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NewsMaterial4 min readDisconfirmAI Editorial

Threadneedle Founder Ann Berry: AI Valuations Tie Tangible Numbers to Intangible Returns

In an interview with Bloomberg's Masters in Business, Threadneedle founder and managing partner Ann Berry outlines her framework for investing in artificial intelligence. She argues that the core concern in current AI valuations is tethering tangible market capitalization figures to returns that are not yet measurable; opportunities, she notes, lie in world models (including AMI, backed by Yann LeCun), idle data assets of mature enterprises, and companies with disciplined execution. Berry also observes a 'flight to enterprise' trend, where major AI firms use financing proceeds to acquire potential competitors, while the balance sheets of companies like NVIDIA and Salesforce have become significant sources of venture capital.

2026-11-11 00:30 GMT+8

Ann Berry, founder and managing partner of Threadneedle, discussed investment opportunities and risks in the AI industry with host Barry Ritholtz on Bloomberg's Masters in Business program. Berry brings extensive experience from Goldman Sachs Mergers & Acquisitions, Cornell Capital partnership, PureStar CEO, and Wheelhouse early-stage investments, infusing her investment framework with a distinct private equity discipline.

On the issue of AI valuation, Berry identified what she considers the market's central contradiction: tangible market cap numbers are being tied to intangible returns. While she believes the underlying technology will be transformative, she points out that when examining the capital expenditures of large AI companies—whether through earnings calls, presentations, or analyst research—there is currently insufficient rigorous measurement regarding the actual returns expected from these CapEx dollars. She contrasts this with the exuberant valuations seen in NFTs and Web3 during 2021 and 2022, noting that the early-stage investment sector currently displays more discipline, which provides her some comfort regarding early AI investments.

Berry views world models as one of the promising directions in AI investment. She explains that while current large language models are primarily trained on text, world models enable AI to understand the three-dimensional or multi-dimensional physical world, which is crucial for robotics and industrial technology. She has invested in AMI, a startup founded by Yann LeCun focused on world models, stating she began actively seeking investment opportunities in this area years ago. She cited Fei-Fei Li’s world model company, recently acquired by AMD, as an example of a broader trend where major AI companies leverage their financing capabilities to build war chests and rapidly acquire potential competitors.

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Regarding corporate data assets, Berry believes many opportunities are overlooked. She notes that many companies sit on vast amounts of data without converting it into commercial value—for instance, PayPal possesses historical transaction data and consumer immersion scale but has not moved quickly to utilize them. She uses Walmart as another example: in January 2022, she published an article on LinkedIn titled 'A Tech Stock by Any Other Name Is Walmart,' at a time when Walmart was gathering data from its e-commerce platform and developing its advertising business. That business has since grown to nearly $6.5 billion in revenue, and Walmart relocated its listing to Nasdaq to reinforce its identity as a tech company.

Berry observes two important structural shifts in the AI market. The first is a 'flight to enterprise': companies like OpenAI and Anthropic are using their fundraising capacity to build war chests and rapidly acquire any new startups that could pose competition, shortening the window for independent entrepreneurs. The second shift is that corporate balance sheets are becoming one of the largest sources of venture capital in the United States. Citing NVIDIA’s significant stake in SpaceX, she notes that companies like NVIDIA and Salesforce have collectively invested billions of dollars in funding young companies within their ecosystems. This serves as both investment and a smart M&A strategy, allowing potential acquisition targets to grow within their sphere of influence.

As an investor with a private equity background, Berry applies different financial standards to early-stage AI companies than typical venture capitalists. She requires very early-stage companies to achieve free cash flow within approximately two years, a target she acknowledges is quite aggressive for that stage. She highlights a recurring challenge in her deal flow: many young companies raise funds from friends and family based on novelty, but she struggles to assess their scalability—specifically, whether there is a viable path to commercialization. Her core criteria include a culture of execution, a leadership team she is highly confident can deliver results, and a clear next chapter for growth.

It should be noted that the above represents Berry’s attributed views from the interview and do not constitute verified investment returns or market predictions. Her judgments on the commercialization timeline for world models, assessments of the monetization potential of corporate data assets, and interpretations of the corporatization of venture capital all await validation by subsequent market performance. She has not disclosed specific valuation terms or exit returns for the companies she has invested in.

Sources

  • ritholtz.comThe Big Picture↗
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