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For the better part of four years, AI has been the only trade that mattered. Nvidia. Data centers. The infrastructure buildout. A rising tide of capex spending that has minted fortunes and reshaped the S&P 500. If you were long the AI stack, you were a genius.<\/p>\n
Ben Silver and David Tykocinski think the easy part may be ending.<\/p>\n
\u201cThere\u2019s a risk,\u201d according to Tykocinski, co-chief investment officer of Maverick Capital\u2019s public funds, of an \u201cair pocket\u201d in the gap between the infrastructure buildout and the actual productivity handoff. Even for a full-throated AI believer, he says, that gap is precisely where market volatility breeds.<\/p>\n
Silver and Tykocinski are the co-CIOs of Maverick Capital, the Dallas- and New York-based hedge fund founded in 1993 by Lee Ainslie, one of the original \u201cTiger Cubs\u201d\u2014the generation of investors trained under legendary hedge fund manager Julian Robertson at Tiger Management. Ainslie built Maverick into one of the most respected long\/short equity firms on the street over three decades and Silver and Tykocinski are the men he chose to carry it forward.<\/p>\n
They are, in other words, the next generation of Tiger Cubs\u2014and, as they said in an appearance on Goldman Sachs\u2019 Exchanges: Great Investors podcast, they are looking at a market most investors still see as a one-directional AI trade and seeing something considerably more complicated.<\/p>\n
Following the bottleneck<\/p>\n
To understand their thesis, you have to understand how they think about where value migrates in a major technology cycle.<\/p>\n
Bloomberg reported in August 2025 it was raising money for a new semiconductor fund in a \u201crare expansion\u201d after \u201ctrouncing\u201d rivals with a strong run since 2021, exactly when Silver and Tykocinski assumed co-CIO duties. Its oldest hedge fund returned more than 70% cumulatively from the start of 2021 through the first half of 2025, which Bloomberg noted beat the performance of founder Ainslie\u2019s Tiger Cub peers.<\/p>\n
The hallmark of the AI trade so far, Tykocinski argues, has been an inversion of the prior two decades of tech investing. In the 2000s and 2010s, value accrued at the software application layer\u2014the Salesforces and Googles and Metas that sat closest to the end user. AI flipped that script. Suddenly it was the hardware and infrastructure layer\u2014Nvidia\u2019s GPUs, the hyperscalers\u2019 data centers, the energy ecosystem powering them\u2014that captured the lion\u2019s share of investor returns.<\/p>\n
The key to monetizing the trade, he said, has been following the bottleneck upstream. <\/p>\n
\u201cIn the early days,\u201d he explained, \u201cwhen demand is still within existing industry production capacity, downstream physical outputs of things like GPUs are where you see the most explosive growth. Once you cross that threshold\u2014which we have\u2014the bottlenecks move upstream to the fabrication level, to the tools that go into making them, even to the obscure materials listed on a Japanese stock exchange.\u201d<\/p>\n
But Tykocinski said he now believes that migration is about to reverse. <\/p>\n
\u201cWe actually think that migration, though, is going to begin to swing back the other direction,\u201d he said\u2014back downstream, toward the infrastructure and application layer, where AI stops being a thing companies build and starts being a thing that transforms how they work.<\/p>\n
The reason is a fundamental shift in how AI is actually being deployed inside enterprises. A year or two ago, the prevailing thesis was that large language models would effectively replace existing enterprise systems\u2014that the LLM would become the central nervous system of a business. What\u2019s happening in practice is almost the opposite. <\/p>\n
\u201cIn the world of AI agents, it\u2019s more about integration of that LLM within many ways the preexisting enterprise, workflow, and stacks,\u201d Tykocinski said. The AI isn\u2019t replacing the enterprise, in other words: It\u2019s plugging into it.<\/p>\n
That changes everything about where the chokepoints are. Suddenly CPUs matter again. Databases matter. The edge matters. <\/p>\n
\u201cThat change in interface suddenly brings a lot more value closer to the edge and closer to the end user,\u201d he said.<\/p>\n
The \u2018large sucking sound\u2019\u2014and the opportunity it created<\/p>\n
While Tykocinski has been tracking the rotation in AI, his co-CIO has been watching money pour out of an entirely different sector and quietly building a thesis around the opportunity that exodus creates.<\/p>\n
Silver\u2019s background is in health care: He ran Maverick\u2019s health care book before being elevated to co-CIO. And right now, he said, the sector sounds like a vacuum. \u201cMostly it\u2019s just a large sucking sound,\u201d he deadpanned, \u201cwith all the capital coming out of health care and going into AI.\u201d<\/p>\n
But Silver said he\u2019s not despairing. He\u2019s hunting.<\/p>\n
The specific opportunity he has his eye on is\u00a0life science tools\u2014the companies that supply the equipment and consumables used to discover and manufacture drugs. It\u2019s an unglamorous corner of the market, a sector that has been consolidating for 20 years and whose stocks, by Silver\u2019s own description, are currently \u201cleft for dead.\u201d But he sees two powerful tailwinds converging the market hasn\u2019t yet priced in.<\/p>\n
The first is reshoring. The push to move pharmaceutical manufacturing back to the U.S.\u2014 driven by trade policy and national security concerns\u2014will require a massive buildout of domestic manufacturing capacity. That means capex. And that capex flows directly to the companies that make the equipment that fills those facilities. Silver said he expects that spending to start showing up in company earnings within three to six months.<\/p>\n
The second is AI itself. Drug discovery is being transformed by machine learning\u2014and more drugs discovered means more drugs manufactured, means more consumables sold, means a sustained revenue cycle for the tools companies supplying it all. <\/p>\n
\u201cThat\u2019s a space that\u2019s very much poised to become an AI winner and kind of modern mercantilist winner as well,\u201d Silver said.<\/p>\n
There\u2019s also a third catalyst, the kind that concentrates minds at hedge funds: M&A. The sector\u2019s consolidators\u2014Silver counts three to five major players\u2014are well-capitalized and have spent decades acquiring smaller firms. With a cohort of $5 billion-$10 billion companies now trading at depressed valuations, he sees meaningful takeout risk as a potential floor. <\/p>\n
\u201cIf those fundamentals don\u2019t turn fast enough,\u201d he said, \u201cthere are real money buyers in that space.\u201d<\/p>\n
The risks they\u2019re losing sleep over<\/p>\n
For all their conviction in specific trades, Silver and Tykocinski were clear-eyed about what could go wrong\u2014and they\u2019re not talking about the usual macro boilerplate.<\/p>\n
Tykocinski\u2019s deepest concern about the AI trade itself is China. The infrastructure plays currently driving a huge portion of equity appreciation\u2014lasers, optics, analog semiconductors, specialty materials\u2014are businesses that are historically vulnerable to the same commodification dynamic that hollowed out earlier generations of hardware companies. <\/p>\n
\u201cThere\u2019s a reason why a lot of the value historically accrued at the application layer,\u201d he said, \u201cbecause that\u2019s where a lot of IP existed. Whereas root hardware and materials is more subject to commodification over time.\u201d <\/p>\n
The worry is that investors are underpricing that structural risk in companies playing in spaces where Chinese competition has historically been relentless.<\/p>\n
Silver\u2019s concerns are more systemic. He pointed to the fundamental difficulty the American political system has making rational long-term decisions in the face of short-term political incentives\u2014a structural problem that cuts across any number of policy domains relevant to markets. And like almost every serious investor, he\u2019s watching the U.S.-China geopolitical dynamic closely, particularly its implications for the technology supply chain.<\/p>\n
The new model<\/p>\n
Silver and Tykocinski were elevated to the co-CIO roles together in 2021 by Ainslie, who had spent years observing not just their individual performance as sector heads, but their chemistry together. That decision\u2014to split the CIO role rather than anoint a single successor\u2014was itself a statement about how Maverick intends to operate going forward.<\/p>\n
Their differences are by design. Silver came up through health care and cyclicals, developing a style oriented around idiosyncratic, highly specialized ideas in industries where managerial decisions and macro cycles drive outcomes more than secular trends. Tykocinski came up through TMT, where secular tailwinds and thematic dominance determine who wins. In practice, each serves as a check on the other\u2019s instincts.<\/p>\n
That matters because Maverick is one of the industry\u2019s best-known Tiger Cubs, and the old Tiger model was often associated with singular investing personalities. By contrast, Tykocinski argued \u201cno singular investment philosophy is kind of perfect in a vacuum,\u201d while Silver said the two agreed from the outset to \u201cdisagree and commit.\u201d\u00a0In a market increasingly defined by overlapping technology, industrial policy, geopolitics, and sector specialization, the co-CIO structure looks like a recognition that complexity itself has become a competitive fact of life.\u00a0<\/p>\n
Despite the caveats\u2014the air pocket risk, the China commodification threat, the rotation away from semiconductors and toward enterprise software\u2014both Silver and Tykocinski end up back at the same place when you ask them what they\u2019re most excited about: AI, just a different phase of it.<\/p>\n
\u201cThe impact of AI on the world over the next 10-20 years is going to be way more profound than we can possibly fathom,\u201d Silver said. Tykocinski was more measured but no less bullish: Beyond the investment thesis, he said, the conversation about AI is too often trapped in either dollar signs or dystopia. The open-ended upside\u2014human, not just financial\u2014is what actually excites him.<\/p>\n
For the next generation of Tiger Cubs, the AI trade isn\u2019t ending. It\u2019s just getting harder. And that, if you ask them, is precisely when the edge of deep fundamental research starts to matter most.<\/p>\n
For this story,\u00a0Fortune\u00a0journalists used generative AI as a research tool. An editor verified the accuracy of the information before publishing.<\/p>\n
#nextgeneration #Tiger #Cubs #bubble #riskand #trade<\/p>\n","protected":false},"excerpt":{"rendered":"
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