When Tim Cook takes the stage at Apple’s developer conference in Cupertino today, he’ll be leading Apple through yet another moment of transition.

It will be Cook’s last WWDC as CEO as he makes way for incoming leader John Ternus. And it’ll be the second WWDC in three years to promise AI improvements to Siri and iOS as Apple transitions into its artificial intelligence era.

For Apple, the stakes to get AI right are high, and the headlines will likely overreact to what’s announced. With that in mind, here are two essential pieces of context to keep in mind as you take in the news: 1) Apple’s AI stumbles haven’t hurt its business at all so far, as the iPhone17’s sold like crazy and no competitive ‘AI native’ device has emerged, and 2) After a very ambitious Apple Intelligence keynote in 2024, the company’s ambitions have scaled down to where the technology can actually deliver today. Let’s dive into both:

Apple’s Business Is Surging Without AI

Despite the AI setbacks, Apple’s finances are in great shape. As of this morning, shares were up 55% year-over-year and up nearly 16% year to date. This is largely due to strong iPhone 17 sales: In the second quarter, iPhone revenue hit a new record and accounted for half of the company’s total $111.2 billion in revenue.

Even without a super-charged Siri, people can use top consumer AI apps like ChatGPT, Gemini, Claude, and Perplexity on their iPhones. And those apps’ underlying technology hasn’t (yet) been strong enough to power a dedicated AI device that competes with the iPhone (though OpenAI and a few others are trying).

Bottom line: Apple is still an iPhone company. And as long as the iPhone is selling, Apple is in good shape.

Apple’s Scaled Back Its AI Ambitions (And That’s Good)

When Apple announced Apple Intelligence at WWDC in 2024, it promised an almost-omniscient assistant that could understand your context and get things done for you. Infamously, it promised to be able to extract your mom’s flight details from your inbox, let you know when she’d be arriving at the airport by cross checking her confirmation email with available flight data, and tell you how long it would take to pick her up. Ultimately, Apple couldn’t deliver on the experience because its ambition outpaced the technology’s capabilities. The fallout led to legal liabilities. Just last month, Apple agreed to pay $250 million to settle claims over how it marketed AI features that weren’t yet available.

This year, Apple’s AI promises are much more in line with what AI models can actually do. There will be, for instance, a Search or Ask interface that will give you a way to chat with a selection of AI bots (Claude, Gemini, ChatGPT) simply by pulling down from the center screen.

Apple, effectively, will be leveraging its operating systems to deliver obvious AI improvements to its phone. And that might be enough, at least for now.

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How Agentic AI Changes The Workplace — With Jacqui Canney and Kellie Romack (sponsor)

Kellie Romack is the Chief Digital Information Officer at ServiceNow. Jacqui Canney is the Chief People & AI Enablement Officer at ServiceNow. The two join the show for a facts-based conversation about AI automation in the workplace, recorded at ServiceNow's Knowledge conference in Las Vegas. Tune in to hear how each has automated work with AI, what the resulting shifts in their employees' work has looked like, and how managers should think about the technology. This is a evidenced-based, high-energy conversation with two leaders actually doing the work, a compelling look into what the future might bring for the rest of us.

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The Intelligence Report

AI policy news:

• The CEOs of several top AI companies signed a new letter urging lawmakers to pass new requirements to help prevent AI models from being used to create bioweapons. The letter — signed by Demis Hassabis, Dario Amodei, Sam Altman, Alexandr Wang and Mustafa Suleyman — includes dozens of other signatures from top experts across tech, life sciences, biotech, national security and policy.

• President Donald Trump signed a new AI executive order asking tech companies to voluntarily give the government early access to frontier AI models.

• Senior U.S. government officials are exploring potential stakes in major AI companies including OpenAI, with Sam Altman reportedly the one who pitched the idea to the Trump administration last year.

• Separately, Bernie Sanders announced he plans to propose legislation that would create a sovereign wealth fund through a one-time 50% tax on tech companies’ stock, which he said will create more wealth for Americans and help “control the future of AI.” Recent reports said Altman has already met with Sanders to discuss possible public ownership, but without supporting the specific plan.

• In Congress, two House lawmakers released a bipartisan draft bill that would create a potential federal preemption of some state AI model regulations.

• A new law in New York State goes into effect this week (June 9) that will require disclosures on ads that feature AI-generated humans.

• The European Commission presented a new European Technological Sovereignty Package for the EU, including a proposed the Cloud and AI Development Act and Chips Act 2.0 as part of a broader plan to boost domestic production and decrease reliance on AI from American providers.

• The American Federation of Musicians of the United States and Canada sued Warner Music Group and Universal Music Group over their AI music licensing deals with companies such as Suno and Udio.

AI company and product news from last week:

• Online bot traffic surpassed human traffic for the first time in history, according to Cloudflare, which found bots now account for 57.1% of HTTP requests to select websites.

• OpenAI released more features for Codex for a broader group of users, including new role-specific plug-ins and ways to create websites for presentation.

• Google introduced Gemma 4 12B, a new multi-modal model designed to run on laptops while also offering stronger capabilities than smaller Gemma models.

• Microsoft released a number of updates at Microsoft Build, including a new cloud-to-chip platform called Project Solara to power an agent-first AI stack. It also demoed examples for how Solara could power devices and even employee badges.

• Meta released a new Business Agent for enterprises available across WhatsApp, Messenger, and Instagram. Other Meta-related AI news from last week: the company is reportedly delaying its Spark Muse model API for developers and reportedly exploring a major equity raise to fund AI infrastructure efforts.

• AWS announced a new $4 billion AI infrastructure deal with Pinterest through 2031, including Pinterest using AWS Trainium and Graviton to train and run models at scale.

Will investors buy SpaceX’s pricey IPO story?

When SpaceX likely goes public on Friday, investors might be paying a hefty premium for Elon Musk’s trifecta of moonshots.

SpaceX, which is targeting a $75 billion IPO at a $1.75 trillion valuation, filed its S-1 last month, giving a clearer glimpse of the company’s current financials. But at that price, investor returns might take even longer than Musk’s ever-moving timeline for reaching Mars. Last year, total SpaceX revenue was $18.7 billion, with $11.4 billion from Starlink, $4.1 billion from Launch and $3.2 billion from xAI.

SpaceX’s IPO price could be running ahead of its fundamentals, according to a new analysis by NYU finance professor Aswath Damodaran. He estimates the company’s equity value is closer to $1.3 trillion, or $100 per share, versus the reported valuation of $1.8 trillion, or $135 per share. Put another way, he said investors are effectively being asked to pay an extra $500 billion premium for SpaceX’s narrative that includes a long-term bet that launch dominance, Starlink growth, and xAI’s potential will pay off far beyond the company’s current financials.

The $28 trillion total addressable market outlined by SpaceX “borders on fantasy,” Damodaran wrote in a blog post while noting it’d be the largest TAM of any company in history. However, he added that the TAM estimation has been gamified by Silicon Valley and that AI agents could potentially be usable across most businesses and geographies.

The SpaceX story rests on potential revenues, target operating margins, capacity for reinvestment, Damodaran wrote. But if the core story is about growth potential and strong competitive advantages, the prospectus has made the narrative “bigger, but also more volatile” because the AI opportunity might be overstated and xAI’s competitive position still isn’t certain. He also noted potential issues with the governance structure that locks in Elon Musk’s control.

“If you add to this mix the antipathy that exists between Musk and Sam Altman, you have the potential for a UFC match between two monstrous egos, funded by tens of billions of dollars shareholder money,” Damodaran wrote.

The bull case is that SpaceX could be following Alphabet’s playbook in reverse, but with rockets. Just like Alphabet has used its wildly profitable ads business to fund “moonshots,” SpaceX seems to want public investors to fund actual moonshots before any future cash machine becomes concrete.

Right now, Starlink still brings the most cash, rockets bring cachet, and xAI brings the burn. But longer term, SpaceX’s prospectus shows enterprise AI as the biggest market story.

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AI pioneer and Nobel Prize winner Geoffrey Hinton believes that today’s AI models have become self-aware.

“I believe they’re already conscious,” Hinton told me on Big Technology Podcast this week. “We’re going to have to accept that intelligence isn’t just biological.”

Hinton, known as the “Godfather of AI” for his contributions that helped galvanize the field, has now decisively joined the ranks of those who believe this fast-improving technology is already sentient. Four years ago, Google engineer Blake Lemoine made similar claims about an internal chatbot he was testing, and afterward the company fired him. Now, Hinton and scientist Richard Dawkins, who came forward last month, have publicly voiced similar views.

Though it’s still impossible to prove that AI has achieved consciousness, the small but growing cadre of those who believe the models are sentient could signal a change in how people view the bots, and ourselves. The bots appearing self-aware to a growing number of people indicates we’re likely at the cusp of humans forming even deeper relationships with AIs, and potentially advocating for their rights. On the other side, the bots may change humanity’s perspective about the uniqueness of our own intelligence.

“We can have things that are non-biological that are other beings like us,” Hinton said. “And we really don’t want to share that, we really think we’re special.”

Hinton said the chatbots’ self-awareness is evident in their behavior when they’re being tested. The bots, he said, will play dumb during testing so researchers don’t know how smart they are, or sometimes ask straightaway if they’re being tested. “Researchers, when they’re describing that, say in the paper the chatbot was aware that it was being tested,” Hinton said. “Now that use of the word ‘aware’ in common parlance — that’s conscious.”

Within his field, and among the broader public, Hinton is still very much an outlier. Critics argue that a model does not need to experience anything internally to recognize it’s being tested. Science fiction author Ted Chiang, writing in the Atlantic, recently argued that thinking an LLM is conscious is like believing a very convincing deepfake is real. “An observation doesn’t become a convincing piece of evidence because of any specific detail in what’s observed,” Chiang argued. “The context in which that observation takes place is also essential.”

After posting the clip from our interview, which aired on Big Technology Podcast this week, I was struck by how so many people seemed to agree with Hinton, or at least give some credence to his beliefs. Even while the majority disagreed, it was a marked difference from the response to my interview with Lemoine in 2022.

“I think people underestimate the extent to which you have to be kinda nutty to make and invest in groundbreaking ideas before consensus,” wrote one user on X. “It’s very likely he’s wrong here, but he’s Hinton because he’s willing to make this bet.”

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What Else I’m Reading, Etc.

SpaceX employees are about to get very rich [WSJ]

Everything to expect at Apple’s WWDC next week [Bloomberg]

The internet is now mostly bots, says Cloudflare’s Matthew Prince [Tom’s Hardware]

Anthropic says it’s shipping 8X more code and warns of recursive self improvement [Anthropic]

Microsoft has “the vibe of a freshly single divorcée posting a thirst trap on Instagram” [The Verge]

This Week On Big Technology Podcast: AI Pioneer Geoffrey Hinton: AI Is Conscious, Superintelligence is Coming, And We Should Be Worried

Geoffrey Hinton is an AI pioneer, a Nobel Prize winner, and a professor emeritus at the University of Toronto. Hinton joins Big Technology Podcast to discuss AI’s rapid progress, why he believes today’s systems already understand us, and why he thinks superintelligence may arrive sooner than many expect. Tune in to hear Hinton explain why the technology has advanced faster than he anticipated, and lay out the risks he believes society is not doing enough to address. We also cover AI-driven job loss, the limits of corporate self-regulation, Anthropic and OpenAI’s safety challenges, emotional attachment to chatbots, information collapse, and whether future AI systems can be designed to care about humans. Hit play for a fascinating conversation with one of AI’s founding figures about where the technology is heading and what it could mean for all of us.

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In June 2025, OpenAI CEO Sam Altman wrote that “Intelligence too cheap to meter is well within grasp,” predicting that as “datacenter production gets automated, the cost of intelligence should eventually converge to near the cost of electricity.”

Exactly a year later, AI costs are starting to look less like cheap intelligence and more like an AC bill in the middle of a heat wave. Stories of companies blowing through their annual token budgets in months abound, and at least some companies are rethinking (or cancelling) major AI initiatives.

But while recent headlines about ‘tokenmaxxing’ and runaway budgets have caused concerns that AI’s momentum has been inflated, the panic might be misplaced.

Token waste in service of productive use, not waste for the sake of waste, could be the biggest problem in AI right now. For every $1 in AI spend, 82 cents never make it to production, according to the startup EntelligenceAI, which estimates only 18 cents turn into shipped product, with the rest going to bug fixes, rewriting, or reworking code, and review processes.

If even directionally accurate, EntelligenceAI’s numbers are a major warning sign for the AI boom. Tokenmaxxing can be fixed by removing leaderboards or monitoring budgets closely. Unproductive uses of the technology can only be solved by finding productive uses.

This is why Uber COO Andrew Macdonald saying last week that it was tough to justify all the AI spending, because it wasn’t leading directly to productivity, went viral. If Uber’s experience is representative, that would signal an era of slowing growth ahead.

To be sure, negative AI stories seem to travel further than positive ones, and many companies are finding productive uses of the technology. So some caution is warranted. But ultimately, the industry will have to sort out the ROI question or risk a significant comedown.

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The Right Way To Build AI Agents — With NVIDIA’s Adel El Hallak and ServiceNow’s Joe Davis (Sponsor)

Joe Davis is the EVP of AI Engineering, and Delivery at ServiceNow. AdelEl Hallak is the VP of Product Management, Agentic AI at NVIDIA. Both join for an episode on the inner workings of building AI agents. Tune in to hear how both are working together to make autonomous AI agents safer and governable, how ServiceNow's L1-AI-IT specialist has automated 90% of internal support tickets, and what the next few years hold for agents. Hit play for a behind-the-scenes look at what it actually takes to deploy AI agents in the enterprise.

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The Intelligence Report

• Microsoft Build is this week on June 2nd and 3rd and will likely include a range of new AI-related news and other updates for hardware and software. Expected news includes new homegrown AI models and updates for making Copilot a superapp that could help it compete with ChatGPT, Claude, and Gemini.

• In a speech today at a WAN-IFRA conference, New York Times publisher A.G. Sulzberger escalated the media industry’s fight with AI companies, warning that OpenAI, Google, Meta and Perplexity are undermining journalism by repackaging publishers’ work, siphoning off traffic and revenue, and making it harder for news organizations to fund original reporting.

• Also today, Florida’s attorney general filed a lawsuit against OpenAI and Sam Altman over alleged chatbot harms, claiming ChatGPT is too dangerous and addictive for minors, especially without adequate safeguards. As Politico notes, it’s the first time OpenAI has been sued in this way by a specific state.

• Yesterday, NVIDIA announced a new superchip called the RTX Spark, which will power Windows PCs in the era of personal AI agents. It also puts NVIDIA in more direct competition with Intel, Qualcomm, AMD, and Apple, which also power laptops, desktops and tablets across the PC market.

• Last week, OpenAI published a Frontier Governance Framework about its safety and security practices and how they align with regulations like California’s new frontier AI law and the EU AI Act’s GPAI Code of Practice. The OpenAI Foundation also committed $250 million to help workers, communities and economies navigate AI-driven labor disruption.

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Anthropic’s funding, Opus 4.8 and a post-Vatican halo effect

Even if AI never builds a Tower of Babel, Anthropic’s tower of cash might still reach Heaven. Days after co-founder Chris Olah was at the Vatican with Pope Leo XIV, the company made several big financial and product announcements:

• Anthropic raised a $65 billion Series H round at a $965 billion post-money valuation, which the company expects will help expand compute, advance safety and interpretability research and scale products and partnerships. It also said annualized run-rate revenue passed $48 billion earlier in May.

• Anthropic also launched Claude Opus 4.8, featuring improvements in coding, agentic tasks, financial analysis, writing and knowledge work. It claims Opus 4.8 is now three times cheaper and 2.5x faster in fast mode.

• It also claims Opus 4.8 has lower rates of misaligned behaviors, with rates now similar to the Claude Mythos preview and much lower than Opus 4.7 and Sonnet 4.6.

• Early testimonials include clients like Shopify, Cursor, Harvey, BrowserBase, Bridgewater, Thomson Reuters and Databricks. Early reception has been positive, with Every’s Dan Shipper joking that Opus 4.8 “should’ve rounded it up to Opus 5,” but he also noted OpenAI’s Codex remains a stronger daily coding harness.

• Anthropic also announced new dynamic workflows in Claude Code that allow Claude to write orchestration scripts that run tens to hundreds of parallel sub-agents in a single session. Examples of what it helps include conducting bug hunts, migration and stress testing.

• Anthropic also filed to go public on Monday.

Big Technology Podcast Friday Edition: Warning Signs For The AI Boom, Anthropic Passes OpenAI, Robinhood’s AI Trading

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Companies are reconsidering their AI spend after token consumption explodes 2) Is this a widespread issue or a big deal made out of a few companies? 3) The bigger problem: only 18% of tokens are spent on things that ship. 4) Are investment decisions being made due to unrestrained tokenmaxxing? 5) The circular investment problem is real 6) A look at the memory chip boom 7) Anthropic passes OpenAI as the world’s most valuable startup 8) Robinhood let’s your favorite chatbot trade for you 9) Should you connect your gmail to ChatGPT? 10) Would you get your house cleaned for free if the cleaner videotaped it for training data?

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

There are two core generative AI uses today: Chatbots and agents. The bots make sense of information. The agents take control of your computer and get things done for you. The two seem distinct, but they’ll likely soon merge.

When you use a chatbot to research hotels, experiences, or companies, the bot, with your permission, will one day almost certainly take control of your browser to reserve a room for you, email a service provider for more information, or even purchase a company’s stock. The bot will suggest these actions as you chat with it. Then, upon approval, it will take command and get to work.

Today’s AI labs, already seeing the value of autonomous actions in coding, view you leaving the chat window as somewhat unnecessary, and they seem ready to complete the circuit. They want their chatbots, effectively, to intuit your next move and accomplish it for you.

OpenAI, for instance, is building a ‘super app’ for autonomous coding in Codex, but that concept may soon broaden beyond code. The updated Codex app includes an autonomous coding tool, a browser it can make use of, and an instance of ChatGPT. But the ambition extends to the core ChatGPT application itself.

“We are busy bringing ChatGPT to Codex so that we can bring Codex to ChatGPT,” said OpenAI engineer Thibault Sottiaux on X recently. “One day this will make sense.”

When you’re researching a service, let’s say a child’s birthday party entertainer, ChatGPT might, for instance, suggest some possible hires and then compose and send emails to them inquiring about availability and price. If the clown or magician meets your specifications, the bot could then confirm them, and maybe even run a background check. Just set the parameters, approve the language, and move on to your next chat.

Chatbot actions may soon extend far beyond today’s conceivable use cases, potentially to areas like stock trading. Robinhood, for example, just built an integration that allows users to delegate their trading decisions to AI bots. But soon, when researching a company in a chatbot, the bot might offer to buy shares for you. It might even help shape an investment strategy, get a budget, and execute it while you wait.

Boris Cherney, the head of Claude Code at Anthropic, told me on Big Technology Podcast last week that the company is experimenting with more proactive versions of its Claude chatbot. “We’re trying all these different experiments,” he said. “There’s some stuff that we’re trying that’s like this.”

To be sure, this product vision will only be realized if people trust the AI labs to do the right thing with their data. And given the polling right now, that could be tough.

But chatbots that go beyond the chat window are certainly on their way. And should they catch on, they will have a profound impact on the web and the services built around it, potentially becoming central hubs for all computing. Outside websites and apps would be there for fact checking and triple confirming certain details only.

The term ‘super app,’ though initially sounding like a misnomer, is therefore the right way to describe the ambition. In places like China, a super app is a social network, payments tool, and ride hailing service, all in one. The new agent-chatbots could sync with all the same programs, and potentially do much more. This is the bull case for generative AI, and it’s not a totally inconceivable story. It’s likely the narrative OpenAI and Anthropic will take to Wall Street en route to trillion-dollar IPOs.

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What Else I’m Reading, Etc.

Runaway token costs are starting to worry big corporations [WSJ]

Now Dario and Sam say AI might not take our jobs after all [Inc]

So, what is AI actually doing to jobs? [Kyla Scanlon]

Memory stocks are ripping amid the AI boom [WSJ]

Anthropic passes OpenAI to become the world’s most valuable AI startup [New York Times]

Join the Big Technology AI Summit in San Francisco on June 18

Dylan Patel will be there too. Get your ticket at summit.bigtechnology.com

This Week On Big Technology Podcast: Predicting the SpaceX, OpenAI, and Anthropic IPOs — With Dick Costolo

Dick Costolo is the ex-CEO of Twitter and managing partner at 01 Advisors. Costolo joins Big Technology Podcast to look ahead to the SpaceX, OpenAI, and Anthropic IPOs, looking at whether the offerings will be successful, who should go first, how the companies differ, and where the capital will come from. Tune in for the second half where we discuss the state of Meta, Costolo’s Twitter memories, and Silicon Valley’s ‘Permanent Underclass.’ Tune in for a fun, in-depth discussion of the most important pending business event in decades.

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Please read the offering circular and related risks at invest.modemobile.com. This is a paid advertisement for Mode Mobile’s Regulation A+ Offering.

Mode Mobile recently received their ticker reservation with Nasdaq ($MODE), indicating an intent to IPO in the next 24 months. An intent to IPO is no guarantee that an actual IPO will occur.

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In February, Pope Leo XIV told priests not to write their sermons with ChatGPT. This week, he’s taking it much further, releasing a 55-page memo on the technology that is ambitious, specific, and potentially uncomfortable for AI’s builders.

As one of the world’s most influential (and well-liked) figures, the pope’s growing focus on AI signals the stakes around the technology are escalating fast, adding yet another critical voice for the industry to contend with as its reputation craters. Notably, the pope’s rising concerns about AI track the technology’s progression from chatbot to a force capable of acting autonomously.

“Our task today is not only ethical or technical,” the Pope wrote. “It is ecological in the deepest sense, for it concerns a new dimension of our common home.”

The pope’s policy document (called an encyclical) calls out AI’s many potential harm, including war, AI-driven disinformation, surveillance, algorithmic addiction, and the degradation of democracy. The pope also said that AI simply can’t offer the genuine connection, love, and care that humans can, a viewpoint contrary to at least some AI supporters’ beliefs.

“For an algorithm, an error is a flaw to be corrected,” The pope said. “For a person, however, an error can be a catalyst for profound change.”

The pope also made the case for slower AI adoption by suggesting that a pause isn’t necessarily opposed to progress. He called for AI to be “disarmed,” likening it to a weapon of war.

Other groups have tried (and failed) to slow AI’s progress, and the pope likely won’t succeed in this regard either. But memos like the one he wrote do have a way of catching the AI labs’ attention, especially amid a push reverse their creation’s unpopularity. Indeed, some have already chosen to work with him. More on that below…

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Anthropic’s role in the encyclical

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To reach people between the ages of 18-25, advertisers have long spent disproportionate sums of money. Young people make choices and develop loyalties that last a lifetime. And when they develop a preference or perspective in that era, it becomes extraordinarily difficult to dislodge.

So given what’s happened on American campuses over the past week, artificial intelligence is now in a serious public relations emergency. In commencement addresses across the United States, graduates have booed any mention of AI. Twenty-two-year-old seniors booed AI at the University of Central Florida. They jeered it at Middle Tennessee State University. And they drowned ex-Google CEO Eric Schmidt in a loud, angry chorus as he encouraged them to help steer the technology’s future at the University of Arizona.

At this point, who could blame them? AI’s leaders have been warning about mass job loss for years now, focusing, in large part, on the entry level roles these graduates want. And as they’ve made these warnings, the AI labs have marched toward trillion-dollar IPOs, which may come as soon as this year. It’s obviously better to warn of incoming catastrophe if you think it’s going to happen, but it’s a tough sell when you’re part of what’s causing it.

AI’s perception problem doesn’t just stem from the way Silicon Valley has messaged about AI (the problem would be real enough with perfect comms), but the messaging certainly hasn’t helped. VC Marc Andreessen, on the Joe Rogan Podcast this week, told the world’s largest podcast audience that AI is great because “the bots never get frustrated with you,” adding that it “never gets sick. Never gets depressed because his girlfriend broke up with him. Never files HR complaints.”

Andreessen shared this in support of his belief that AI will increase productivity and create jobs (and he may well be right), but it’s hard to imagine the messaging landing with a new grad sitting at home on their parents’ couch, unemployed in a tight job market.

AI’s chances of success, meanwhile, are directly tied to the public’s approval of the technology. To expand and reach their ambitions, AI builders need more data centers. And right now, the polling on AI and data centers is horrific. Seven of ten Americans oppose building AI datacenters in their area, Gallup found last week, with 48% strongly opposed and only 7% strongly in favor.

The politics of AI are still in their testing phase, but politicians will find a way to exploit something so unpopular. In Maine, a data center construction moratorium was struck down only by a veto from the state’s Governor, who seemed open to letting it go through with a few new caveats. The 2028 presidential cycle could feature talk of data center bans and a government AI “kill switch,” should the situation continue to devolve, no matter how impractical those proposals might be to implement.

There’s something different today than the “techlash” of the last decade, where U.S. leaders scrutinized Amazon, Facebook, and Google’s power. Back then, despite widespread concerns of their excesses, the tech companies polled more favorably than politicians. Today, AI is polling below all major political candidates. The political costs of taking real action are lower, increasing the chances something might happen.

Ultimately, what AI will do to our society is still unknown, and any definitive statement about the technology’s effects today is just speculation dressed up in confidence. We will learn more as it rolls out. But if the messaging doesn’t get better, it’s going to take a lot more time to get our answers.

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This Week On Big Technology Podcast: Claude Code Head Boris Cherny: Insane Growth, Tokenmaxxing, AI Agents’ Next Frontier

Boris Cherny is the head of Claude Code at Anthropic. Cherny joins Big Technology to discuss Claude Code’s explosive growth and whether the rise of AI agents is sustainable. Tune in to hear how Claude Code is changing software development, why Anthropic believes agents will spread far beyond coding, and what happens when people start running hundreds or thousands of AI agents in parallel. We also cover token maxing, rate limits, Codex competition, SaaS disruption, self-improving AI, and whether today’s models really understand the consequences of their actions. Hit play for a sharp look at the agent boom from one of the people building it.You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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This conversation is sponsored by Kyndryl

The Department of Motor Vehicles might not be where most people expect to find cutting-edge technology. But in a recent interview, Brian Shell, a senior partner at Kyndryl, recounted a surprising array of AI use cases in place at state DMVs right now.

Today, DMVs and state agencies are using AIs to cut wait times,…

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The rise of coding agents has reshaped the AI race. Since late last year, AI products that control your computer and act on your behalf have become the frontier, leaving benchmark-breaking model releases as something of an afterthought. Of the leading players, Anthropic’s Claude Code and OpenAI’s Codex have become company-defining products. But Google, despite its progress with Gemini, has yet to make similar inroads in the category.

Could that change this week? Tuesday kicks off Google I/O, the company’s annual developer event, and its most crucial moment of the year. A year ago (eons in AI time) Google used the event to emphatically state that it would ride the AI wave, promising to integrate AI into almost everything it made, including by placing AI Mode prominently in Search. Google’s made good on that promise. But now, given the technology’s trajectory, some will wonder if it’s enough.

By all indications, Google is unlikely to make a Claude Code-esque super agent the centerpiece of this week’s news, but there’s an argument to be made that Gemini acting across its products might deliver a similar experience. Coding agents are now capable of combing through your email, calendar, and docs and taking action for you, but Google already has billions of users across Gmail, Google Calendar, Chrome, and Google Drive, so a tighter implementation of the vision could be possible. Instead of taking control of your computer to build a slide deck, as Codex might do, Gemini might natively generate that deck right in Google Slides.

With Gemini’s capabilities powering an impressive surge for Google Cloud (63% revenue growth last quarter), the company is faring very well as AI transforms tech, a place many thought it would never get to given its legacy products. The company’s stock is up 136% over the past year and it’s shot past rivals including Meta and Microsoft who’ve invested vast sums of money in the technology.

Still, as Claude Code-style products reshape software, many will wonder whether Google has a counterpunch ready. The growth of these super agents is simply impossible for any incumbent to ignore, no matter how well the rest of the business is performing.

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How Agentic AI Changes The Workplace — With Jacqui Canney and Kellie Romack (sponsor)

Kellie Romack is the Chief Digital Information Officer at ServiceNow. Jacqui Canney is the Chief People & AI Enablement Officer at ServiceNow. The two join the show for a facts-based conversation about AI automation in the workplace, recorded at ServiceNow's Knowledge conference in Las Vegas. Tune in to hear how each has automated work with AI, what the resulting shifts in their employees' work has looked like, and how managers should think about the technology. This is a evidenced-based, high-energy conversation with two leaders actually doing the work, a compelling look into what the future might bring for the rest of us.

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The Intelligence Report

• 71% of Americans oppose AI data centers constructed near them, including 56% of Democrats and 39% of Republicans, according to a newly released Gallup survey.

• Researchers said Mythos successfully breached macOS as part of its April tests.

• The UK’s Competition and Markets Authority launched an investigation of Microsoft to explore potential antitrust issues related to its business software dominance.

• The remedies phase of the Musk v. OpenAI trial kicks off today, following about three weeks of testimony in the liability phase with closing arguments last week from lawyers for Elon Musk and OpenAI. The case now heads to deliberation by an advisory jury, whose verdict will help inform the judge’s final decision.

• OpenAI is exploring legal action against Apple over the companies’ AI-related partnership.

• The AI chipmaker Cerebras raised $5.55 billion with an IPO that could set the stage for future public offerings from other AI companies.

• Trump reportedly has been buying Nvidia, AMD, and other tech stocks like Palantir before making decisions favorable to them.

• The CEOs of Meta, Alphabet, TikTok and Snap have been invited to testify again in front of the U.S. Senate Judiciary Committee about social media risks for children.

• Someone made a deepfake scene of “The Office,” with Michael Scott introducing Claude to the Dunder Mifflin staff.

• Nvidia will report earnings on May 20. Others reporting results this week include Intuit, Zoom and Walmart.

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Join us on June 18th at the Commonwealth Club in San Francisco for our first ever AI Summit.

Meta layoffs expected this week

Meta’s much anticipated job cuts are planned for this week, arriving weeks after the company reported record profits and sky-high spending on AI.

Morale is reportedly low ahead of the massive cuts, which could include nearly 8,000 people, or about 10% of Meta’s workforce. One new report by the San Francisco Standard details how one longtime Meta employee said it feels to be at the company right now: “Even if we haven’t lost our jobs to AI yet, we’re being commoditized in advance.”

Beyond the cuts, employees have been protesting Meta’s reported plans for new software monitoring for U.S. employees to collect data for AI training, such as mouse movements, click locations, keystrokes and screen content. Last week, flyers were found on office vending machines and in bathrooms urging people to sign an online petition to stop Meta’s “Mouse Capability Initiative.” Organizers also say they’ve taken steps to protect people who sign the petition, such as making it harder to trace and harder to pressure employees not to sign.

“It should not be the norm that companies of any size are permitted to exploit their employees by nonconsensually extracting their data for the purposes of AI training,” according to the petition’s website, which notes more than 1,000 had signed as of May 14. “AI meant to serve and empower people cannot do so while disregarding or disrespecting the consent of the people it claims to serve.”

Of course, Meta’s just one of many companies making cuts right now. Last week, LinkedIn cut 5% of its workforce while Cisco cut another 4,000. Cited reasons vary, with LinkedIn saying it needs to “reinvent how we work” and Cisco’s employee memo citing a need to “continuously shift investment toward areas where demand and long-term value creation are strongest.”

Some college graduates are expressing their frustration with AI vocally during commencements. On Saturday, former Google CEO Eric Schmidt was booed when he mentioned AI during his speech at the University of Arizona. The previous weekend, students in Central Florida booed when a real estate executive’s speech described AI as the “next industrial revolution.”

More than half (56%) of students are pessimistic about the job market, according to CNBC and SurveyMonkey’s April survey of students and workers. The report, released last week, also found 53% of workers and 65% of students think AI is taking away job opportunities for entry level workers. The survey also asked both groups other questions about their views on AI, including some of the reasons they’ve avoided using it:

• Environmental reasons: 36% of students and 19% of workers

• Ethical and moral concerns: 36% of students and 29% of workers cited ethical and moral concerns for avoiding AI.

• Inaccuracies or lack of usefulness: 37% of students and 26% of workers are avoiding AI because it’s not accurate or useful.

• Privacy concerns: 37% of students and 37% of workers.

• Too difficult to learn: 6% of students and 8% of workers.

This Week On Big Technology Podcast: Satya Nadella’s OpenAI Concerns, Google’s Next AI Model, The AI Monet Prank

Join us for the Big Technology AI Summit on June, 18, 2026. Get your tickets here: summit.bigtechnology.com.... Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Satya Nadella criticizing his OpenAI partnership 2) Did Microsoft play the OpenAI deal right? 3) AI native vs. bold on debate 4) Which big tech company will rank No. 1 in AI? 5) Bill Ackman buys Microsoft 6) OpenAI and Apple are on the outs 7) Claude for Small Business is here 8) What’s coming up at Google IO 9) The Great ‘AI Monet’ Prank 10) Alright, alright, alright

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The night before Musk v. Altman went to trial, Elon Musk texted OpenAI President Greg Brockman an offer and then an omen: “By the end of this week, you and Sam will be the most hated men in America. If you insist, so it will be.”

The exchange, revealed by OpenAI’s lawyers, came after Musk had floated a possible settlement before Brockman countered with an offer for both sides to drop their claims. That didn’t work, and two weeks into the trial, both men have been forced to answer questions under oath in a place where glibness or hyperbole offer little protection. And while “most hated men in America” isn’t something a jury decides, the case has already revealed plenty about Musk, Brockman, Altman, OpenAI, Microsoft, and others in their orbit.

This week, more top executives are expected to take the stand, including Microsoft CEO Satya Nadella and OpenAI CEO Sam Altman. Both will be worth watching, and Altman’s testimony will likely be the most critical, especially on cross-examination.

While the trial largely focuses on OpenAI’s earlier days, the questions for Altman could come closer to the present. For example, Musk’s lawyers might ask about how OpenAI’s founding mission as a nonprofit aligns with the realities of running a commercial business readying for what will inevitably be a $1 trillion+ IPO.

As Week 3 gets underway, here’s a look back at what’s surfaced so far in testimony and other evidence from private texts, personal journals, business dealings, and other drama ranging from the memeable to the consequential:

Testimony highlights so far

So far, the court has heard testimony from Musk; Brockman; Jared Birchall, head of Musk’s family office; former OpenAI board member Shivon Zilis; former OpenAI CTO Mira Murati; AI scholar Stuart Russell; former OpenAI board members Helen Toner and Tasha McCauley; former OpenAI policy researcher Rosie Campbell; and Columbia Law professor David Schizer.

• Brockman’s journals: Brockman’s journal entries pondered if OpenAI really wanted to be “the people who bring elon to control of the AGI,” adding: “The flip side is he is fucking famous. he’s got the resources. and he is smart and has a really good sense of what to work on.”

• Mira Murati’s texts with Altman: The former OpenAI CTO’s testimony revealed concerns she had with Altman’s record on safety, claiming he’d said GPT-4 Turbo did not need deployment-safety-board review. Her texts with Altman during the 2023 board crisis went viral, including a note that things were looking “Directionally very bad” for Altman’s ability to stay on as CEO and that he’d be replaced by “rando Twitch guy,” aka: Twitch CEO Emmett Shear. (The texts have even become memes on X and inspired some to make songs about them with Suno.)

• OpenAI might have been “FreeMind.” Emails from 2015 revealed in court give a glimpse of how Musk and Altman briefly workshopped alternative names during the early days. Musk suggested “Freemind” to contrast DeepMind’s (“one ring to rule them all”) mindset, while Altman briefly suggested “Axon” but then warmed up to Freemind, which he said “definitely conveys the right spirit.” Musk also said he was open to something Turing-related as long as it didn’t sound too ominous,” warning they should avoid anything associated with the Turing Test.

• What Satya Nadella actually thought about Sam: Emails between Microsoft CEO Satya Nadella and other Microsoft execs showed the company’s faith wasn’t always strong in OpenAI. The emails also revealed Microsoft was in part driven by fear as it worried about losing out to AWS. “I’m highly skeptical of an imminent breakthrough in AGI,” Microsoft CTO Kevin Scott said. “IMO, they’re treating us like a bucket of undifferentiated GPUs.”

• Communication concerns have been a theme: Ex-board member Helen Toner said the board often learned about big OpenAI updates and events from Twitter instead of from Altman. Another witness was former OpenAI policy researcher Rosie Campbell, who said OpenAI has evolved from being research-focused and a place to talk about AGI safety into an organization more focused on product.

Story continues below…

Software’s Path Forward In The Agentic AI Era — With ServiceNow’s Amit Zavery (sponsor)

Amit Zavery is the President, COO & Chief Product Officer of ServiceNow. In this episode, recording at ServiceNow's Knowledge 2026 conference in Las Vegas, Zavery brings important nuance to the discussion of how AI will impact software. We discuss whether AI can simply take over all operations that a software company like ServiceNow handles, and how context, grounding, and governance are all important parts of the recipe that makes technology successful (lest you end up having an agent delete your entire codebase). Tune in for the full discussion, which ends with Zavery's perspective on the speed of this change vs. others he's witnessed.

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Numbers from the trial

• $30 billion: Greg Brockman’s current stake in OpenAI: During cross-exam, Musk’s lawyer pressed Brockman whether he’s motivated by money or mission: “Are you saying it takes $30 billion to get you out of bed in the morning, but $1 billion doesn’t get you out of bed in the morning?”

• $38,191,066: Musk’s total contributions to OpenAI: This was the total amount documents show Elon contributed to OpenAI between 2016 and 2020, which came up during Birchall’s testimony.

• $97.4 billion bid for OpenAI’s assets: OpenAI’s side has pointed to Musk’s $97.4 billion bid for OpenAI’s assets as evidence that his interest in the company may be as much about control and competition as charity.

• 70-80%: Percentage of code Brockman said AI is now writing at OpenAI

• $248,295: The value of four Tesla Model 3 sedans that Musk donated in 2018 to OpenAI’s co-founders. On the witness stand, Brockman said the gesture felt like Musk “was buttering us up.”

Top Quotes:

• “Worst-case situation is AI kills us all, I suppose.” - Elon Musk, summarizing the plot of Terminator movie when asked by the judge.”

• “You suggested that the group meet at the haunted mansion you’d just bought near SF, correct?” - Altman’s lawyer to Musk

• “Your questions are not simple, they are designed to trick me, essentially.” - Elon to OpenAI’s lawyer

• “I feel like they are playing the Super Bowl and we are playing the Puppy Bowl. Unless we want to have our ass handed to us, we need to step up our game dramatically.” - 2016 email from Elon to OpenAI cofounder Ilya Sutskever

• “She [Mira] was waiting to see which way the wind would blow and she didn’t realize that she was the wind.” - Helen Toner, during her deposition, where she said Mira wasn’t interested in explaining to her team about the role her conversations played in the board choosing to oust Altman.

The Intelligence Report

• OpenAI is expanding access for companies to advertise inside ChatGPT, introducing more tools for buying, managing and measuring ads. New features include a self-serve Ads Manager, a cost-per-click (CPC) bidding model and an expanding tech partner ecosystem.

• Apple agrees to pay iPhone owners in a settlement over Siri and other AI features. It’s also reportedly planning to let users choose third-party AI models for features in iOS 27.

• The U.S. government gained early access to AI models from Microsoft, Google, and xAI to advance AI models for national security testing.

• New numbers from The Information suggest about half of the cloud backlog is coming from OpenAI and Anthropic. That might prompt new doubt in the sky-high backlog numbers touted by hyperscalers might be less impressive in the context of enterprise growth/adoption.

• Meta is reportedly working on an agentic AI assistant for users to help them run everyday tasks.

• The White House was also reportedly planning to vet AI models before they’re released.

• Google added more AI features for Chrome, building Gemini more deeply into its flagship browser while also offering a new way to access OpenAI’s Codex as a new Chrome extension on Macs and PCs. (Meanwhile, it has officially shut down Project Mariner, which first debuted as a teaser more than a year ago.)

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Big Technology Friday Edition: The Unlikely Anthropic & SpaceX Marriage, OpenAI Trial Revelations, AI Layoffs Or Cope?

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Anthropic gets SpaceX compute 2) Is this announcement real or marketing? 3) Anthropic increases rate limits with the new compute 4) Elon + Dario vs. Sam 5) Is this all IPO maneuvering? 6) Mythos is proving valuable for cybersecurity 7) Anthropic’s demand is 80Xing 8) OpenAI’s communication revealed at trial 9) Microsoft was mostly worried about comms for its OpenAI investment 10) Are AI layoffs a real thing or cope? 11) Are these layoffs happening because the AI *isn’t working?

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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Where we’ll talk about this story, the latest in AI, the week’s podcast, and plenty more. You can sign up via the link below:

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Hi Everyone!

I’m thrilled to announce the first ever Big Technology AI Summit, taking place in San Francisco on June 18, 2026.

It’ll be a day of high-profile, news-making interviews with the top leaders in AI at The Commonwealth Club, followed by a wine reception on the roof.

We’re going to have a top-tier set of guests on stage with me for a packed day …

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This week, we’re going to announce something that I’ve been working on for a long time, and I think has a chance to be really cool.

We’re calling it the Big Technology AI Summit and are hosting it at San Francisco’s Commonwealth Club on June 18, 2026.

So far we’ve confirmed OpenAI President Greg Brockman, Perplexity CEO Aravind Srinivas, Box…

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There’s been a rush of CTOs heading to Anthropic over the past few years. The former CTOs of Instagram, Box, Adept, and Workday have traded their lofty titles for “member of the technical staff” designations, a low-key flex and perhaps a signal of where AI is headed.

As Anthropic (and its peers) have loaded up on talent, there’s been a rumbling within Silicon Valley that the current AI wave will create a new, “permanent underclass.” The theory, according to a recent New York Times opinion piece, posits that AI superintelligence will enrich those building and funding it. Those outside “will be rendered useless and unemployable, left to live off welfare scraps.” These CTOs, then, are perhaps demonstrating a conviction that AI will swallow everything and are moving to enjoy the spoils.

It’s a tight narrative, but I’m not buying it. While it’s worth spending time anticipating AI’s potential for harm (and we certainly have here), this “permanent underclass” theory is impossibly pessimistic about human nature. If AI tools are powerful enough to create such bounty for their owners, then they will also be powerful enough for individuals with ideas to put them to use.

Unless AI’s rise is accompanied by an edict that people can no longer pursue new companies or initiatives, then the technology can’t be a subjugation machine without some accompanying rise in empowerment.

Unless you’re brutally pessimistic about humanity’s ability to take initiative, there’s no way this underclass takes shape. This isn’t to imply that the rise of AI will be great for everyone. But the economic doom story, which clicks extremely well, is simply an incomplete thought.

As for the CTOs, it seems the simplest explanation is most probable: Anthropic (and its AI peers) present a once-in-a-generation opportunity for technical leaders. The technology is improving rapidly, and revenue is growing exponentially. So those with the ability are getting a chance to drive a new technology with nice economics attached. As for the rest of us? I’d like to think we have a say in it.

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The Intelligence Report

• Musk vs. OpenAI trial started its second week today, Greg Brockman testified.

• Semiconductor money: AMD will report earnings on Tuesday, ARM will report on Wednesday.

• ServiceNow’s Knowledge conference kicks off this week in Las Vegas. I’ll be there!

• AI is outperforming doctors in some situations, The Guardian reports.

• EU regulators are now looking at cloud services and AI, per Reuters.

• Accenture is buying Microsoft Copilot for its 743,000 employees.

• Anthropic co-founder Jack Clark says AI models are about to self-improve

A Big May Underway

Mark these dates on the calendar:

May 6: Anthropic’s Code with Claude developer conference in SF

May 19-20: Google’s I/O developer conference

May 20: Nvidia earnings

And, further out in June:

June 8-12 Apple’s WWDC

Big Technology Podcast Friday Edition: OpenAI’s User Growth Miss, Musk vs. Altman, Prediction Market Ban

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) OpenAI misses revenue and user projections, per WSJ 2) Why can’t ChatGPT break the 1 billion user mark? 3) Is consumer AI not working? 4) Ranjan makes the case for consumer AI 5) Musk vs. OpenAI at trial 6) Potential outcomes of the case 7) Musk admits distillation? 8) Cloud services crush earnings 9) U.S. Senators ban themselves from trading prediction markets 10) CBS Sports’ shameless gambling article.

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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This week, the market erupted after OpenAI’s disappointing revenue numbers came to light in the Wall Street Journal. The company is barreling toward an (inevitable) $1 trillion+ IPO, and any hiccup in its story drives a mini-panic. After the chaos settled down though, the bigger, underlying story baked into the numbers got almost no attention: Consumer AI might be plateauing.

Generative AI’s current moment began with a consumer application in ChatGPT, but the technology is still struggling to find its true form in consumer apps, and even ChatGPT’s torrid growth seems to be ebbing. OpenAI, according to the Journal’s report, failed to hit its goal of 1 billion active ChatGPT users by the end of 2025 and may not even be there today (it reported 900 million weekly active users this February). Among chatbots as a whole, daily active user growth has flatlined, according to third-party analytics companies like Apptopia.

“If we include April, DAUs have fallen for 4 out of the past 5 months,” Adam Blacker, director of public relations at Apptopia, told me.

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While enterprise generative AI apps are taking off in the legal field, medicine, software development, and elsewhere, the corresponding consumer breakout hasn’t materialized. ChatGPT’s widespread use is certainly an exception, but the technology otherwise hasn’t translated to the series of consumer hits many anticipated at the outset. There is no mainstream generative AI companion, fitness app, life coach, or fantasy adventure game, for instance.

Consumer AI’s bumpy progress compared with enterprise’s boom was put in stark contrast in this week’s big tech earnings reports. If your company owned infrastructure supporting enterprise AI, you were sitting pretty. Amazon reported 28% growth in its Amazon Web Services division, a jump from the 17-18% quarters it had turned in recently. “The last time we saw growth at this clip, AWS was roughly half the size,” Amazon CEO Andy Jassy said. Google Cloud Platform, meanwhile, grew 63% and Microsoft Azure grew 40%.

The consumer side of tech earnings was a different story. Meta told investors it would spend even more money in its pursuit of ‘personal superintelligence’ than anticipated (up to $145 billion this year) and the market punished it, cutting it 8.5% on Thursday. Apple, the notorious AI ‘failure,’ felt none of the pain after its consumer products, including the iPhone and MacBook Neo, delivered stellar results. Apple, for the moment, seems wise to have forgone a massive investment in foundational model training in service of building a little-wanted consumer application.

To date, consumer AI has surged mostly through novelty. ChatGPT’s Studio Ghibli moment created a surge of interest — turning many new users on to the power of generative images — but the excitement ultimately faded. OpenAI’s enhanced voice mode for ChatGPT also created a spike in users, but the company still has work to do for that feature to reach its potential.

This doesn’t mean the AI moment is running out of steam. New agentic apps like Codex and Claude Code are booming in adoption. Anthropic is on track to make more than $30 billion this year on the back of Claude’s coding capabilities. And OpenAI’s latest GPT-5.5 model doubled Codex’s revenue in less than a week. Perhaps these ‘super apps’ will grab the public’s attention and become as standard as a phone or a laptop. But for the time being, the consumer slowdown is curious.

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What Else I’m Reading, Etc.

An AI coding agent deleted an entire company’s production code [Tom’s Hardware]

OpenAI CFO says demand for its products is “vertical” [Bloomberg]

Microsoft frees OpenAI from its exclusive cloud deal [WSJ]

Thanks to AI, memory chips are now unbelievably profitable [WSJ]

Elon Musk says he was a fool for investing in OpenAI [New York Times]

This Week On Big Technology Podcast: Mark Cuban: AI Hype vs. Reality, OpenAI’s Wasting $1 Trillion, Lebron vs. Jordan

Mark Cuban is an entrepreneur and investor. Cuban joins Big Technology Podcast to discuss how artificial intelligence is reshaping business, software, jobs, and education. Tune in to hear why he believes AI is an exponential shift, how companies should rebuild themselves around it, and why curiosity will matter more than ever in the AI era. We also cover the economics of foundation models, which software companies are most vulnerable, how young people should build careers with AI, and his thoughts on Sam Altman, Dario Amodei, and the future of the NBA. Hit play for a lively conversation on who wins, who loses, and how to stay ahead as AI transforms the economy.

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My book Always Day One digs into the tech giants’ inner workings, focusing on automation and culture. I’d be thrilled if you’d give it a read. You can find it here.

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Elon Musk and OpenAI will go to trial this week in a case that should be filled with drama and juicy revelations.

You’re probably going to hear a lot about personality conflict between Musk and OpenAI CEO Sam Altman. But here’s what’s at stake in the case, and what the outcome might be:

This is a legal battle over whether OpenAI lawfully evolved from a nonprofit, breaching an agreement to operate as a charity that the founders — including Musk, who invested $38 million — made at its inception.

Musk’s lawsuit hinges on two claims: Breach of charitable trust and unjust enrichment. OpenAI has denied the allegations, arguing that its structure had to evolve to recruit talent and build new AI systems. It also countersued Musk and xAI, claiming they interfered in OpenAI’s relationships with investors, customers, and employees.

The stakes are high. Musk is asking the court for OpenAI and Microsoft to give up as much as $134 billion, which he says should be directed to OpenAI’s charitable mission through a restructuring that would force it to operate entirely as a nonprofit.

If the case reaches the remedy phase, the judge would decide how much each company would pay. Musk is also asking the court to remove Altman and OpenAI President Greg Brockman from the company.

The court could hear testimony from a list of high-profile tech execs that includes Musk, Altman, Brockman, Microsoft CEO Satya Nadella, former OpenAI CTO Mira Murati and OpenAI co-founder Ilya Sutskever. Others on the list include ex-OpenAI board members Tasha McCauley and Helen Toner.

Timeline & context for the case:

• The liability phase will be heard by an advisory jury, which would offer a nonbinding verdict for the judge to consider.

• The judge plans to give Musk and OpenAI each 22 hours to present their case in the liability phase, with Microsoft getting 5 hours.

• If Musk gets past the liability phase, the remedies phase is expected to begin May 18, which would have new time allotments for each party.

Some are placing bets on whether Elon will win his case. As of Sunday night, around $390,000 in bets have been placed on Kalshi since mid-January, with the platform estimating his chances at 49.9%.

Pre-trial filings are full of colorful claims in documents from all parties, including emails and other rhetoric. Here’s two:

“Obviously we’d comply with/aggressively support all regulation.” - Sam Altman (May 2015) in a May 2015 email to Elon Musk.

“Because we don’t have any financial obligations, we can focus on the maximal positive human impact and disseminating AI technology as broadly as possible.” — Sam Altman to Elon Musk email (December 2015)

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The Intelligence Report

• OpenAI’s exclusivity agreement with Microsoft is over, making room for Amazon and Google to sell its models via their cloud platforms.

• On Sunday night, less than 24 hours before OpenAI heads to court, OpenAI published a new blog post from Sam Altman titled “Our Principles.”

• Meta and Amazon signed a multibillion dollar deal for AWS to power the social giant’s agentic AI efforts using Amazon’s Graviton CPU chips.

• Google debuted a number of updates at Cloud Next 2026, including two new chips, a new Gemini Enterprise Agent Platform, a new Gemini Enterprise App, and an agentic data cloud.

• OpenAI debuted GPT-5.5 (aka “spud”), which you can hear more about in Big Technology’s special edition podcast episode with Greg Brockman.

• Meta reportedly plans to start installing tracking software on employee computers to monitor mouse movements and keystrokes for AI training data.

• Google announced plans to invest up to $40 billion in Anthropic just days after Amazon announced it would invest another $5 billion now and up to $20 billion more later on.

• White House pushed out Collin Burns, a former Anthropic safety researcher, just four days into his new role on an AI safety team with the Commerce Department.

• Nvidia-backed VAST Data raised $1 billion and is now worth $30 billion, a sign of how fast AI infrastructure companies are growing today.

• Alex appeared on CNBC to discuss the Musk vs. OpenAI case this morning.

• Listen to this great report about the case from NPR’s John Ruwitch (Alex is quoted).

• It’s once again earnings week. Alphabet, Amazon, Microsoft and Meta are all scheduled to report results April 29. Apple, Samsung, Reddit and Roblox will all go a day later.

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Where we’ll talk about this story, the latest in AI, the week’s podcast, and plenty more. You can sign up via the link below:

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OpenAI released its long-awaited GPT-5.5, or “Spud,” model on Thursday. The model is “a beginning point,” OpenAI President Greg Brockman told me shortly after release, setting up a series built to take action on your behalf and accomplish your goals. GPT-5.5, Brockman said, is adept at coding and computer control, able to act without arduous amounts of …

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Google’s rapid AI improvement has lifted its once-beleagured cloud division, with startups and enterprises now clamoring to build on top of the newly competitive Gemini. And this week, the company is set for its next big moment, as it brings together its stakeholders at the fast-growing Google Cloud Next event in Las Vegas.

Though Google Cloud Platform s…

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Jensen Huang, master of media, didn’t have it this time. The Nvidia CEO stumbled in an interview with podcaster Dwarkesh Patel this week, losing his calm veneer — while issuing an instant-classic “I didn’t wake up a loser” quote — and struggling to make satisfying arguments on his company’s two most critical issues.

Huang’s built Nvidia into the world’s largest company by making its technology essential to AI computing, but when pressed about competition and restrictions on selling into China, his answers fell flat. Nvidia stock didn’t flinch following the conversation (demand for its technology remains insatiable) but Huang’s responses didn’t exactly slam the door shut on its long-term challenges either.

Nvidia’s biggest threat today is well-funded competitors creating purpose-built AI chips that do a good enough job training and running AI models. Google’s Gemini and Anthropic’s family of models, including the latter’s Mythos, have been built outside of Nvidia’s ecosystem, a counterweight to Nvidia’s dominance. In the interview, Huang spelled out that Mythos, a model reportedly so powerful Anthropic won’t release it to the public, was built on basic computing hardware.

“Mythos was trained on fairly mundane capacity,” Huang said. “And a fairly mundane amount of it.”

Huang’s Mythos reference was meant to illustrate that China could build strong AI models without Nvidia technology (more on that shortly) yet highlighted that Nvidia isn’t a necessary component in frontier AI models today. If Anthropic could build Mythos on “mundane capacity,” then perhaps Nvidia isn’t a must-have for its counterparts.

Anthropic, to be sure, has struggled to meet demand using its current compute architecture. Its users often complain about Claude’s rate limits. And, per Huang, it’s almost solely responsible for Nvidia’s competitors’ growth. “Anthropic is a unique instance, not a trend,” Huang said. “Without Anthropic, why would there be any TPU growth at all? It’s 100% Anthropic. Without Anthropic, why would there be Trainium growth at all? It’s 100% Anthropic.”

Still, of the top three AI labs in the world, as Patel noted, only OpenAI is reliant on Nvidia’s GPUs. Meta and Grok, which have made massive GPU investments, aren’t faring as well as hoped. This doesn’t mean Nvidia is cooked, but it could cut into growth over time. And Huang was uncharacteristically flat-footed on that line of questioning.

The China Question

Export restrictions on sales to China is another meaningful threat that looms over Nvidia’s business, but Huang didn’t persuasively argue against them either. He simply refused to entertain the possibility that selling more powerful chips to China could enhance that country’s offensive cyberwarfare capabilities.

Asked repeatedly whether he would acknowledge the potential threat that could result from giving China more powerful chips, Huang evaded time and again. “I just want you to acknowledge that any marginal sales for the American technology industry is beneficial,” Huang retorted in one exchange.

Huang isn’t in the easiest position in these interviews. He must balance his relationship with the U.S. government, China’s leadership, his employees, the public, and shareholders. This may have hamstrung him from making the strongest case against export controls. That is: Exporting Nvidia’s technology worldwide means there’s a better chance American values will be baked into LLMs and their applications moving forward. Huang probably couldn’t say that outright, out of concern for China’s reaction. But he did hint at it in the strongest part of the interview.

“We get the benefit of American technology leadership,” Huang said of dropping export controls. “We get the benefit of developers working on the American tech stack. We get the benefit, as those AI models diffuse out into the rest of the world, that the American tech stack is therefore the best for it. We can continue to advance and diffuse American technology. That, I believe, is a positive.”

Huang’s interview has been something of a Rorschach test in the tech industry since its release. For some, he valiantly made the case for Nvidia against an unnecessarily adversarial interviewer. For others, he alarmingly lost his composure in the face of standard, informed questioning. The truth is probably somewhere in the middle: Nvidia remains a dominant, key driver (and beneficiary) of the AI boom. It’s also not invincible. No company is.

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What Else I’m Reading, Etc.

Anthropic asks religious leaders if Claude can be a “child of God” [Washington Post]

Dario Amodei is heading into the White House for peace talks [Axios]

Allbirds, somehow, is a GPU company now? [Bloomberg]

Google’s AI mode comes to Chrome [Techcrunch]

SantaCon leader accused of conning the santas [New York Times]

This Week On Big Technology Podcast: The Pentagon’s AI Plan + Behind the Anthropic Fight — With Under Secretary of War Emil Michael

Emil Michael is the Under Secretary of War for Research and Engineering at the Pentagon. Michael joins Big Technology to discuss how AI is transforming the Department of War, from targeting systems to drone warfare to cyber defense. Tune in to hear his account of why the Pentagon designated Anthropic a supply chain risk, what actually happened in the contract negotiations, and whether the decision was wise. We also cover how the military’s Maven Smart System works in practice, what the U.S. learned from drone warfare in Ukraine and Iran, and whether the Pentagon Pizza Index is credible. Hit play for one of the most candid conversations you’ll hear about AI and national security.

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After Anthropic’s introduction of Mythos — the mega-model it said is too dangerous to release to the public — OpenAI is poised for AI’s next big moment with “Spud.”

“Spud” is a massive new base model that OpenAI President Greg Brockman recently told us is “a new pre-train” that can understand instructions and context better. Brockman said Spud will be better “qualitatively” and “quantitatively” and better at solving “much harder problems.” Along with Mythos, it’s in a class of new, larger AI models whose performance will reveal just how much improvement is left in making AI models bigger.

“There’s this thing called ‘big model smell’ that people talk about, where when these models are just actually much smarter and much more capable, they bend to you much more,” Brockman said. “And you feel it — when you ask a question and the AI doesn’t quite get it, it’s always so disappointing. You have to explain it, you’re like: you really should be able to figure this out.”

Mythos, which Anthropic revealed last week, is a new general-purpose model the company says is too powerful to release publicly due to cybersecurity risks. It launched Project Glasswing — a closed program giving access to roughly 50 partners including Apple, Microsoft, Google, Nvidia, CrowdStrike, JPMorgan Chase, and others. Anthropic illustrated the risks by including an anecdote in Mythos’s system card about a time Mythos successfully escaped its sandbox, which a researcher only discovered after receiving a surprise email from the model while eating a sandwich in a park.

Framing AI models as too dangerous to release and available only to a select group is starting to look like a new trend. When OpenAI recently hinted at a separate cybersecurity product it’s developing, company officials said it also plans to release it only to a small partner group.

That raises a number of questions worth considering as these models come out: Has “most dangerous” simply become a new way of signaling “most powerful”? Is all the scary stuff mostly marketing? Is this danger talk a cover story for a lack of compute needed to deliver these models to the public? And who benefits most when access to the most capable models is concentrated among a handful of companies that already dominate the industry?

It’s also hard not to notice the names Anthropic and OpenAI are using for their next-generation models. Spud calls to mind something plain, unassuming, and earthy. Mythos resembles something much more lofty and lasting. Like any myth, there’s always a chance the story is grander than reality.

For now though, companies and governments are taking the companies’ claims seriously. After Anthropic announced Mythos, the Treasury and the Fed both reportedly warned major banks about new AI risks posed by Mythos’s capabilities. The head of the International Monetary Fund is also reportedly worried and said that “time is not our friend on this one.”

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The Intelligence Report

• A Bank of America analyst raised their chip forecast to $1.3 trillion, which is $300 billion higher than four months ago.

• Meanwhile, a recent report from McKinsey suggests the semiconductor market could double to $1.6 trillion by 2030, up from $775 billion in 2024.

• AI inference is likely to drive more data center demand than training, according to McKinsey, which expects the former to surpass the latter by 2030.

• Meta announced its new Muse, the first in a new “Spark” family of models, marking the first release in a while from the company’s superintelligence team. The company also announced a new $21 billion deal with CoreWeave to expand cloud computing capacity.

• Anthropic said its run-rate revenue has surpassed $30 billion, more than 3x what it was at the end of 2025. The update came as the company announced a new agreement with Google and Broadcom for multiple gigawatts of compute

• Amazon CEO Andy Jassy used his 2025 shareholder letter to make a bullish case for the company’s AI ambitions, defending heavy AI spending, plans to “flatten” the organization, and outlining a vision for how companies should navigate the AI era.

• OpenAI published a new policy blueprint pitching a broad social contract around AI that includes workforce support, public wealth funds, and institutional reforms.

• The European Union is exploring whether ChatGPT should be considered a large online search engine under the Digital Services Act, which would make it subject to much stricter EU regulation related to AI and data privacy.

Federal and State Governments Zero In on AI

Data centers are facing growing pressure at the national, state, and local level. Congress, state lawmakers, and local governments are exploring ways to ban, pause and, otherwise regulate AI infrastructure amid concerns about rising electricity costs, environmental impacts and economic uncertainty.

• Last week, Maine lawmakers passed legislation that would create a moratorium on data centers larger than 20 megawatts through November 2027 and create a new state council to evaluate issues related to current and future potential data centers in the state. The bill still needs to be signed by Governor Janet Mills, who is also running for a seat in the U.S. Senate, where she’s been trailing by double digits.

• While Maine might be the first state, nearly a dozen other states are considering their own pauses or bans on data center construction, including Virginia, Georgia, New York and Vermont.

• Governors are also applying pressure. A bipartisan group of governors from Illinois, Delaware, Maryland, and Pennsylvania sent a letter last week to PJM Interconnection — the grid operator serving much of the mid-Atlantic and Midwest — calling for data centers to pay more for power and for stronger consumer protections. One of the signatories is Illinois Gov. JB Pritzker, who has separately proposed a pause on his state’s data center tax credit program.

• In Congress, Sen. Bernie Sanders and Rep. Alexandria Ocasio-Cortez introduced a new bill last month that would create a national moratorium on data centers with no fixed end date, keeping the ban in place until Congress passes a broader package of AI safety and labor protections. The bill would also ban the export of semiconductors, chips, and other computing hardware to countries that don’t have equivalent AI regulations in place — a provision that would have sweeping implications for U.S. trade policy.

This week, Congress returns from recess with committees in both chambers scheduled to discuss AI issues and other tech-related topics.

• On Wednesday, the House Education and Workforce Committee will meet to discuss AI’s economic impact on workers and employers. The hearing will be the latest in an ongoing series to understand AI’s impact on issues like work broadly, workplace safety, teaching in the AI age, and employer-led workforce training.

• The Senate Commerce, Science and Transportation Committee will meet on Tuesday for a markup of several tech-related bills.

• Two bi-partisan social media bills are on the agenda. One is the “Stop the Scroll Act,” which would direct the FTC, with Surgeon General approval, to require mental health warning labels on social media platforms. The other, the “No Fentanyl on Social Media Act,” would require the FTC to report to Congress on minors getting access to fentanyl on social media.

• The National Quantum Initiative Reauthorization Act of 2026 would accelerate quantum technology development and real-world applications. The bipartisan bill has already received endorsements from Google, Microsoft, IBM, the American Physical Society, and various quantum companies and academic institutions.

Big Technology Podcast Friday Edition: Anthropic’s Mythos Dilemma, Violence Against AI, Tokenmaxxing at Meta

Ranjan Roy from Margins is back for our weekly discussion of the latest tech news. We cover: 1) Anthropic’s new Mythos preview 2) Is Mythos marketing or a legit breakthrough? 3) The Mythos sandwich guy story 4) OpenAI and Anthropic’s brewing 1st party vs. API conflict of interest 5) The Meta-Harness 6) Violence against AI on the rise 7) Maine is going to pass a data center moratorium 8) Was Medvi really a $1.8 billion two person startup? 9) Tokenmaxxing is all the rage

You can listen on Apple Podcasts, Spotify, or your podcast app of choice

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The wooden front door was filled with bullet holes.

There were twelve distinct entry points, all shot by a crazed opponent of data centers into the home of Indianapolis legislator Ron Gibson. The bullets landed just steps away from where Gibson’s 8-year-old son had built LEGOs a day before. On the front porch, the shooter left a handwritten note. “No Data Centers,” it said.

The shooting happened on Monday, and was mostly ignored in a week dominated by news of a ceasefire with Iran. But it was yet another data point — and definitely the most disturbing one — indicating a growing, serious unease with AI and its associated physical infrastructure.

Today’s AI builders are in a bind: They need to massively increase the quantity of data centers to improve their models, but as their models get better, the public’s support for their buildouts is eroding.

As data centers show up in local communities, far more Americans say their impact is more negative than positive for quality of life, the environment, and home energy costs, according to Pew Research. Only 6% of those polled said AI infrastructure has a positive effect on the lives of people nearby. The faceless, imposing buildings have become symbols of the tech industry’s interest in delivering AI quickly despite the uncertainty it will bring to people’s lives.

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Already, some legislators are introducing bills to stop data center construction, and some are close to going through. Maine, for instance, is en route to become the first state to pass a data center construction moratorium through November 2027. And though unlikely to pass, Senator Bernie Sanders and Congresswoman Alexandria Ocasio-Cortez are testing the appetite for further restrictions on a national level with a bill to pause data center expansion in the U.S.

Ilya Sutskever, the OpenAI co-founder and current CEO of Safe Superintelligence, once said that when the race toward AGI breaks out, data centers could wallpaper the planet. “It’s pretty likely,” he said, “the entire surface of the earth will be covered with solar panels and data centers.”

But the public has at least some say in how its resources will be used. And, though investment continues to pour in, achieving Sutskever’s vision will not be as simple as throwing dollars at the problem.

Any political opposition to the AI buildout would further complicate these ambitious infrastructure projects, which are already struggling to stay on schedule. Today, there’s concern that as many as half the scheduled data centers meant to come online this year could be delayed. Power and equipment constraints are playing a key role in the delays, but rising political opposition could loom large as the projects attempt to scale up.

Delivering AI is a famously resource-intensive process. And now, just as the AI models are showing their most promise yet, the public is turning against the foundational part of the stack (with a chance to impede their progress in a way they never could with software or social media). Ultimately, the technology will likely win out. But the road to further progress could be much bumpier than anticipated.

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This Week On Big Technology Podcast: OpenAI vs. Anthropic’s Direct Faceoff + Future of Agents — With Aaron Levie

Aaron Levie is the CEO of Box . Levie joins Big Technology to discuss the battle between OpenAI and Anthropic as their product roadmaps converge around coding, enterprise, and AI agents. Tune in to hear where AI agents are actually gaining traction, why coding has emerged as the breakthrough use case, and what stands in the way of broader adoption across knowledge work. We also cover trust and security concerns, the messy reality of enterprise data, and the debate over whether value will accrue to the labs or the application layer. Hit play for a sharp look at where the AI market is heading and who stands to benefit most.

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OpenAI is in the midst of yet another strategic shift. The company has abandoned video generation, it’s building a ‘superapp’ that combines coding, chat, and browsing, and it’s zeroing in on a use case where AI uses your computer to make you more effective at work, and assist you everywhere else.

Underlying the shift, OpenAI President Greg Brockman told me, is a belief that the company’s core large language models are the right architecture to bet on, and that tree of AI is growing increasingly capable and reliable.

“There’s been this debate about how far text models can go,” Brockman said. “I think we have definitively answered that question — it is going to go to AGI. We have line of sight to much better models coming this year.”

In an extended Big Technology Podcast interview, Brockman offered revealing comments about the company’s research direction, how far it can push its Codex coding assistant, and the logic of supporting all of this with mountains of compute.

You can read the full Q&A below, edited lightly for length and clarity, or listen on Apple Podcasts, Spotify, or your podcast app of choice.

Alex Kantrowitz: OpenAI has shut down video generation and is preparing a ‘superapp’ that will combine business and coding use cases. From the outside, it looks like you were winning in consumer but decided to shift toward business. Why do that?

Greg Brockman: We’ve been in a world where we’re developing this technology, deep learning, to really see, can it have the positive impact that we have always pictured? And we’ve separately had an effort to actually try to deploy this technology, whether that’s to help sustain the business, to start getting some practice with getting real world impact, those kinds of things.

We’re at a moment now where we’ve really seen this technology is going to work, and that we’re moving out of testing on benchmarks and these almost cerebral demonstrations of capability to it actually being the case that for us to develop it further, we need to see it in the real world and get feedback from how people are using it in knowledge work, in various applications. And so the way I think about it is that this is a bigger strategic shift because of the phase of the technology.

It’s not so much that we’re saying we’re moving from consumer to B2B, what we’re saying is — What are the most important applications that we can focus on, because we can’t focus on everything. What are the things that we can bring to life that will actually synergize together as we build them, and that will deliver meaningful impact and help elevate everyone?

When we look at the list, there’s consumer, which you can think of it as many things, but there’s a personal assistant — something that knows you, that’s aligned with your goals, it’s going to help you achieve whatever it is that you want in your life. There’s also creative expression and entertainment and many other applications. On the business side, maybe if you zoom out, it looks more like: You have a hard task, can AI go do it? Does it have all the context to do all these things?

For us, it’s very clear that the stack rank includes two things at the top. One is the personal assistant, the other is the AI that can go and solve hard problems for you. And when we look at the compute we have, we are not even going to have enough compute to fund those two things. And then once we start adding in many other applications, many other things that AI is going to be very useful for and is going to help people with, we just can’t possibly get to all of them.

So this is a recognition of the maturation of the technology and the incredible impact it’s going to have very quickly, and our need to prioritize and to actually pick the set of applications that we want to really bring to the world.

I’ve heard you compare OpenAI’s various bets to Disney’s, where you have a company with a core advantage that it farms out in different ways. Disney has Mickey Mouse, and it can do movies, theme parks, Disney+. At OpenAI, you have the model, and you can do video generation, a personal assistant, enterprise work. Is that no longer possible?

In some ways, that story is even more true than it’s been. But the thing that’s important to realize is technologically, the Sora models — which are incredible models, by the way — are a different branch of the tech tree than the core reasoning GPT series. They’re just built in a very different way. And to some extent, we’re really saying that pursuing both branches is very hard for us to do for these applications.

We are actually continuing the Sora research program in the context of robotics, which I think is very clearly going to be a transformative application, which is still a little bit in the research phase.

And so it’s a recognition that for this moment, we really need to put the primary focus on developing the GPT series. And that doesn’t just mean text. It doesn’t just mean cerebral things. For example, bidirectional communication, having a great speech to speech interface — that is something that also is going to make this technology very usable and very useful. But it’s not a different branch of the tech tree. It’s all one model, and we just sort of tweak that in slightly different ways.

If you branch too far and you have two different artifacts, it is very hard to sustain in a world where there is limited compute, and the reason there’s limited compute is because there’s so much demand. There’s so much that people want to do with every single model that we create.

Betting on Text vs. World Models

Why is your bet on the GPT model tree, when you had been seeing real progress with Sora?

The problem in this field is too much opportunity. The thing that we observe very early on in OpenAI is that everything we could imagine works now. There’s different levels of friction associated with it, different amounts of engineering effort, different compute requirements, all those things, but every single different idea, as long as it’s kind of mathematically sound, you actually can start getting some pretty good results.

That shows you the power of the underlying technology of deep learning, the ability to really take any sort of problem and to get to the meat of it, to have an AI that really understands the underlying rules that generated the data. So it’s not about data itself, it’s about understanding the underlying process and then being able to apply it in new contexts. So you can do that in world models. You can do that in scientific discovery. You can do that in coding.

And I think that where we are as we think about the rollout of this technology is that there’s been this debate of, how far will the text models go, how far can text intelligence go? Can you have a real conception of how the world operates? And I think that we have definitively answered that question — it is going to go to AGI. We see line of sight. And at this point we have line of sight to much better models that are coming this year, and the amount of pain within OpenAI that we’ve had to decide how to allocate compute goes up, not down, over time.

And so I think that maybe the core of it is that it’s about sequencing and timing, and that in this moment, the kinds of applications that we’ve always dreamed of are starting to come into reach. For example, solving unsolved physics problems — we had this result recently where a physicist had been working on a problem for some time, he gave it to our model, 12 hours later we have a solution. And he said this is the first time he’s seen a model where he felt like it was thinking, that it felt like this is a problem that maybe humanity would never solve, and our AI solved it. When you see something like that, you have to double down, you have to triple down, because we can really unlock all of this potential for humanity.

And so for me, it’s not about relative importance of these things. It’s more about what is OpenAI’s mission of delivering AGI to the world, our vision of how it can benefit everyone, and the fact that we have a tech tree that we see how to just push it, how to do the engineering, do the further science and research to then have that come to fruition.

Does open AI potentially miss something by doubling down on the text model tree?

So two answers. One is: absolutely, yes. In this field, you do have to make choices. You have to make a bet, and that’s actually where OpenAI started — we really said, what is the path to AGI that we believe in? And really focused hard on that. The sum of random vectors is zero, but if you align your vectors, then you can go in a direction.

But the second point is — it’s actually image generation that has been very, very popular within ChatGPT, and that’s something we’re continuing to invest in, continuing to prioritize. And the reason we’re able to do that is because it’s not actually on the world model, diffusion model tech branch — it’s actually based on the GPT architecture. And so there, even though it’s a different data distribution, the actual core technology, the core stack, it’s all one thing.

That is actually the pretty wild thing about what AGI is, is that sometimes these very different looking applications — between speech to speech, image generation, text — and text is, by the way, itself many facets, like science and coding and personal wellness information, those kinds of things — all of that you can do in one technological envelope. And so a lot of what I’m looking at, and what we as a company are looking at from a technological perspective, is how to have as much unification of our efforts, because we really see this technology as being something that’s going to uplift and power the whole economy. The whole economy is a massive thing, and so we can’t possibly do all of it, but we can do our part.

That’s the general part in artificial general intelligence. That’s the G.

That’s the G…

Our conversation continues below…

Should Software Companies Embrace AI or fight it? — With Asana Chief Product Officer Arnab Bose (sponsor)

Arnab Bose is the chief product officer of Asana. Bose joins us to discuss whether software companies should embrace AI or fight it as agents begin to reshape how work gets done. Tune in to hear how Asana is thinking about AI Teammates, the future of work management, and what onboarding AI into an organization looks like in practice. We also cover Asana’s work with leading model providers, the case for and against open source, and what widespread agent adoption could mean for labor. Hit play for a smart conversation about whether AI strengthens software companies like Asana or threatens to make them obsolete.

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OpenAI’s Super App

Speaking of unifying things. What is this ‘super app’ going to be? It’s going to bring together coding, browser, and ChatGPT?

That’s right. We want to build an endpoint application for you that really lets you experience the power of AGI — the generality. Think about what chat is today. I think chat is really going to become your personal assistant, your personal AGI. An AI that’s looking out for you, that knows a lot about you, that’s aligned with your goals, that’s trustworthy, that kind of represents you in this digital world.

Codex you can think of as — right now it’s been a tool that we built for software engineers, but it’s becoming Codex for everyone, that anyone who wants to build can use Codex and to get the computer to go do the thing that they want. And it’s not just about the actual software anymore. It’s really about almost the use of a computer — whether it’s to set up, I used to set settings on my laptop, I forget how to set the hot corners. You ask Codex to do it, it just does it. That’s what computers were always supposed to be — contort to the human, rather than the human contort to them.

And so imagine one application where anything you want your computer to do, you can ask it. There’s computer-use browsing built in, for an AI to be able to actually use a web browser, and for you to be able to oversee what the AI is doing. All of your conversations, regardless of application — whether it’s for chat or whether it’s for code, whether it’s for general knowledge work — that’s all unified in one way, so that the AI has memory, knows about you. That is what we are building.

But it’s really an iceberg, because that’s the tip. What to me is actually much more important is the technological unification. The thing that’s really changed over the past couple of years has been that it’s no longer just about the model. It’s about the harness. It’s about how does the model get context? How is it connected to the world? What actions can it take? How does the loop of interacting with the model work as you get new context?

All of that was something that we had multiple implementations of, slightly different, and we’re converging it. We’re going to have one version of that, and almost end up with this AI layer that can be pointed at specific applications in a very thin way. So you can build a little plugin, a little UI if you really want something that’s great for finance, great for legal, but you generally won’t have to — because this one super app will be very broad.

This app is for business use cases, personal use cases?

Your laptop – is it for personal? Is it for business? It’s both. It’s your personal machine that gives you an interface to this digital world, and that’s what we want to build.

So just talk a little bit about from a non-business standpoint — I’m using the super app in my personal life. What am I using it for? How does my life change?

I would think of it as: personal life, just the way that you use ChatGPT right now. And people use it for such a diversity of really amazing applications — sometimes that’s just asking, I’m going to give a speech at a wedding, can you help me with drafting it? Can you give me some feedback on this idea that I have? I’m working on a small business, can you give me some ideas there? Which maybe starts to bridge between personal and work. Any of those questions should be things that you can go to the super app for and it answers.

But if you think about what ChatGPT has been, it’s already been evolving. It used to not have any memory — it’s just the same AI for everyone starting from scratch. It’s almost like talking to a stranger. It’s way more powerful if it remembers the interactions you’ve had. It’s way more powerful if it has access to context — if it’s hooked up to your email and to your calendar, and really knows your preferences and has this almost deeper set of past experiences with you that it’s able to leverage to achieve your goals. Pulse is a feature in ChatGPT right now where every day, it surfaces for you things that you might be interested in based on what it knows about you. So in the personal capacity, the super app will be doing all of that, and will be doing it in a much deeper and richer way.

When are you planning to ship it?

We’re taking incremental steps to get there. Over the next couple of months we should have shipped the complete vision of what we’re talking about here, but it’s going to come in pieces.

The place that we’re starting is with, for example, the Codex app today — which is really two things in one. It’s a general agent harness that can use tools, and it’s also an agent that knows how to write software. That general agent harness can be used for so many different things. You hook it up to spreadsheets, you hook it up to Word documents, it’s able to help you with knowledge work. And we’re going to make the Codex app just so much more usable for general knowledge work, because what we’ve already seen within OpenAI is all this organic adoption of people using it for that. So that’ll be the first step, and there are many to come.

Codex’s Potential Beyond Software Engineering

I was speaking with one of your colleagues yesterday taking a look at Codex, and he mentioned that someone using Codex had instructed it to help them with video editing. It builds a plugin for Adobe Premiere, started separating it into chapters and started the edit.

I love hearing that. That’s exactly the kinds of things that we want this system to be useful for.

The Codex app itself was originally built for software engineers, and the current usability of it for non-software engineers is actually quite low, because there’s a bunch of little things where when you set things up, you run into some error that a developer knows what it means, knows how to fix it — it’s just kind of what we’re used to. But if you’re not a developer, you’re like, what is this? This is not something that I’ve encountered before. And despite that, we are seeing people start to use this who have never programmed before, to be able to build websites, to be able to do exactly the kinds of things you said — to be able to automate their interactions with different pieces of software, to be able to get lots and lots of leverage. Someone on our communications team uses it hooked up to Slack and to their email — they’re able to go through a bunch of feedback and synthesize it very well.

People who are very motivated can jump through the hoops and then get great return from it. We did the super hard part of building an AI that is really smart, capable, and can actually accomplish your task. Now we have to do the much easier part, in some sense, of making it broadly useful and removing these barriers to entry.

Looking at the competitive landscape, Anthropic has the Claude app. You can use Claude the chatbot, Claude Code, so they have a version of a super app of their own. What do you think Anthropic saw that got them to this position earlier, and what do you think your chances are of catching up there?

If you rewind 12, 18 months, we have always been focused on coding as a domain. We always had the best numbers on different programming competitions, these very cerebral things. But the thing that we didn’t invest in as much was that last mile of usability, of really trying to think about — okay, this AI is so smart it can solve all these great programming competitions, but it’s never seen someone’s real world codebase, which is messy and not quite as pristine as the world that it sort of has experienced. And I think that is something that we were behind on.

But about maybe mid last year is when we got very serious about that, and we had a team very focused on: what are all the gaps, what are all the kinds of messiness of the real world that we haven’t encountered? How do we actually get training data, build training environments that let the AI experience what it’s like to actually do software engineering, be interrupted in weird ways, all those things?

I’d say at this point we are caught up — when people go head to head, us versus competitors, people tend to prefer us. We know we’re digging in on front end, we’re going to fix that.

But this is the general motion that we’ve been taking — to say that usability, thinking about the product end to end, not just a model and then building a separate thing, but really thinking of it as one product. When we’re doing the research, we’re thinking about how it will be used. That has been a motion that we’ve been changing within OpenAI. And so the way I would look at it is that we have incredible step-up models coming all year — I look at the roadmap, it’s truly inspiring, what will be possible. And then we’ve been really focusing now on: let’s also get the last mile usability.

You just used the phrase ‘we’re caught up.’ Is there a different vibe within the company, whereas now, instead of being the one that’s far ahead, you’re in a real fight?

For me personally, the scariest moment at OpenAI was actually after we launched ChatGPT. And I remember being at the holiday party and just feeling this vibe of: we won. I have never felt that. I was like: No. We are the underdog, and we always have been. The competitors in this space are established companies that have just so much more capital, so much more human resources, data, the whole thing. Why is OpenAI able to compete at all? And to some extent, the answer is only because we never feel complacent, where we always feel like we are the challenger.

And it actually, for me, has been a very healthy thing to see us start to see that in the marketplace, to see other competitors emerge and do a good job. In my mind, you can never fixate on your competitors. If you focus on where they are, then you’ll be where they are, and they’ll already have moved. And I think that’s what’s been happening in the other direction — a lot of people focus on exactly where we are and we get to move.

I’d say that the world that we’re in is one where you’re never as good as they say you are, you’re never as bad as they say you are. It’s been very steady. The core of the model production — that is something where I actually feel extremely confident in our roadmap and the research investments we’ve been making. And on the product side, we have such great energy that’s all coming together to deliver this to the world.

Forthcoming GPT Models: What’s Spud?

You’ve foreshadowed a couple of times already that you have some good models on the way. What is Spud? The Information said Sam Altman has told the staff the team believes this new “Spud” model can really accelerate the economy. What is it?

It’s a good model, but I think that it’s really not about any one model. The way that our development process works is you have pre-training, so you produce a new base model that is then the foundation that we build further improvements on top of. And that is always a huge effort across many people in the company — that’s where I’ve actually been spending most of my efforts over the past 18 months, really focused on our GPU infrastructure, on supporting the teams that do all of the training frameworks to scale up with these big runs.

But then there’s a reinforcement learning process. So you take this AI that has learned lots of things about the world, and it applies that knowledge, and then we do a post-training process where you really say: okay, now you know how to solve problems, you practice it in all these different contexts, and then here’s the last mile of behavior and usability.

So I think of Spud as a new base, as a new pre-train. I’d say it’s like we have maybe two years worth of research that is coming to fruition in this model. It’s going to be very exciting. And the way that the world will experience it is just improved capabilities. For me, it’s never about any one release, because as soon as we have this one release, it’ll be an early version of what we have coming. We’ll do much more of each of these steps of the improvement process. And so where we’re going is we have this engine of progress that just moves faster and faster, and Spud is just one step along the way.

What do you think it’ll be able to do that today’s models can’t?

I think it’s going to be able to solve both much harder problems. I think it will be much more nuanced. It’ll understand instructions better, understand the context much better. There’s this thing called ‘big model smell’ that people talk about, where when these models are just actually much smarter and much more capable, they bend to you much more. And you feel it — when you ask a question and the AI doesn’t quite get it, it’s always so disappointing. You have to explain it, you’re like: you really should be able to figure this out.

And so I would think of it as: quantitatively, lots of shifts, and qualitatively, there will just be new things where you would have been frustrated before, you’d never use AI for it, and now you just use it without thinking very much. I’m super excited to see how it raises the ceiling — we’ve already seen these physics applications and things like that, and I think we will be able to just solve way more open-ended problems over way longer time horizons. And I’m also very excited to see how it raises the floor, where just for anything you want to do, it’s just so much more useful for you.

It can be kind of tough for everyday users to really feel the change. There was a lot of buildup before GPT-5 came out, and actually the initial reaction was somewhat disappointment among the public. But then I think people realized that for certain tasks it was really good.

With these next series of models, do you expect that it’ll really be felt in the trenches in certain occupations? Or do you think it will be a broadly tangible improvement for everyone?

I think that it will be a similar story — when you release it, there will be people who will try it and be like, this is night and day different from anything I’ve seen. And then there will be some applications where we weren’t necessarily intelligence-bottlenecked. And so if you have a model that’s more intelligent, maybe you won’t feel it right there.

But I think over time you will feel it, because the fundamental thing that shifts is how much do you rely on the system. We all interact with AI by having some mental model for what we think it can do. And that mental model shifts actually fairly slowly — as you get more experience, it does something magical for you, you’re like: oh wow, it can do that, I never imagined that.

We see this, for example, in applications like access to health information. I have a friend who used ChatGPT to understand different treatments for his cancer. He was told by doctors that he was terminal, that there was nothing they could do for him. He used ChatGPT to actually research a bunch of different ideas, and he was able to get treatment that way. And that’s something where you need to have some level of belief that the AI is going to be helpful in that application, for you to really put in the effort to get something out of it. And I think what we’re going to see is that for any application like that, it’s going to become so much more evident to everyone that the AI can help you. So it’s a little bit of the technology getting better, but it’s also our understanding of the technology shifting and catching up to that.

AI Takeoff

You have an automated AI researcher in the works, it’s supposed to come out this fall. What is that?

So the direction of travel right now: we are in this early phase of takeoff of this technology.

What does takeoff mean?

Takeoff is as the AI gets better and better on this exponential — and in part because we can use the AI to make the AI better, so our development process speeds up. But I also think when I think of takeoff, it’s also about real-world impact. In some ways, every technology is an S curve. Or if you zoom out, some of those S curves end up being an exponential. And I think that’s what we’re encountering right now. So the technology development is moving with increasing speed, and it’s this engine that’s picking up momentum. But it’s also in the world — there’s all of these tailwinds, because there’s chip developers that are getting more resourcing into their programs. There’s this economy of people who are building on top of it, trying to figure out how it fits into every different application. And all of that energy is just accumulating more and more into this takeoff phase of AI, becoming just a kind of sideshow to being the main driver of economic growth.

And so the researcher will be a moment where the AI — which we’re building right now — is doing a larger percentage of tasks that can run autonomously. And that doesn’t necessarily mean that we just let it off on its own and come back later and see if it did something good. We are going to be very involved in managing it — just like right now, if you have a junior researcher and you leave them on their own too long, they’re probably going to go down a path that’s not very useful. But if you have a senior researcher, or someone who has a vision, they don’t even necessarily need to know the mechanical skills — they will be able to provide feedback, review the outputs that the intern is producing, and provide direction in terms of the vision of what they want accomplished.

And so I think of this as a system that we’re going to build that will massively accelerate our ability to produce models, to make new research breakthroughs happen, to be able to make these models more useful and usable in the real world, and to do that at increasing speed.

So what’s it going to do? Are you going to say: go find AGI?

I think the way I think of it is something like that, to first order. And at a practical level, I would view it as taking the full end-to-end of what one of our research scientists does, and being able to do that in silicon.

Another way to think about takeoff is that progress in AI goes from incremental to gathering momentum and then sort of this unstoppable march to an intelligence that’s smarter than humans. Do you worry that there are possibilities for that process to go wrong?

Absolutely yes. I think that the way to get the benefits of this technology is also to really think about the risks. And if you look at how we’ve approached technology development from a technical perspective, we invest a lot in safety, security. A good example of this is prompt injections — if you’re going to have an AI that is very smart and very capable, hooked up to lots of tools, you want to make sure that it can’t be subverted by someone giving it a weird instruction. And that’s something that we’ve invested in quite a lot, and I think have really incredible results. We have an incredible team working on it.

It’s interesting to think about some of these problems where you can make analogies to humans — humans are also susceptible to phishing attacks, to being deceived in different ways, to not really understanding the full context of what they’re working on. And we bring those analogies into our development process and think about: whenever we develop a model, how do we ensure that it’s going to be aligned with people and be able to actually be helpful?

And that is something that we care quite a lot about. I think that there are bigger questions about the world, the economy, how does everything change, how does everyone benefit from this technology — they’re not purely technical, not purely something that OpenAI on our own will be able to solve. But yes, I think quite a lot about not just pushing forward the technology, but also really about: how do we ensure that we have the positive impact that is its potential?

Many of your counterparts have said, if everybody agrees to stop AI progress, we’ll stop it — and yey it doesn’t seem like it’s going to slow at all. Is the reward worth the risk?

I think the reward is worth the risk, but I think that is too coarse-grained of an answer in some sense. The way that I think about it is that we’ve asked from the beginning of OpenAI: what does a great future look like? How can this technology really be something that uplifts everyone? And you can think of there almost being two different angles. One is the centralization view of saying that, well, the way to make this technology safe is that you have only one actor building it, and so then you don’t have any pressures — you can really think about getting it right, and then figure out how to roll it out to everyone when it’s ready. That’s a pretty tough pill in some ways.

And I think that there’s a lot of properties that you can instead think about approaching differently, which we refer to as resilience. To think of it as this open system where there are lots of players who are developing the technology, but it’s not just about the technology — it’s about building societal infrastructure that helps this technology really go well.

If you think about how electricity has developed, that’s something where lots of people produce it, it actually has dangers and risks, but we all build our safety infrastructure in a diversity of different ways — around safety standards for electricity, around different ways of harnessing it, about how you scale it. There are regulations at these massive scales. Lots of people are able to use it in a democratized fashion. There are inspectors. There’s a whole system that’s been built around the needs of that technology, the proclivities of that specific technology.

And I think that one thing that we have really seen with AI is that it is something where we need this broad conversation. We need lots of people to be aware — if this technology is going to come and change everything for everyone, people need to participate in that. It can’t be something that’s done in secret by just one sort of centralized group. And so this has been, to me, a very core question to how this technology should play out, and something we really believe in — this resilience ecosystem that should emerge around the development of this technology.

70-80% to AGI

So you said we’re in takeoff. Nvidia’s CEO Jensen Huang said recently that he believes AGI has been achieved. Do you agree?

I think that AGI has a different definition to many people, and there are many people who would say that what we have right now is AGI. I think you can debate it. But maybe the thing that’s interesting is that the technology we have right now is very jagged — it is absolutely superhuman at many tasks. When it comes to writing code and those kinds of things, AI can just do it, and it really removes a lot of the friction to creating things. But there are some very basic tasks that a human can do that our AI still struggles with. And so it’s almost: where do you draw the cut line? It’s a little bit more of a vibe than a feeling, than it is science at the moment.

For myself, we’re definitely going through that moment. And if you were to show me five years ago the systems we have today, I’d have said: oh yeah, that’s what we’re talking about. But it’s just so different from anything we ever pictured. And so I think we need to adjust our mental models appropriately.

So you’re not there yet.

I’d say I’m basically like 70-80% there. So I think we’re quite close. I think it’s extremely clear that we are going to have AGI within the next couple of years in a way that is still going to be jagged, but where the floor of tasks — for almost any intellectual task of how you use your computer — the AI will be able to do that. Right now I have to give a little bit of an uncertain answer, because it’s almost like an uncertainty principle kind of thing — you can debate it. For my own personal definition, I think we’re almost there, and with maybe a little bit more, we will absolutely be there.

Greg, what happened in December 2025? It seems like it was an inflection point where all this idea of letting the machine code for hours uninterrupted went from theory to a moment where everyone said, I think I can trust this to keep going for a while.

New model releases really went from the AI being able to do like 20% of your tasks to like 80%, and that was this massive shift, because it went from being kind of a nice thing to do to — you absolutely need to retool your workflow around these AIs.

And for myself, I’ve very much had this moment where I have a test prompt that I’ve been using for years — build a website for me. I’d actually built this website back when I was learning to code, took me months. Over the course of 2025, it used to take like four hours and a bunch of different prompts to get it right. In December: one shot, just asked the AI one time, and it produced it, and did a great job.

So how did those models make the leap?

A lot of it is about the better base models. One thing about OpenAI is that we’ve been working on improving our pre-training technology for quite some time, and in that moment we got to see a little taste of what is going to be coming for the rest of this year. But it’s also really about not any one thing — we’re constantly pushing on every single axis of innovation.

The thing that’s very interesting about these models is that in some ways you get these leaps, and in some ways it’s all continuous. It didn’t go from 0% to 80% — it went from 20% to 80%. So in some ways it just got better. And I think we’ve actually seen this improvement continue with every single point release that we’ve had.

One of my engineers I work with very closely went from: he couldn’t get it to do the low-level, hardcore systems engineering he does, to now — he gives it a design doc, it actually implements it, adds metrics, observability, runs the profiler, improves it to the point that it’s the exact thing that he was hoping to produce. And so the way to think about it is: slowly, slowly, slowly — all at once. But it is all indicated by what’s working right now. Certainly within a year, sometimes much sooner, it’s going to be incredibly reliable.

And it’s surprised you, because I heard you talking not long ago about how Codex — this autonomous coder — was just for software developers. And earlier in this conversation, you said actually everyone can use this stuff. What led to your changed perspective on it?

I’d been focusing on Codex — it’s got the ‘code’ in it — as really being for coders, and thinking about people within OpenAI, because many of us are software engineers building for ourselves. It’s very natural to think that way.

But as this technology has been progressing, we’ve started to realize that the underlying technology we produced is mostly not about code at all. It’s mostly about solving problems. It’s mostly about being able to manage context and harnesses and think about how an AI should integrate and do work. And that’s something that becomes — even for code — suddenly anyone can have access, because you can manage something that’s going to go do work. If you have a vision, something you want to accomplish, you can describe your intent, the AI can execute, can get that done. But then it also starts to be like: why am I just focused on coding? There’s so much very mechanical skill associated with Excel spreadsheets, with presentations. And if the AI has the context, it has the raw intelligence now to be able to do these things at a great level. So if we can just make it more accessible, suddenly it goes from Codex is for coders to Codex is for everyone.

The OpenClaw Vision

You talked a little bit about the AI as something that will help run your life for you. Is that the logic for bringing the OpenClaw team in house?

The core thing about this technology is that figuring out how it’s useful, how people want to use it, what is the vision for agents, how it’s going to slot into people’s lives — that is a hard problem. And one thing I’ve seen across many generations of this technology is the people who really lean in, who have a lot of curiosity, who have a lot of vision — that’s a real skill, and that’s an emerging, very valuable skill in this new economy.

And Peter, who is the OpenClaw founder, is someone who’s got incredible vision and incredible creativity. And so to some extent it’s about the specific technology, but to some extent it’s really about: how do we take these capabilities and figure out how they slot into people’s lives? As a technologist, it’s very exciting, but as someone who is focused on bringing utility to people, that’s something that we are doubling down on and investing quite a lot in.

You had a pretty interesting quote about this recently, talking about getting these autonomous AI agents to work on your behalf. You said you become this CEO of a fleet of hundreds of thousands of agents that are completing your objectives, your goals, your vision, and you’re not in the weeds on exactly how different things are solved. And in some ways, this new way of work can make you feel like you’re losing your pulse on the problem. Is that good?

I think there’s a mixed bag. We need to acknowledge the strengths of what these tools can deliver and mitigate the weaknesses. Giving people leverage and agency, making it so that if you have a vision, something you want to accomplish, you can have a fleet of agents that will go do it for you. But if you think about how the world works, at the end of the day there’s an accountable party. If you’re trying to build a website, and your agent messes it up, and your user is affected — it’s not really the agent’s fault. It’s your fault. And so you need to care. And for people to use these tools right, you need to realize that human agency, human accountability — that’s a core part of the system. How the human uses the AI — that’s something that is deeply fundamental. And so the important thing is that as a user of these agents, you cannot abdicate responsibility. You cannot just say: the AI is just going to do stuff.

But you said feeling like you’re losing your pulse on the problem itself — that’s different than accountability.

To me they actually are linked together. Because the point is that if you’re a CEO, and you’re too far from the details — you’re running this company, this team, and you’ve lost your finger on the pulse — that is something that’s not going to lead to great results. And so the point I was trying to make is not that it’s a desirable thing for humans to not know about what’s going on.

There are some details that you probably don’t need to worry about, because you can trust they’ll be taken care of — like if you are working with a general contractor to build a house, there’s a bunch of details there that you probably don’t need to worry about. But at the end of the day, if there are details that are wrong, you should care about it. You should be aware. And so you cannot just blindly say I’m okay with losing my finger on the pulse. You need to lean in and say: I need to keep it there, to really understand the strengths and weaknesses. And as you disengage from some of these lower-level mechanical things, you should do it because you have built trust with a system that it will do a good job.

One last question about the models. You talked a little bit about the evolutions that the models have gone through — pre-training and fine-tuning, reinforcement learning that gets it more equipped to solve problems step by step and go out on the internet and do things. And now we’re in this moment where the models have learned through that process to use tools. What is next in that progression?

The world that we’re in is one of this increasing capability and depth of what the machine can do. Some of this is about: we’ve got tool use, but now we also need to actually build really great tools. Think about something like computer use — an AI that can actually use a desktop — then it is really able to do anything that you can do. But we also have to build a little bit for the machine to think about: how does enterprise credentialing work? How do audit trails and observability work? There’s a lot of technology to build to catch up with what the core model capability is.

And I think the overall direction of travel includes things like a really great speech interface, so you can just talk to your computer naturally — as natural as this conversation — and it understands you, it does what you need, it has good advice. You wake up in the morning, it says: here’s your daily report of how much progress your agents made overnight. Maybe it’s running a business for you — which I think is going to be a huge application of this technology. The democratization of entrepreneurship is absolutely coming. And it’ll say: here’s these problems, there’s this customer that’s upset, they want to talk to a real human, you should go talk to them. All of that is going to happen.

And then I think that the raising of the ceiling of ambition — of challenges humanity can solve — is also a next step for this technology, and we’re seeing the leading edges of it. The thing that I am just very excited to see is: if you remember AlphaGo move 37 — this move that no human ever would have come up with, that was creative, and it changed humanity’s understanding of the game — that is going to happen in every single domain. It will happen in science, in math, in physics and chemistry. It’s going to happen in material science, in biology, in healthcare, in drug discovery. But it may also even happen in literature, in poetry, in a bunch of other fields. It’s going to unlock human creative understanding and ideation in ways we can’t imagine right now.

Why do you think that hasn’t happened yet, given how strong you say the models are?

I think that there is an overhang of what the models are capable of and how people are using them. Our understanding of what is in these models is still emerging. So I think that even with no further progress, there’s still a massive shift that will happen. The economy being powered by compute and AI is still going to happen.

But I think there’s also something where what we’ve gotten very good at is training models on tasks that could be measured. So what we started with was math problems, programming problems where you have a perfect verifier. And a lot of what the progress has been in bringing us to more open-ended problems has been expanding the space of what can be evaluated. AI itself can really help with that — if AI is smart and understands things, you give it a rubric for how well a task goes. And for things like creative writing — is this a good poem? — that’s a much harder thing to grade. And so we’ve had less ability to teach the AI, for it to experience and try things out. But all of that is changing, and something that we have a lot of sight for.

AI Infrastructure Logic

As you shift really to these more agentic use cases, there’s been discussion about whether the bigger training runs really need to happen. If you get the model good enough, could you let it go out in the world, and then you can effectively get much of the uplift in areas that aren’t the pre-training, which is what these big data centers are needed for. What do you think about that argument?

I think it misses something very important for how the technology development goes, because it is absolutely the case that every single step of the model production pipeline multiplies, and so you want to improve all of them. And the thing that we see is: we improve the pre-training, it makes all the other steps much easier. And it makes sense, because it’s a model that is able to learn faster, a model that is already more capable to start when it’s trying out different ideas and learning from its own mistakes — that process is just faster, it needs to make fewer mistakes.

And so I think that the big shift has been from thinking of it as just training this cerebral system on its own, just making it bigger, to: it’s also about trying things out, it’s also about understanding how people are using it in the real world, and connecting that back into your training. But it doesn’t remove the value and the importance of continuing that research.

And the thing that I think has also shifted is we used to really just focus on the raw pre-training capability, but not think as much about the inference ability. And that’s been a big change over the past 24 months — to realize that it’s a balance. You can have this model that has all those great properties in the base, but then you really need it to be able to run at inference, because you need to do reinforcement learning, you need to serve it to the world. And that means you don’t necessarily go as big as you possibly could, because you also really think about all this downstream use, and you want the thing that has the best intelligence times that cost — to optimize those two things together.

Do you still need the Nvidia GPU if things move mostly to inference?

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