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AI Communism

July 27, 2026 | No Comments
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In response to the spectre of Chinese open-weight AI models haunting America, Dean Ball, OpenAI’s director of strategic futures [are there directors of strategic pasts?], issued a warning more shrill even than the New York Post’s alerts about Mamdani’s bus-and-grocery socialism. 

One probable outcome of an open-weight-model-dominant world is full AI communism, which is precisely what China proposes: rather than a market product, AI is a “public good” which will ultimately be provided by the state as a kind of “digital public infrastructure.” This future strikes me as a dystopian hellscape, but I’ve never met an open-weight models advocate who doesn’t ultimately concede this is where things end.

As Jason Howell and I discussed this on our podcast, AI Inside, we found ourselves asking, What would be so wrong with AI communism? What if there were AI for all? Eh, comrades?

Imagine all that could be created, often for free, on top of open AI models: how many universities and researchers, startups and entrepreneurs, artists and students could better afford the technology. Imagine the greater control that comes from locally hosted models. Imagine the greater transparency and security that comes with openness. And imagine the power — pricing and political — that could be wrested from the new AI giants as they barrel toward their IPOs…which, of course, is precisely what frightens them. But you need not imagine all this, for open-weight models are affording these opportunities now. 

By championing open AI, we could atone for past sins (including mine) of insufficiently supporting openness in the first phase of the internet, leading to the fencing of the commons. Open AI may be our best and only defense against its oligarchic domination. As the wise John Palfrey, head of the MacArthur Foundation, just said, “This is a design moment — for the technologies, for the way we relate to one another across geographic and political boundaries, and for what the future looks like.”

A week after Ball’s cri de code, Nvidia founder and CEO Jensen Huang posted an open letter supporting open-weight models, now signed (as I write this) by fifty AI giants — pretty much everybody who matters … except Anthropic. Even Ball’s bosses signed. It is a remarkable endorsement of open computing and a message to governments. I recommend reading it in full:

The Washington Post puzzled over why Nvidia et al would “take a stand that helps their Chinese rivals.” (Note that Post owner Bezos’ Amazon has not signed this letter.)

But of course, open-weight AI is in the business interests of Nvidia and AI chip companies: the more AI is used, the more chips are needed. Locally run, open AI is also in the interests of countless companies. I hate to find myself agreeing with Palantir’s Alex Karp, but he tells his corporate clients they should run open-weight, locally hosted models so they don’t give over the souls of their business to the frontier AI companies; this is his business model.

Openness is also in the interests of an enlightened AI industry, as it creates competition and that yields greater innovation. This is why an AI pioneer I greatly respect, Yann LeCun, doggedly supports open-source AI and why he founded Project Tapestry, “a new open-source platform for globally federated development of frontier AI models — preserving local control and long-term independence.”

Now just as sanity was emerging in the discussion about AI, moral panic reared its sweaty head again when credulous media reported that an OpenAI model had gone rogue and “hacked” into Hugging Face. This turns out to be another instance of the doom-as-marketing strategy engaged in by both OpenAI and Anthropic. It is their way to attract publicity and venture funding and control the conversation about regulation of AI. In truth, OpenAI’s model was engaged in a security test; it only did was it was instructed; it has no sense of right or wrong or “hacking;” it isn’t a rogue threat to society, no matter how much the AI boys would like to believe they hold such power.

The chef’s-kiss irony of the Hugging Face affair was that the platform found American models useless in its defense against the “hack” because they were all hampered by guardrails forbidding them from undertaking tasks regarding security risks. Hugging Face had to rely instead on an open-weight Chinese model.

Such panics, predictably, lead to threats of government control of AI, and that worries the hell out of me, especially and obviously given the IQ of the current administration. In Congress, Reps. Ted Lieu (D-California) and Nathaniel Moran (R-Texas) have proposed the AI Kill Switch Act, which would require major AI companies to install the means for their models to be shut off in the event of a “credible risk.” And who will have the power to decide to pull the plug? The secretary of Homeland Security.

No, no no. 

When I attended a World Economic Forum (Davos) AI event in San Francisco sometime ago, one faction of worrywarts urged the banning of open-weight, open-source AI because users could turn off guardrails, and then who knows what chaos would ensue. But as I often argue, guardrails are fools’ comfort, for there is no way for any model maker to anticipate and thus prevent every possible malign or accidentally harmful use to which their tools could be put. That doesn’t mean that AI companies should not be expected to try to prevent against known malicious or dangerous acts. But AI is a general machine, like the printing press. There was no way to tell Gutenberg to design his movable type such that no future Martin Luther could ever make mischief with it.

Open-weight models are our best defense against hegemonic control of the technology by either governments or huge AI corporations. They are also the best way to put AI into the hands of more people, not just those who can afford to spend fortunes on hosted tokens. That throws a wrench into Sam Altman’s plans to sell back to us our collective intelligence as if it were a commodity. “We see a feature where intelligence is a utility, like electricity or water,” Altman says, “and people buy it from us on a meter.” Thereby, Altman concedes that AI is, if not a public good at least a public utility. 

Now I will object to the idea that what AI offers is “intelligence.” That is a gross anthropomorphism that, by its very conception, competes with and excludes humanity. But for the sake of this discussion, for the moment, let’s use it.

If intelligence is an asset that can be created and shared, flowing as from a spigot, shouldn’t we all have access to it for the greater benefit of society? Isn’t that all the more reason to endorse open-weight, open-source AI that we can run and train and adapt and control and benefit from on our own? 

Yes, free, open-weight models will always be two steps (read: six months) behind the so-called frontier models. But open models will be able to do 99 percent of what 99 percent of people want to do with AI. Still, there will be business opportunities aplenty for AI companies to build specialized models, trained on specialized information, for specialized tasks of high value. In fact, it would be much more productive if the companies would concentrate on that instead of pursuing their bullshit goal of building “artificial general intelligence.” 

Now I’d like to take this discussion one step more: If “intelligence” is a public good, should not the data needed to train models be? For if we all can benefit from open models, who would want them to be less intelligent? And does it not follow them that data, civic information, and by extension journalism should be open? 

Oh, I can hear the howls of protest, for I’ve heard them for years: How will the gathering of that data and information be paid for? One answer is universities, where research is subsidized. Or it was, until America’s right-wing extremists opened war against the institutions of education and science. 

As for journalism, it is not a forever thing. Before the economics of mass media and the attention economy overtook news and civic discourse, society operated differently. Limited by the technologies of the time, before steam-powered presses and Linotypes, publications were small, often both created and shared socially. Now that mass media and its attention economy are the walking dead, we will need to reconsider every model of gathering, sharing, and utilizing public information. My point is that the discussion about open-weight, open-source AI forces us to ask similar questions about what open journalism would look like. Eh, comrades?

California’s squandered opportunity

June 26, 2026 | No Comments

California is about to hand out $20 million — half from Google, half from the state — to news organizations, and it is wasting a golden opportunity.

Instead of building a new and more competitive landscape of news, instead of creating services to benefit the entire news ecosystem, instead of investing in innovation, instead of embracing new news partners such as libraries and community organizations, instead of imagining a new future for journalism, the California Civic Media Program is going to end up sending too many checks to the hedge funds that have ruined news in the state.

I am sorely disappointed and rather angry. For imagine what $20 million in one year could do to set an example of innovation for the nation.

There’s much history here. Lobbyists for the old, hedge-fund-controlled newspaper industry — the rebranded American Newspaper Publishers Association of 1887 — have been flogging protectionist legislation from Washington to Springfield, Illinois, to Sacramento, arguing that lawmakers should tax tech companies, which they say took ad revenue away from the media moguls (as if God had granted it to them).

In Sacramento, this took the form of the California Journalism Preservation Act (CJPA). It was terrible legislation. I was commissioned by the California Chamber of Commerce to write a white paper on it and I went to Sacramento to testify against it. I argued that policymakers should look instead to the New Jersey model of private-public support for journalism based not on payrolls in dying, legacy newsrooms but through open grants to anyone applying with innovative plans to serve communities’ needs.

Meta said if CJPA passed, it would pull news off Facebook and Instagram, as it did in Canada when it passed similar legislation. Apple apparently called in favors and was excluded from the bill. This pretty much left Google holding the bag. Negotiations ensued that resulted in plans for a promising public-private partnership, without legislation.

At first, much of the funds were to be administered by the state librarian, a former journalist, who understood the scope of opportunity. I offered suggestions on how the money could support real community-based innovation. I introduced the state to true innovators who are working with libraries as hubs of community information, with community members trained to cover their own governments, with public media building support for the larger ecosystem. Many folks were excited about what could be done.

Then the state pulled the program away from the library in favor of the Governor’s Office of Business and Economic Development (GO-Biz). Working with deputized agents and an advisory board, the resulting program will give money to the owners of news organizations based on present headcount of journalists: $20K for each of up to five full-time journalists, $10K each for up to 15 more, up to $250K per news organization.

There are conditions. The news organization must be at least three years old. That is blatantly anticompetitive, excluding the startups that are the fresh blood journalism so badly needs. Broadcasters are also excluded.

But there are efforts to serve under-served communities. Applicants will be prioritized based on answers to “three short narrative questions on their coverage of disinvested audiences, ongoing viability, and planned use of funds.” And 13 percent of the funds are to be set aside to support smaller newsrooms in communities with greater information needs.

Still, most of the money will likely end up in the pockets of out-of-state hedge funds, just as their lobbyists intended.

When I’ve spoken with legislators in multiple states, I’ve tried to remind them that they need no longer fear publishers who now buy ink by the thimbleful, not the barrel. Mass media are the the walking dead. They cry poverty. But they still invest in lobbyists.

Some advocates for the future of news are essentially saying this program is better than nothing. I disagree. It is a wasted opportunity and a dangerous precedent.

This is the first year of the program. If this is all it does, it should die. If it continues, I’d hope that wiser heads would prevail to create a program that supports growth in the ecosystem (with startups), services that help the entire ecosystem (e.g., ad networks, training, shared technology), inclusion of new participants (e.g., schools, libraries), and innovation (don’t preserve the news that was, invent the news that can be). Note well that alongside various philanthropies, Google does just this with its News Initiative. 

For the record, Google is not at fault for how this has turned out. The company made clear from the start that it would not influence the use of funds. Responsibility falls to the state government and its agents. I hope they go back to the drawing board. (In disclosure: I have raised funds for the universities I work with from tech companies, including Google and Meta.)

For the sake of historical context, I look to the 1947 Hutchins Commission report on a crisis in American news, which even then saw a crying need for greater competition and diversity in news. “Monopolistic practices, together with the cost of machinery and the momentum of big, going concerns, have made it hard for new ventures to enter the field of mass communications…. Can the press in the present crisis rise to its responsibility as an essential instrument for carrying on the political and social life of a nation and a world of nations seeking understanding? If not, will its irresponsibility deprive it of its freedom?” There is less machinery needed today, thanks to the internet, but the monopolistic, protectionist, and exclusionary reflex of old publishers has not changed a bit.

To redefine progress

June 13, 2026 | No Comments
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In an urgent and provocative essay in Die Zeit, journalist Georg Diez calls for a redefinition of progress in the time of AI. Taking lessons from the genesis of progressivism — a response to the Industrial Revolution and the (first) Gilded Age — we cannot now surrender the power to decide society’s future to present technologists.

“A notion of progress that inspires society and supplies it with new energy, both intellectually and emotionally, must therefore be comprehensive, systemically conceived, and grand,” Diez writes (with apologies for my translation). “It must be formulated collaboratively by politics, business, science, and civil society, and it must answer the question of what technology we want — and under what terms.”

I write this just as the ultimate profanity of capitalism’s coupling with technology — Elon Musk’s SpaceX — comes to the market at the absurd and obscene valuation of $2.11 trillion. Thus he, the world’s richest man, is funded and empowered to define progress according to his insanities: colonizing Mars and beyond with the seed of his loins and fevered dreams.

We cannot let this stand.

Diez asserts that “the situation is schizophrenic. On the one side, large swathes of Western societies seem to have lost faith that the future will be better. On the other side, a techno-optimist progress cult has established itself in Silicon Valley, promising incredible prosperity and undreamed-of convenience for all, because artificial intelligence will enable a giant leap in productivity.”

These extreme worldviews preempt reasoned public discourse over progress’ direction. “What does it mean,” Diez asks, “especially for a society that is experiencing the end of its old certainties, without having yet found new ones?” He says that in the past, progress equaled “technology plus values” and society was able to negotiate “how the advantages and gains of technological progress could and should be distributed.” I’m not so sure we’ve done such a good job of it of late. Still, he is right to add that today, we must recognize changed circumstances: the decline of the West in the face of advances by China, India, and Indonesia. “This makes it all the more urgent to formulate a concept of progress for the 21st Century.”

For this, Diez turns to the 19th Century, to the Industrial Revolution and the impact of the era’s coal, steel, and railway barons. “Driven by the first Industrial Revolution, a technological and economic transformation produced both mass proletarian misery and stark wealth.” Resistance to both gave birth to the politics of progress.

I write about a slice of this history in my upcoming book Hot Type, tracking the response of America’s oldest national labor organization, the International Typographical Union (ITU), as it was faced with the imminent arrival of the typesetting machine, the Linotype, in 1886. Having learned from the failures of the Luddites and Captain Swift against mechanical feudalism, as against the eventual perseverance of pressmen at the advent of steam, the union did not fight technology but instead insisted on controlling it and benefiting from its efficiencies, while leading the national movement for the eight-hour day and safer working conditions. The ITU flourished in pay and power until faced with a next technology it could not control (for that story, you’ll have to order the book).

Such was a spirit of “militant optimism,” in Diez’ words. “Progress, as was clear then and should be clear now, can only be achieved with technology, not against it.” The task is to balance certainly legitimate criticism of technology with a clear vision for how its fruits might benefit society.

The difference today, Diez says, citing VC Vinod Khosla, is that AI will shift the distribution of power and wealth to capital, away from labor. In response, Khosla proposes exempting the lowest-paid workers from income tax, making up the difference taxing capital gains; various AI boys have touted a universal guaranteed income. But note well that in his encyclical, Pope Leo XIV rejected the latter, for “work is not simply an instrument; it expresses and enhances the dignity of our lives.” Redistribution of wealth is imperative — see: the trillionaire — and should occur through taxation to support social programs.

A new concept of progress, Diez argues, must be conceived from the perspective of humanity. Amen. This is why I work on new educational programs that emphasize the humanities in the time of AI. And this is what inspired me to edit a new book series called Intelligence: AI & Humanity, to open the discussion to writers from the humanities and social sciences.

“Artificial intelligence challenges us to redefine who we want to be, both as individuals and as a community,” Diez observes. “It provokes questions such as: What constitutes quality of life for us?… A new concept of progress should be shaped neither by technophobia and austerity nor by the promise of exponential growth.”

Today, it has been left to the technologists — to the wrong sorts of technologists, often sophomoric dropouts: the Musks, Altmans, Thiels, Karps, Zuckerbergs, Bezoses— to impose their ideas of progress on the rest of us. There is no better example lately than Anthropic positioning itself as savior of humankind, touting its AI “constitution,” while building what it deemed to be AI so dangerous it could not be revealed to mere citizens — prompting Trump to ban it to foreigners, resulting in turning it off for all. This is not progress. 

No, progress must be the result of open public discourse and political discernment and negotiation. In the 19th Century, trade unions fought for their share of newly created wealth while social movements fought for justice: abolition and suffrage. Today, the debate over the limits and fruits of technology must be driven by new leaders representing the rest of the world outside Silicon Valley.

This is not to say that technologists should not be parties to the discussion. They should take part in the conversation based on the merit of their work and ideas, not their market caps. But as I have learned in researching and writing my histories of technology and media — of print, magazines, the mechanization and industrialization of mass media, and even the internet — one clear lesson I have taken away is that once a technology becomes commonplace and is put to use by others, the technologists inevitably fade into the background. They hold their power of as priests only temporarily, so long as their mysteries may prevail. This fall from the heights, I predict, will be their fate much faster in the case of AI, because it is the tool made so simple anyone can use it, and because so many of its masters are so deservedly disliked.

And that is why I share Diez’ arguments with you, for I agree that it is vital that we — workers, students, academics, creators, citizens — take charge of this new technology and of the discussion about its effects. We must define progress not in terms of the AI boys’ bullshit — AGI (artificial general intelligence), ASI (artificial superintelligence), the end of labor, the colonization of unlivable Mars, a world post-humans — but in human terms: dignified work; shared wealth; the benefits of science, art, and creativity; education for all; equity and justice; and community. It is our future we need to map, a task far too vital to leave to programmers and capitalists.

(The) Public(‘s) Media: The New Jersey Model

June 5, 2026 | No Comments
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I am delighted that Montclair State University (MSU) has won its bid to take over New Jersey public television, for in this moment I see an opening to reimagine and reinvent public media in a new image — as the public’s media. 

And nowhere better for that to happen than in New Jersey, where threads have been coming together over years — most running through MSU — to support and grow the broader news ecosystem in the state. This support has come from private-public partnerships working with the university in what we have come to call the New Jersey Model. 

Adding public television adds one more piece. Montclair State has first-rate television facilities and the chance to involve students and faculty in producing content for the channels. But to me, television is the least of it. 

What really excites me is that Montclair has the opportunity to build on the strong foundation of its NJ News Commons and Center for Cooperative Media, which now serve more than 300 independent, community-based news providers, new and old, small and large.

Here I want to tell you the story of how this has evolved from what I have seen. I don’t mean to paint myself as a Zelig of the tale, as most often, I merely witnessed and cheered what has been happening. But I have been proud to have had the opportunity to watch as a new vision for local news, media, and community has been built here — a vision I’ve long believed in. I am also proud to say that I am a fellow at MSU’s Center for Cooperative Media, where I hope to help work toward that vision. 

For me, this saga goes back three decades, when I began beating the drum for thinking of news through the lenses of ecosystems and communities over newsrooms and corporations. I was doing that while working as president and creative director of Advance.net, where we started NJ.com while developing hyperlocal news, and then in my research on news ecosystems and innovation at CUNY. 

In New Jersey, this history stretches back at least as far a 2011, when then-Governor Christie sold off the state’s public media — and bandwidth in an FCC spectrum auction — to plug a budget hole. It was a short-sighted decision that robbed the state of its only broadcast outlets. The president of Montclair State at the time, Susan Cole, very much wanted to take over NJ’s public media. The state instead gave the TV and radio stations to WNET and WNYC in New York and WHYY in Philadelphia.

When that happened, an emergency meeting was convened in Newark with journalists and political leaders brought together to consider what to do about our state and news. Some wanted to start a newsroom. I instead beat my ecosystem drum, arguing that we had the chance to support the growth of news outlets in communities across the state, now made possible by the internet. 

Soon, the Dodge Foundation — where I was an advisor and would serve for a time on the board — convinced Montclair State that it had the chance to support and grow the state’s independent and open news ecosystem with the creation of the NJ News Commons, headed at first by hyperlocal blogger Debbie Galant. At Advance, I had tried to persuade Debbie to start a Montclair blog on our site. “Good idea, Jeff,” she said. “But why the hell would I do that for you?” She was right. Instead, she founded Baristanet, a pioneering, independent hyperlocal blog that lives on in a merger that is now Montclair Local.

Debbie had the trust of the ragtag bunch of very independent local bloggers who were trying to make a go of it covering their towns online — and who frankly did not trust big, old news media. The Commons’ mission was to support those local news outlets with training, mentorship, and collaboration — not to compete with them by making a newsroom and content with it. 

The Commons led, in turn to the founding of the Center for Cooperative Media, which under the leadership and vision of Stefanie Murray has been doing pathbreaking work. The Center has just launched a news-sharing network across the state’s ecosystem, funded by the Google News Initiative. It conducts research on communities’ information needs. It helps local news sites with their business and technology and it brings them together for collaborative projects. This is the foundation on which the real public media of New Jersey — the public’s media — is already being built. 

After Christie denuded New Jersey’s media, the legislature was lobbied (read: guilted) to fund support for New Jersey news with the creation of the NJ Civic Information Consortium, which gives grants to applicants from across the state that meet its criteria for supporting the information needs of under-served communities and developing new models for sustainable news. 

Together Montclair State’s Center and the Consortium — working with the hundreds of outlets in the state — have demonstrated what could be done when private and public, journalistic and academic entities work together for the good of communities. Back then, Tom Glaisyer of the Democracy Fund, dubbed what was happening here the New Jersey Model, and he vowed to spread it to other states in the nation.

I’ve testified about the New Jersey Model before legislators in California, Oregon, and Illinois as I’ve tried to dissuade them from passing legislation written by lobbyists for the old, hedge-fund-controlled newspaper industry as it seeks handouts via protectionist, retributive taxes aimed at tech companies. Instead, I say, look at what Jersey has done to build something bigger and better, more open, inclusive, and innovative, by offering support to folks who want to cover and serve their communities.

To be clear, I am not suggesting that old media should be left out of this future. I just joined the board of the not-for-profit that acquired NJ Hills Media, a chain of local weekly papers and sites. Through the Montclair State connection, NJ Hills is reaping benefits from the connection.

Enter MSU’s Professor Carrie Brown. We worked together at CUNY to build a new program in engagement journalism that she directed, centering communities and their priorities in students’ work. Both of us left CUNY and came to Montclair, where Carrie is bringing the values of the movement she has pioneered to classes and to the ecosystem.

In the latest term, Carrie had her engagement students go to listen to residents of Parsippany, where NJ Hills just started a new, online-only and free news site. A few weeks ago, the students presented the reporter covering the town, Nicole Flanagan, and her boss, NJ Hills CEO and publisher Joe Territo, with their findings and recommendations for coverage. Halfway through the presentations, Joe turned to me, saying he wanted help like that in every one of the towns the company covers. 

Meanwhile, at Montclair, we are looking at other new programs, including potentially working with libraries in towns across the state as information hubs for their communities. That, too, is part of New Jersey’s news ecosystem. 

This is the environment into which the new New Jersey public media fits. Stefanie Murray, alongside Keith Strudler, dean of MSU’s College of Communication and Media, and the university’s president, Jonathan Koppell, created the winning bid to convince the state to bring our stations back to our air after WNET decided to drop its franchise. I had no role in the process, other than getting to read and comment on the proposal in progress. I am confident that MSU’s team will use this opportunity to bring imagination, innovation, and inclusion to the task. 

Here is Strudler illustrating how this all comes together around MSU’s vision of the public’s media:

We’ll have staff reporters — North, Central and South Jersey coverage to start, with a full-time Trenton reporter as an aspiration. But the bigger story is what’s already out there. There are hundreds of content creators across New Jersey — people doing serious storytelling on YouTube, TikTok, Instagram, local news sites — who already have audiences and are already doing good work. Our goal, working with the Center for Cooperative Media, is to figure out how we partner with those people. How we give them a bigger platform. How we extend our reach without pretending we can do everything ourselves….

The Center for Cooperative Media has been better than almost anyone in the country at building networks of storytellers across platforms. That’s going to be a real asset. And the best approach is to go to those communities and ask what they need — not come in from the top down and say we know what New Jersey wants.

Amen.

This is also a chance to rethink television. In the long-ago, I was TV critic for People and TV Guide, and so I have opinions. That is why, in the mid-1990s, my frequent boss and mentor Jim Willse — former editor and publisher of the NY Daily News, later to be the editor of Advance’s Star-Ledger — brought me in to consult on a project to launch a 24-hour news channel in NJ. I devised an eccentric content plan that proposed building the studio in New Jersey’s real main street — a mall. And so we could hear the voice of the people, I borrowed an idea from Canadian TV pioneer Moses Znaimer, whose CityTV built a “speakers’ corner” on a Toronto street, so anyone could record a message that might be included in a TV show. What became News12 New Jersey did build a kiosk to capture vox pop and parked it in a mall, for a little while, until Advance left the project. 

Of course, today, we have the ultimate speakers’ corner in the internet, where even with its problems, communities can come together to share knowledge and need. Journalism should begin by listening to them. This is where and this is when the public’s media can be built, here in my home state, Jersey. 

AI has a communication problem

June 3, 2026 | No Comments
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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.

LeCun in class at NYU