip — a GPU or graphics processing unit named Blackwell — that holds 208 *billion* transistors shunting electrons and with them logic and knowledge. Then AI-generated animation on the giant screen behind him multiplies those chips in racks and in turn multiplies racks—each component clicking into place with satisfying, synthetic clunks — to add up to an entire data center populated with 32,000 GPUs — thus six quadrillion transistors. Mind blown. Here is that moment, beginning at minute 26:
Huang’s lesson in that keynote is the value of scale, for the wonders of generative AI have been made possible not only by Google’s theoretical breakthrough in 2017 but by the massive computing power (“compute” in their abbreviated argot) powered by Nvidia’s processors (and megawatts).
In that and subsequent keynotes, Huang imparts more lessons. He lectures on the economy of tokens — generative AI’s numerical abstractions of words, for instance, which LLMs create to calculate their predictions. He explains the need to manufacture tokens at ever-high speed and ever-lower cost in the data centers he characterizes as factories (of tokens). He declares the end of Moore’s Law, which since the 1960s predicted the rate of growth of the number of transistors on a chip: a 100x increase in computing power every ten years. Huang touts instead what he calls accelerated computing, boasting that Nvidia’s chips and its CUDA software platform have multiplied computing power 1,000x in just eight years.
I am transfixed as he animates the idea of digital twins: how AI, trained on a massive number of possible scenarios, computes every alternative that could next face a factory robot or an autonomous car. (I am so taken with the idea of the digital twin that I keep imaging there’s a novel in it: the machine that predicts the consequences of every choice we face.) It is clear from what he shows off that Huang believes robotics represents the next industrial explosion.
Last fall, Huang gave a keynote to Nvidia’s Washington conference, in which he deftly thanks the Trump administration and Department of Energy for helping his industry’s cause, without sacrificing a gold bar or too much dignity. Here he wants to teach policymakers about the needs of the industry that is holding up our economy even with tariffs and war.
It turns out Huang is not just a presenter but also an effective debater. See him here making his case for allowing sales to China as a benefit to American industry, in this podcast with Dwarkesh Patel.
Huang took a company that began making chips to power computer — and particularly game — graphics and positioned it to be at the heart of the AI explosion, its market valuation exceeding $5 trillion. If anything would give a CEO license to make outlandish claims and predictions, that would. But Huang doesn’t talk about flying to Mars. Instead, he tries to explain the value of what is being built. To his DC audience, he asserts that “AI is not a tool. AI is work. AI is work that can use tools.” I think that is new.
If you’re hooked now like me, watch his latest California keynote, in which he talks about the agentic age suddenly upon us.
Yann LeCun is a different kind of communicator. He is first and foremost a researcher and educator, secondarily now an entrepreneur. I have tried to read and watch his presentations to NYU students and I must confess that not too long in I regret my success at dodging higher mathematics courses … in high school. Yet LeCun is also an effective communicator even to humanities dolts like me. Here he is a year ago patiently explaining his perspective on AI to Jason Howell and me on our podcast, AI Inside:
Getting to speak with LeCun is a privilege, for he is one of AI’s godfathers (though, as he says, living in New Jersey, he’d prefer a different honorific). Among his many accomplishments and recognitions, he has received— alongside Yoshua Bengio and Geoffrey Hinton — the 2018 Turing Award for work on deep learning.
What I value most about listening to LeCun is that he does not suffer bullshit, whether that is claims of imminent superintelligence or prognostication of inevitable disaster for jobs or humankind itself. As a clear-eyed academic, he never shies away from debate or disagreement rooted in research.
Lately, LeCun has developed a reputation as a contrarian regarding LLMs, though his is a view he has long held. While the putative Wunderkinder of the infant industry — the leaders of OpenAI, Anthropic, et al — insist that all they need is ever-more compute to reach their Everest of so-called artificial superintelligence (any day now), LeCun argues persuasively — to me — that LLMs, though impressive and useful, are essentially a dead-end, for they traffic in text, which represents a finite sphere of representation of life.
LeCun has argued instead for devising world models to teach the machine the sort of experiential constraints a toddler or a kitten can learn: about gravity acting upon a ball falling off a table, where it persists, though now unseen. If one wants to build AI for the real world, one must conquer Moravec’s paradox: that “it is comparatively easy to make computers exhibit adult level performance on intelligence tests or playing checkers, and difficult or impossible to give them the skills of a one-year-old when it comes to perception and mobility.” This from LeCun’s presentation:

For an effective starter course in LeCun’s worldview, I highly recommend watching him debate LLMs vs. world models with Google DeepMind’s Adam Brown, in this Scientific Controversies discussion moderated by Janna Levin at Brooklyn’s Pioneer Works.
In 2022, LeCun published a paper proposing a “path towards autonomous machine intelligence… enabling them to reason, predict, and plan at multiple time horizons… [as] intelligent agents.” Or as he sums up the goal: to work with common sense. Here, too, is a 117-slide presentation he made to NYU students explaining his ideas. I’ll admit I am soon lost in the formulae, though the lecture isn’t meant for the likes of me. This is how he instructs students, fellow computer scientists, competitors — and now funders, as LeCun has founded AMI Labs to build advanced machine intelligence on his model for the future, which he calls Joint-Embedding Predictive Architecture (JEPA).
I will do a bad job summarizing his proposal; that’s why I offer the videos below, for they are far more effective at communicating the ideas than I am. But in short, LeCun describes using photos and videos from real life to train models — or to train them to train themselves — so they understand representations of the world and consequences of actions, rather than trying to predict every pixel or merely ape human teachers. That is, his method can discern, say, a ball as a ball and predict the result of action on it. JEPA does this by encoding an original image alongside a corrupted version so the system can, in LeCun’s words, “predict the representation of the original image from the representation of the corrupted one.”
The implications are many. LeCun says JEPA will lead to models that can undertake a wider variety of tasks with less training, for they do not need to be taught every possible permutation of a situation. These models are also more efficient, for they concentrate attention on particular change — a ball thrown — rather than every change — countless leaves rustling in the scene — and need not predict the progress of every pixel.
He says these systems will be capable of thinking. Unlike LLMs, they won’t — at first — listen or talk, which might make them less flashy than ChatGPT or Claude: fewer neat parlor tricks to convince journalists that they’re in love. But LeCun convinces me that being able to understand the world and its constraints and to operate in it is table stakes for the next paradigm of AI. This is what it will take for AI to deliver value to industry, including in robotics, industrial control, and health care. He also is persuasive when he argues that his framework will be safer than LLMs, for rather than producing the general machines that their creators claim can do everything better than all of us — as the AI boys insist — JEPA will produce specialized agents with bounded tasks, each built to “anticipate the consequences of its own actions.” JEPA won’t take over the world. LLMs, LeCun says, can be dangerous; haven’t we learned that?
He says he is building “AI for the real world” — that is, world models — for as it turns out, “reality is way more complicated than language.”
If you want to dig into LeCun’s view of AI’s future (and understand why, on my podcasts, I regularly declare myself on Team Yann), here is a series of videos, stepping up in complexity. The first is a casual conversation with Jacob Effron:
Next is a more technical and deeper explanation of JEPA from Welch Labs. I had to watch a few times to understand some (not all) of it. It is an excellent primer (and here is part 2.
And here is a video in which LeCun does not speak. It is a victory lap for his vision of building reasoning: building a system that can solve any problem given a set of constraints rather than just generating a next token (or writing a brute-force, old-fashioned program). The host, Ksenia Se of Turing Post, extols JEPA (but notes that it is “quite a tragic name for a beautiful idea”).
Note the title of the video: “Yann was right.” To which Yann added when he shared it on his socials, “But of course I was.” (That’s another reason I like both these men; they have a sense of humor that is not evident in the other AI boys.)
What I most appreciate about LeCun is the humility inherent in a scientist’s worldview. The generative AI companies display the hubris of believing they are almost done building the general machine that can take over every human task, or destroy us in the process. LeCun is, instead, on a scientific journey of discovery and iteration. He knows there is yet much work to be done.
I also greatly respect his support for open source. When he was at Meta, heading a lab and then advising on AI, he fought for it to release its Llama models openly. This is how academics and scholars work: inviting challenges, teaching others, and collaborating and learning together.
AI is here to stay. There is no wishing or protesting or regulating or boycotting it away. We must not surrender its future and ours to a few oligarchic billionaires. The only way we can have a voice in its direction is to understand it better. And the only way we can understand it is by listening critically to the people actually building it. That is why I want to see technologists better able to communicate about technology — so we can debate it and its effects. That’s why I’m studying these two masters of communication.












