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Tag: Brainstorm AI

  • Outsiders see a circular economy. CoreWeave’s CEO sees a ‘violent change’ that’s rattling the supply chain down to the inside of the earth | Fortune

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    Addressing one of the most persistent critiques of the current artificial intelligence boom, CoreWeave CEO Michael Intrator pushed back against the narrative of a “circular AI economy” in an appearance at the Fortune Brainstorm AI conference in San Francisco.

    While skeptics often point to the tangled web of investments between chipmakers, cloud providers, and AI startups as a financial bubble, he argued that deep industry collaboration is the only viable response to a historic supply chain crisis.

    Circular is “the incorrect way of looking at it,” Intrator told Fortune Editorial Director Andrew Nusca, reframing the dynamic not as financial engineering, but as logistical necessity. “It’s a lot of companies working to address an imbalance that is distorting the globe.”

    The concept of the “circular economy” in AI suggests that revenue is merely being recycled between a handful of tech giants—such as Nvidia investing in CoreWeave, which in turn uses that capital to buy Nvidia chips. However, Intrator described the market conditions as a “violent change in supply demand,” adding that the only way to navigate such volatility is “by working together.”

    The ‘physical bottleneck‘

    According to Intrator, the primary constraint facing the AI sector is not funding or policy, but “a physical bottleneck associated with getting … the most performant compute into the hands of the most cutting-edge players.” This scarcity forces companies to cooperate in ways that may look insular to outsiders but are essential for survival, he insisted.

    The CEO recounted a recent conversation with a mining company boss, whom he declined to name. Intrator said he learned just how deep the supply chain is being impacted: “two levels deeper,” down to the raw metals and copper required to build the infrastructure. Intrator noted that the executive specifically requested industry-wide cooperation to meet production needs.

    The mining CEO explained that to get out of this jam, “we need to work together as a group.” If he said the same thing about the AI space, Intrator reasoned, “I get accused of being in a circular economy … So that’s all I’ll say on the circular economy is, like, you do that by working together.”

    Critics warn that if a firm like CoreWeave cannot roll its debt or loses a key client, lenders could dump large volumes of used GPU chips into secondary markets, hitting hardware prices and rippling through the AI supply chain. But Intrator described a rapid, even violent escalation of demand.

    Managing ‘relentless’ demand

    CoreWeave, which specializes in parallelized computing solutions essential for AI, sits at the center of this storm.

    “The demand from the most knowledgeable, most sophisticated, largest tech companies in the world is relentless,” Intrator said. “That’s what the trend that matters to me.”

    This rapid expansion has come with volatility. Since its IPO, CoreWeave’s stock has seen significant fluctuation, a phenomenon that Intrator attributed to the market adjusting to a disruptive business model challenging the traditional cloud dominance of major tech players. Despite the “seesawing” stock price, Intrator noted that the company has been successful, with the stock trading around $90, compared to an IPO price of $40.

    He also addressed concerns regarding customer concentration. While he acknowledged that CoreWeave was previously reliant on Microsoft for 85% of its revenue, he said aggressive diversification efforts mean that no single customer now represents more than 30% of the company’s backlog.

    The super-cycle view

    Intrator urged investors to look past short-term execution hiccups, such as a data center opening delayed by a week, which he said caused “bedlam” among myopic observers. Instead, he views the current landscape as a “macro super-cycle,” where the fundamental shift from sequential to parallelized computing is opening up computational power at an order of magnitude previously unimagined.

    Ultimately, the collaboration that critics decry is the mechanic that is moving the industry forward, Intrator maintained. “The reasons that you have challenges in delivering that compute is because of policy… because of physical infrastructure … because of energy,” he said. “You do that by working together.”

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    Nick Lichtenberg

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  • AI is taking over managers’ busywork—and it’s forcing companies to reset expectations | Fortune

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    AI isn’t just a new tool for the modern workplace; it’s already quietly reshaping how some companies are organized. Companies including Amazon, Moderna, and McKinsey are already eliminating management layers, working to flatten organizations, and deploying AI agents to automate routine work. 

    As AI rewrites the corporate org chart, humans can avoid some managerial drudgery, according to industry leaders at Fortune’s Brainstorm AI conference. Managers currently spend a lot of time bogged down with digital tools and administrative tasks, Danielle Perszyk, a Cognitive Scientist at Amazon’s AGI SF Lab, said: “Whether you are a manager or an IC, you are tethered to your computer screen, and all of the productivity apps that we are using are actually undermining our productivity.”

    AI agents functioning as “universal teammates” and doing some of these tasks could help managers escape this cycle, Perszyk said, allowing them to focus on strategy. Aashna Kircher, Group General Manager in the Office of the CHRO at Workday, said this could free up managers’ time for other kinds of work. “The role of the manager will very much be as a coach and enabler and a team work director, which theoretically has always been the role,” she said.

    Toby Roberts, SVP of Engineering and Technology at Zillow, said that the shift toward AI agents could fundamentally change management structure. Escaping day-to-day minutiae could allow managers to oversee larger teams, he said.

    However, as AI automates more of managers’ work, companies may need to reset expectations around what management means in the AI age.

    “Historically, we’ve measured management by the output of their teams, not necessarily by the human qualities of being a manager,” Kircher said. Organizations need to build “accountability and incentive structures around rewarding the things that are going to be absolutely critical moving forward for people leaders.”

    What AI can’t do

    AI can also have negative downstream effects on interpersonal relationships if it is overused or misused. When managers over-rely on AI for collaborative work, organizations risk deteriorating people’s ability to work together effectively, said to Kate Niederhoffer, Chief Scientist and Head of BetterUp Labs.

    “Direct reports’ perceptions of managers go down the more they perceive AI and agents to be used in moments of recognition or providing constructive feedback,” Niederhoffer said. “People perceive that humans are better at these empathetic and more essentially human tasks.”

    Some managers already struggle with the emotional side of leadership, with many becoming “accidental managers”—employees who were promoted for their professional talents rather than people skills. 

    But AI’s “synthetic empathy”—even if it’s sometimes more consistent than human interactions—is not the answer, said Stefano Corazza, Head of AI Research at Canva. “The more AI there is, the more authenticity is valued,” he said. “If your manager really shows that he will spend time with you and cares, that goes a long way.”

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    Beatrice Nolan

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  • Why banks should leverage AI to serve more than the affluent—and build a financial system for everyone

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    Artificial intelligence is no longer just a buzzword thrown around in the boardroom. This technology now powers modern finance, shaping how money moves and how decisions are made. Through rapid trading, personalized wealth management, algorithmic credit scoring, and automated back-office functions, AI is helping financial institutions reduce costs and deliver greater value to their clients.

    Yet these benefits will primarily help those that already have access to a bank—and not the more than one billion that still lack access to formal financial systems. A staggering $5.2 trillion credit gap prevents small businesses in emerging markets from growing. Financial inclusion is stubbornly out of reach for these business and individuals.

    AI, combined with Web 3.0 technologies, could expand access to unbanked and underbanked populations, but only if it’s not treated as an afterthought. Financial institutions must harness AI, develop advanced methods to determine consumers’ intent to repay loans, and use alternative datasets to unlock collateral-free credit for those most in need. Collaboration, not disruption, is the way forward.

    In Kenya, Indonesia, and Brazil, startups are utilizing alternative datasets, such as mobile usage and merchant transactions, to deliver microloans and insurance to last-mile customers overlooked by traditional banks. In India, multilingual AI chatbots are already breaking down language barriers. In Latin America, fintech platforms have leveraged AI to reach millions of customers, making financial services accessible at scale.

    But financial exclusion won’t be eliminated by just another app. Instead, policymakers need to create inclusion frameworks that embed equity and access directly into the financial system.

    This requires building global infrastructure where inclusion is the norm, not the exception. For example, the UPI-PayNow bridge between India and Singapore is a real-time payments corridor allowing instant transfers with just a mobile number. But this bridge wasn’t built overnight; it’s the result of years of policy coordination, regulatory alignment, and public-private trust.

    Furthermore, in banking, collateral remains the cornerstone of traditional lending: If you want a loan, you must pledge an asset. This approach excludes low-income individuals—millions without property or savings—from accessing formal credit.

    While banks use AI mainly for efficiency today, the real potential lies elsewhere. Banks could develop strong behavioral data models using AI, serving as proxies for collateral and indicators of creditworthiness, thereby opening access for those left behind.

    Lasting change in any sector requires sustained collective action, not just individual brilliance. Disruptive breakthroughs spark innovation, but when multiple stakeholders work together toward common goals, they can overcome resistance, manage complexities, ensure everyone’s input, and keep up momentum to make progress resilient and deeply rooted.

    In finance, AI can have unintended consequences due to opaque algorithms, biases that reinforce risks, and systems that are hard to understand. For AI to promote inclusion, it must be transparent and understandable to regulators. Institutions that use such AI need to be accountable. This involves rigorous bias testing, built-in human oversight, and clear channels for appealing major decisions. Trust is essential: Without it, liquidity dries up, credit markets freeze, and economic growth slows.

    As the world enters a new technological age, AI, digital token networks, and quantum information systems are poised to transform global financial inclusion. AI will redefine financial services. Digital token networks will enable borderless, low-cost transactions through asset tokenization, eliminating the need for traditional infrastructure. And quantum information systems will enhance cybersecurity and streamline digital identification, payments, and smart contracts.

    Together, these technologies will build a trustworthy financial infrastructure, providing everyone, regardless of location, literacy, or economic status, with safe and affordable access to the global economy.

    By embedding inclusion into our financial infrastructure, we’ll have another opportunity to create a system that meets the needs of the world’s eight billion people.

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    Sopnendu Mohanty

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  • Sam Altman’s AI paradox: Warning of a bubble while raising trillions

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    Welcome to Eye on AI! AI reporter Sharon Goldman here, filling in for Jeremy Kahn. In this edition… Sam Altman’s AI paradox…AI has quietly become a fixture of advertising…Silicon Valley’s AI deals are creating zombie startups…sources say Nvidia working on new AI chip for China that outperforms the H20.

    I was not invited to Sam Altman’s cozy dinner with reporters in San Francisco last week (whomp whomp), but maybe that’s for the best. I have trouble suppressing exasperated eye rolls when I hear peak Silicon Valley–ironic statements.

    I am not sure I could have controlled myself when the OpenAI CEO said that he believes AI could be in a “bubble,” with market conditions similar to the 1990s dotcom boom. Yes, he reportedly said, “investors as a whole are overexcited about AI.” 

    Yet, over the same meal, Altman also apparently said he expects OpenAI to spend trillions of dollars on its data center buildout in the “not very distant future,” adding that “you should expect a bunch of economists wringing their hands, saying, ‘This is so crazy, it’s so reckless,’ and we’ll just be like, ‘You know what? Let us do our thing.’”

    Ummm…what could be more frothy than pitching a multi-trillion-dollar expansion in an industry you’ve just called a bubble? Cue an eye roll reaching the top of my head. Sure, Altman may have been referring to smaller AI startups with sky-high valuations and little to no revenue, but still, the irony is rich. It’s particularly notable given the weak GPT-5 rollout earlier this month, which was supposed to mark a leap forward but instead left many disappointed with its routing system and lack of breakthrough progress.

    In addition, even as Altman speaks of bubbles, OpenAI itself is raising record sums. In early August, OpenAI secured a whopping $8.3 billion in new funding at a $300 billion valuation—part of its plan to raise $40 billion this year. That figure was five times oversubscribed. On top of that, employees are now poised to sell about $6 billion in shares to investors like SoftBank, Dragoneer, and Thrive, pushing the company’s valuation potentially up to $500 billion.

    OpenAI is hardly an outlier in its infrastructure binge. Tech giants are pouring unprecedented sums into AI buildouts in 2025: Microsoft alone plans to spend $80 billion on AI data centers this fiscal year, while Meta is projecting up to $72 billion in AI and infrastructure investments. And on the fundraising front, OpenAI has company too — rivals like Anthropic are chasing multibillion-dollar rounds of their own. 

    Wall Street’s biggest bulls, like Wedbush’s Dan Ives, seem unconcerned. Ives said Monday on CNBC’s “Closing Bell” that demand for AI infrastructure has grown 30% to 40% in the last months, calling the capex surge a validation moment for the sector. While he acknowledged “some froth” in parts of the market, he said the AI revolution with autonomous systems is only starting to play out and we are in the “second inning of a nine-inning game.” 

    And while a bubble implies an eventual bursting, and all the damage that results, the underlying phenomenon causing a bubble often has real value. The advent of the web in the ’90s was revolutionary; The bubble was a reflection of the massive opportunities opening up.

    Still, I’d be curious if anyone pressed Altman on the AI paradox—warning of a bubble while simultaneously bragging about OpenAI’s massive fundraising and spending. Perhaps over a glass of bubbly and a sugary sweet dessert? I’d also love to know if he fielded tougher questions on the other big issues looming over the company: its shift to a public benefit corporation (and what that means for the nonprofit), the current state of its Microsoft partnership, and whether its mission of “AGI to benefit all of humanity” still holds now that Altman himself has said AGI “is not a super-useful term.”

    In any case, I’m game for a follow-up chat with Altman & Co (call me!). I’ll bring the bubbly, pop the questions, and do my best to keep the eye rolls at bay.

    Also: In just a few weeks, I will be headed to Park City, Utah, to participate in our annual Brainstorm Tech conference at the Montage Deer Valley! Space is limited, so if you’re interested in joining me, register here. I highly recommend: There’s a fantastic lineup of speakers, including Ashley Kramer, chief revenue officer of OpenAI; John Furner, president and CEO of Walmart U.S.; Tony Xu, founder and CEO of DoorDash; and many, many more!

    With that, here’s more AI news.

    Sharon Goldman
    sharon.goldman@fortune.com
    @sharongoldman

    FORTUNE ON AI

    Wall Street isn’t worried about an AI bubble. Sam Altman is – by Beatrice Nolan

    MIT report: 95% of generative AI pilots at companies are failing – by Sheryl Estrada

    Silicon Valley talent keeps getting recycled, so this CEO uses a ‘moneyball’ approach for uncovering hidden AI geniuses in the new era – by Sydney Lake

    Waymo experimenting with generative AI, but exec says LiDAR and radar sensors important to self-driving safety ‘under all conditions’ – by Jessica Matthews

    AI IN THE NEWS

    More shakeups for Meta AI. The New York Times reported today that Meta is expected to announce that it will split its A.I. division — which is known as Meta Superintelligence Labs — into four groups. One will focus on AI research; one on  “superintelligence”; another on products; and one on infrastructure such as data centers. According to the article’s anonymous sources, the reorganization “is likely to be the final one for some time,” with moves “aimed at better organizing Meta so it can get to its goal of superintelligence and develop AI products more quickly to compete with others.” The news comes less than two months after CEO Mark Zuckerberg overhauled Meta’s entire AI organization, including bringing on Scale AI CEO Alexandr Wang as chief AI officer. 

    Madison Avenue is starting to love AI. According to the New York Times, artificial intelligence has quietly become a fixture of advertising. What felt novel when Coca-Cola released an AI-generated holiday ad last year is now mainstream: nearly 90% of big-budget marketers are already using—or planning to use—generative AI in video ads. From hyper-realistic backdrops to synthetic voice-overs, the technology is slashing costs and production times, opening TV spots to smaller businesses for the first time. Companies like Shuttlerock and ITV are helping brands replace weeks of work with hours, while tech giants like Meta and TikTok push their own AI ad tools. The shift raises ethical questions about displacing creatives and fooling viewers, but industry leaders say the genie is out of the bottle: AI isn’t just streamlining ad production—it’s reshaping the entire commercial playbook.

    Silicon Valley’s AI deals are creating zombie startups: ‘You hollowed out the organization.’ According to CNBC, Silicon Valley’s AI startup scene is being hollowed out as Big Tech sidesteps antitrust rules with a new playbook: licensing deals and talent raids that gut promising young companies. Windsurf, once in talks to be acquired by OpenAI, collapsed into turmoil after its founders bolted to Google in a $2.4 billion licensing pact; interim CEO Jeff Wang described tearful all-hands meetings as employees realized they’d been left with “nothing.” Similar moves have seen Meta sink $14.3 billion into Scale AI, Microsoft scoop up Inflection’s founders, and Amazon strip talent from Adept and Covariant—leaving behind so-called “zombie companies” with little future. While founders and top researchers cash out, investors and rank-and-file staff are often left stranded, sparking growing concern that these quasi-acquisitions not only skirt regulators but also threaten to choke off AI innovation at its source.

    Nvidia working on new AI chip for China that outperforms the H20, sources say. According to Reuters, Nvidia is developing a new China-specific AI chip, codenamed B30A, based on its cutting-edge Blackwell architecture. The chip, which could be delivered to Chinese clients for testing as soon as next month, would be more powerful than the current H20 but still fall below U.S. export thresholds—using a single-die design with about half the raw computing power of Nvidia’s flagship B300. The move comes after President Trump signaled possible approval for scaled-down chip sales to China, though regulatory approval is uncertain amid bipartisan concerns in Washington over giving Beijing access to advanced AI hardware. Nvidia argues that retaining Chinese buyers is crucial to prevent defections to domestic rivals like Huawei, even as Chinese regulators cast suspicion on the company’s products.

    EYE ON AI RESEARCH

    Study finds AI-led interviews improved outcomes. A new study looked at what happens when job interviews are run by AI voice agents instead of human recruiters. In a large experiment with 70,000 applicants, people were randomly assigned to be interviewed by a person, by an AI, or given the choice. Surprisingly, AI-led interviews actually improved outcomes: applicants interviewed by AI were 12% more likely to get job offers, 18% more likely to start jobs, and 17% more likely to still be employed after 30 days. Most applicants didn’t mind the change—78% even chose the AI when given the option, especially those with lower test scores. The AI also pulled out more useful information from candidates, leading recruiters to rate those interviews higher. Overall, the study shows that AI interviewers can perform just as well as, or even better than, human recruiters—without hurting applicant satisfaction.

    AI CALENDAR

    Sept. 8-10: Fortune Brainstorm Tech, Park City, Utah. Apply to attend here.

    Oct. 6-10: World AI Week, Amsterdam

    Oct. 21-22: TedAI San Francisco. Apply to attend here.

    Dec. 2-7: NeurIPS, San Diego

    Dec. 8-9: Fortune Brainstorm AI San Francisco. Apply to attend here.

    BRAIN FOOD

    Do AI chatbots need to be protected from harm? 

    AI lab Anthropic has introduced a new safety measure in its latest Claude models, which empowers the AI to terminate conversations in extreme cases of harmful or abusive interaction. The feature activates only after repeated redirections fail—typically for content requests involving sexual exploitation of minors or facilitation of large-scale violence. The company is notably framing this as a safeguard not principally for users, but for the model’s own “AI welfare,” reflecting an exploratory stance on the machine’s potential moral status.

    Unsurprisingly, the idea of granting AI moral status is contentious. Jonathan Birch, a philosophy professor at the London School of Economics, told The Guardian he welcomed Anthropic’s move for sparking a public debate about AI sentience—a topic he said many in the industry would rather suppress. At the same time, he warned that the decision risks misleading users into believing the chatbot is more real than it is.

    Others argue that focusing on AI welfare distracts from urgent human concerns. For example, while Claude is designed to end only the most extreme abusive conversations, it will not intervene in cases of imminent self-harm—even though a New York Times opinion piece yesterday urged such safeguards, written by a mother who discovered her daughter’s ChatGPT conversations only after her daughter’s suicide.

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    Sharon Goldman

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  • Europe is falling behind in generative AI, with the U.S. light years ahead. But the race is just getting started

    Europe is falling behind in generative AI, with the U.S. light years ahead. But the race is just getting started

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    The potential of generative AI knows no limits. And what we have seen of it now might only be the tip of the iceberg. 

    For years, companies around the world have been working on mustering up their AI prowess—be it in the U.S., China, or France.

    Generative AI’s potential to boost productivity, ignite creativity, and overhaul workflows is now taking off within countless industries. Regardless of the business case, companies working with or making their own generative AI tools have been catapulted to the forefront of the conversation.

    Marking our inaugural Brainstorm AI conference at the Rosewood Hotel in London, where we’ll unpack some of these complex yet pressing subjects, Fortune took a deep dive into the state of play for generative AI across the world, with exclusive insights based on data from the Amsterdam-based intelligence company Dealroom.  

    Our analysis covers the world’s top 100 generative AI companies by funding. It’s little surprise that U.S.-based (and specifically, San Francisco Bay Area-based) companies dominated other regions by light years. The Sam Altman-led OpenAI is, by far, the highest-funded AI company, while its California neighbors Anthropic and Inflection AI follow closely after. Over in Europe, the likes of Mistral AI and Aleph Alpha have gained traction for their innovations. 

    Still, companies in France, the U.K., and Germany received a fraction of the funding—not because there aren’t enough of them, but because they haven’t reached the mammoth size their American peers have. Israel, which we’ve included in our analysis, also has a buzzing generative AI scene. 

    In numbers, that means Dealroom’s data on the 100 companies cuts off those that have raised below $70 million in total funding. That’s where the bulk of Europe’s fledgling companies fall. Since Dealroom data mainly considers funding figures in this case, some noteworthy players in the generative AI realm, like Google, aren’t part of the analysis below.

    But Europe has to pat itself on the back for some of the strides it’s made. For instance, three of the 15 companies on our list have female founders. Seven companies were initially founded in Europe but have since moved to the U.S., where they obtained about $1.7 billion in funding.  

    Given the technology’s various use cases, defining what qualifies as a generative AI company can be challenging. By definition, generative AI uses algorithms to create new and realistic content—including text, images, and audio—based on training data. Dealroom’s data, which is as of April 2024, looks at companies that either use or create large language models trained on massive data sets to produce new content. 

    The charts below give us a glimpse of how Europe compares to some of the world’s AI power players. They also show us where the biggest strides in generative AI are being made in Europe and who the movers and shakers are.

    Total funding for the world’s top 100 generative AI startups, by region

    It’s clear that the U.S. has received the lion’s share of funding. American companies are ahead with more than 10 times the funding–$36.8 billion in funds raised compared to European and Israeli companies, which have only raised $3.2 billion so far. OpenAI is a clear leader with $12.3 billion in funds raised, according to data compiled by Dealroom.

    Key European markets home to the biggest gen AI players by funding

    In our analysis, Israel has the lead over Europe as a hub for generative AI companies, based on how much they’ve secured in funding. Within continental Europe, Germany and France emerge at the top. 

    The majority of the funding for European companies originates from European investors, based on Dealroom data. Roughly 43% of the funding for European and Israeli companies comes from their home countries, about 13% comes from a different country within Europe, and 39% comes from the U.S.   

    Most funded companies in Europe and Israel

    Here’s a glimpse at the most funded companies in Europe and Israel–Aleph Alpha, the German answer to OpenAI founded by Jonas Andrulis, leads the category. In Nov. 2023, Bosch, SAP, and Hewlett Packard Enterprise backed a $500 million series B funding round, marking one of Europe’s biggest AI funding rounds ever.

    France’s Mistral AI, led by Arthur Mensch, comes up second. Microsoft said it would invest $16.3 million into the French company in February.

    See below for the full list of generative AI companies headquartered in Europe and Israel ranked by funding, per Dealroom data. 

    Aleph Alpha

    Launch year: 2019
    HQ city/country: Heidelberg, Germany
    Total funding (USD): $641.14 million

    Mistral AI

    Launch year: 2023
    HQ city/country:     Paris, France
    Total funding:     $553 million

    AI21

    Launch year: 2017
    HQ city/country:     Tel Aviv-Yafo, Israel
    Total funding:     $326.5 million

    Lightricks

    Launch year: 2013
    HQ city/country: Jerusalem, Israel
    Total funding: $305 million

    Cera

    Launch year: 2016
    HQ city/country: London, United Kingdom
    Total funding: $302.5 million

    Synthesia

    Launch year: 2017
    HQ city/country: London, United Kingdom
    Total funding: $155.58 million

    Stability AI

    Launch year: 2019
    HQ city/country: London, United Kingdom
    Total funding: $151 million

    Poolside AI

    Launch year: 2023
    HQ city/country: Paris, France
    Total funding: $126.01 million

    Pecan

    Launch year: 2016
    HQ city/country: Tel Aviv-Yafo, Israel
    Total funding: $112 million

    DeepL

    Launch year: 2009
    HQ city/country: Cologne, Germany
    Total funding: $110 million

    MDClone

    Launch year: 2015
    HQ city/country: Beersheba, Israel
    Total funding: $104.01 million

    Corti

    Launch year: 2016
    HQ city/country: Copenhagen, Denmark
    Total funding: $90.9 million

    Stratio

    Launch year: 2014
    HQ city/country: Pozuelo de Alarcón, Spain
    Total funding: $85.8 million

    Sana Labs

    Launch year: 2016
    HQ city/country: Stockholm, Sweden
    Total funding: $82.57 million

    Ready Player Me

    Launch year: 2014
    HQ city/country:     Tallinn, Estonia
    Total funding:     $72.55 million

    This feature was reported with assistance from Fortune’s executive editor Alex Wood Morton, list director Grethe Schepers, research analyst Elena Medina, and production editor Aslesha Mehta. 

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    Prarthana Prakash, Alex Wood Morton

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  • Box CEO Aaron Levie’s top takeaway from OpenAI meltdown: ‘Don’t have weird corporate structures’

    Box CEO Aaron Levie’s top takeaway from OpenAI meltdown: ‘Don’t have weird corporate structures’

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    When OpenAI’s board ousted CEO Sam Altman over a reported disagreement, claiming he was “not consistently candid,” it left many onlookers scratching their head. How could such a thing occur to one of the most buzzy startups in Silicon Valley? The surprise firing highlighted the bizarre corporate structure at the $86 billion startup where the nonprofit controls the for-profit subsidiary. This structure has drawn criticism from plenty of tech characters including Box CEO Aaron Levie. From tweets to the stage, Levie doubled down on his stance regarding OpenAI’s unorthodox structure at Fortune‘s Brainstorm AI conference in San Francisco on Monday.

    “If you just look at the ratio of the amount of drama to the amount of takeaways, the ratio is way off,” Levie said on stage. “The main takeaway is, don’t have weird corporate structures. It never ends well.”

    At the heart of the dispute at OpenAI was a reported clash of perspectives on the trajectory of artificial intelligence growth. On one side stood the effective altruist faction, to which former board member Helen Toner subscribed, that worries about a doomsday-like scenario where AI could destroy the world. On the opposing front there are effective accelerationism (e/acc) enthusiasts, believing in AI’s potential to positively transform our world and advocating for an expedited development. It wasn’t that black and white internally, but that seems to be the layman’s gist of the dispute.

    Levie highlighted these two growing factions within Silicon Valley, and while he leans more towards acceleration, he said his biggest takeaway from the philosophies is that we need to “land the plane as an ecosystem on this topic ASAP.” There’s “tens of thousands of products” that rely on OpenAI, giving rise to a community of companies whose own fortunes have become deeply entwined in the success of OpenAI.

    Take Khan Academy founder Salman Khan, who described earlier Monday at Brainstorm AI, how his team had to reach out to “the highest levels of contacts” they had at Microsoft to make sure that they wouldn’t have an interruption of service as a result of the boardroom drama.

    Levie highlights this dependence as a key reason why so much drama was kicked up, with so many figures rallying behind the success of OpenAI and Altman.

    “It was not your classic sort of leadership struggle or dynamic,” Levie said.

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    Kylie Robison

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