Invest Like The Best
Conversations with investors and business leaders about their ideas, methods, and stories related to investing time and money.
American Power Depends on Preserving the Open Order China Needs
Walter Russell Mead argues that China’s integration into the U.S.-led commercial order is also its central strategic weakness: in a conflict over Taiwan, Beijing’s dependence on imported energy, raw materials and export markets could be turned against it. But Mead warns that American power rests on the open trading system, alliances and institutions that have allowed others to prosper under U.S. leadership—and that Washington, particularly under Donald Trump’s transactional approach, risks eroding its own leverage.
Compute Independence Will Determine Whether AI Becomes Economic Capacity
Sarah Guo, founder of AI-focused venture firm Conviction, argues that the contest over artificial intelligence will be decided not only by frontier models but by the industrial capacity to deploy them: compute, energy, supply chains, data centers and robotics. She makes the case for a competitive Western AI ecosystem built around compute independence and open models, while warning that physical bottlenecks and political resistance could leave the US dependent on a narrow set of providers and foreign supply chains. For investors, Guo’s framework is to form a technical and commercial view early, then test it against evidence before consensus arrives.
AI Infrastructure May Outrun the Capital Needed to Finance It
Ben Thompson argues that the AI boom’s central risk is not that the technology fails, but that its infrastructure buildout exhausts available financing before AI revenue can support it. On Invest Like the Best, he compares the mismatch between short-term capital and long-lived assets to the railroad era: a financial bust could hurt the companies and investors funding the buildout without stopping AI’s broader economic impact. He says the likely survivors are companies such as Amazon and Google that can finance capacity cheaply and put it to work inside existing businesses while external demand develops.
AI Has Shifted the Competitive Frontier for Software Incumbents
Benchmark general partner Eric Vishria argues that AI is moving the competitive frontier for software faster than incumbents can respond by adding features to existing roadmaps. In his view, companies can destroy value while executing a pre-AI plan because model capabilities are weakening old switching costs, changing what customers value, and demanding products built around AI’s uneven, rapidly shifting capabilities. Vishria also contends that the market can support major winners across models, infrastructure and applications, though power, deployment expertise and operational execution will determine who captures the value.
AI Selloff Tests Whether GPU Scarcity Can Fund the Buildout
Gavin Baker, an investor focused on AI infrastructure, argues that July’s selloff in AI and semiconductor stocks reflected fears about financing and model-layer disruption rather than deterioration in compute demand. He says rising GPU rental rates, accelerating hyperscaler operating cash flow and growing private-lab and open-source workloads support a different reading: capacity contracted at older prices can reset higher and fund more of the buildout internally. The thesis fails, he says, if GPU prices remain depressed, demand weakens, debt becomes essential or regulation blocks new data-center power.
AI’s Broad Access Will Depend on Concentrated Compute Infrastructure
Sam Altman argues that OpenAI’s task is to make advanced AI as broadly available as electricity while building the concentrated compute, energy and data-center infrastructure required to produce it. He says demand for cheap, capable intelligence could be effectively uncapped, making large-scale inference revenue the basis for ever-larger training runs. But Altman also warns that cyber risks and the concentration of frontier capabilities could undermine the human agency that, in his account, widespread AI is meant to expand.
Permanent Capital Becomes an Edge Only With Liquidity and Transparency
Vlad Barbalat, Liberty Mutual Insurance’s chief investment officer, argues that the company’s $120 billion balance sheet is unusual not simply because it has no outside investors, but because its permanent capital must still be managed with liquidity, discipline and transparency. In a conversation with Patrick O’Shaughnessy, Barbalat says Liberty Mutual’s mutual-insurance structure lets it invest differently from funds, pensions or public insurers, but only if the organization avoids complacency, builds expertise across credit and equity markets, and remains prepared rather than predictive.
Coding Revenue and Compute Shortages Are Extending the AI Boom
Alex Sacerdote, founder and portfolio manager of Whale Rock Capital Management, argues that AI is still at the earliest stage of enterprise adoption and may be a steeper curve than prior technology shifts. In his telling, coding has become the first clear proof that AI can generate large revenue by replacing or augmenting labor, while the model layer is consolidating around a few leaders rather than commoditizing. Sacerdote’s broader case is that investors are underestimating both the earnings power of those winners and the hardware renaissance required to supply the compute behind them.
Uber’s Trillion-Dollar AV Bet Depends on Aggregating Autonomous Supply
Uber chief executive Dara Khosrowshahi argues that the company’s next phase depends on becoming the supply aggregator for “physical AI”: autonomous vehicles, drones, delivery networks, and other systems that turn digital demand into real-world services. In an Invest Like the Best interview, he says Uber’s advantage is not simply consumer demand but access to drivers, merchants, couriers, fleets, and eventually autonomous supply — a position he believes could open another trillion-dollar marketplace if lower costs and higher reliability expand usage.
AI Has Made Technology Fluency Mandatory for Fundamental Investors
Dan Loeb, founder of Third Point, argues that investing has become inseparable from technology, with AI, semiconductors and energy now overriding much of the usual macro framework. In a conversation with Patrick O’Shaughnessy, Loeb traces Third Point’s shift from event-driven credit and deep-value situations toward quality businesses, thematic technology investing, activism and cross-capital-structure credit, while maintaining that markets still misprice companies because humans, governance failures and structural trading constraints have not gone away.
The U.S. Military’s Constraint Is Industrial Depth, Not Battlefield Skill
Former Pentagon official Darren Farber argues to Patrick O’Shaughnessy that the United States’ military advantage depends less on battlefield skill than on whether its politics, industrial base, and technology pipeline can sustain force before a crisis becomes existential. Farber portrays China and Iran as powerful but brittle authoritarian systems, while warning that democracies face a harder test: defining victory, maintaining public consent, and converting commercial innovation into usable military depth. His case links Ukraine’s drone war, Taiwan, the Strait of Hormuz, defense startups, and military AI to a single constraint — whether America can turn legitimacy and markets into durable strategic capacity.
TSMC’s Wafer Scarcity May Be Preventing an AI Overbuild
Investor Gavin Baker argues on Invest Like The Best that the AI boom is being organized less by software adoption than by scarcity: compute demand is outrunning power, wafers, and frontier-model access. In his account, Anthropic’s growth, Nvidia’s position, TSMC’s capacity discipline, and even SpaceX’s possible orbital compute are all expressions of the same constraint. Baker’s central claim is that the AI cycle may avoid a classic infrastructure bubble only if physical bottlenecks, especially leading-edge wafer supply, keep capital from building far ahead of demand.
Compute Allocation Is Anthropic’s Core Constraint as Claude Revenue Surges
Anthropic CFO Krishna Rao argues that the company’s rise is best understood through compute: a scarce capital asset that must be bought years ahead and constantly reallocated across model training, customer demand, internal automation and future products. In an interview with Patrick O’Shaughnessy, Rao says ordinary forecasting and software-margin frameworks break down when model capability, adoption and revenue compound together, leaving Anthropic to manage growth through scenarios rather than point estimates.
Airbnb Is Rebuilding Around Identity, Not Homes, for AI
Airbnb’s challenge in the AI era is less a feature rollout than a company reinvention, chief executive Brian Chesky argues in a conversation with Patrick O’Shaughnessy. Chesky says the company has to move beyond a business still identified mainly with homes, rebuild around identity and personal preferences, and do so without damaging a large public platform that hosts and investors depend on. His answer is a more hands-on operating model: fewer abstraction layers, smaller elite teams closer to users, continuous recruiting, and a CEO directly engaged with the work.