Gavin Baker

Founder and Managing Partner of Atreides Management, a crossover investment firm.

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Key positions and views

The central and most important competition in AI is the hardware arms race between NVIDIA's GPUs and Google's TPUs, which are currently the only viable alternative for large-scale training.

A crossover investing strategy that spans both public and private markets is essential in the AI era due to the unique mix of public and private competitors at every layer of the tech stack.

The AI buildout is not a 2000-style bubble because it is constrained by physical limits on energy and semiconductor supply, and the massive capital expenditures are already generating higher returns on invested capital for the spenders.

Traditional SaaS companies face an existential threat and must fundamentally change their business models to accept the lower gross margins inherent in compute-intensive AI products to survive.

AI scaling laws for pre-training remain intact, as proven by models like Gemini 3, and recent advances in reasoning have bridged an 18-month innovation gap, ensuring continued progress.

Podcast consensus on Baker

Points of consensus

▶The primary competition in the AI hardware market is between NVIDIA's GPUs and Google's TPUs, with other chipmakers like AMD relegated to a secondary, second-source role.Apr 2026

▶The massive capital expenditure on AI compute by large tech companies is currently justified, as evidenced by a roughly 10-percentage-point increase in their Return on Invested Capital (ROIC) since ramping up spending.

▶Physical, real-world constraints, specifically shortages of electricity ('watts') and semiconductor manufacturing capacity ('wafers'), are significant bottlenecks for the AI buildout that may prevent a 2000-style oversupply bubble.Apr 2026

▶AI is forcing a fundamental shift in the business models of software companies, requiring them to abandon high-margin SaaS models and accept lower gross margins to remain competitive in an AI-native world.

Points of debate

▶Baker argues AI may be a 'sustaining innovation' that benefits large incumbents with data, capital, and distribution, which contrasts with the common narrative that AI will be a disruptive force that unseats them.

▶While acknowledging a 'rolling bubble' in niche sectors, Baker actively refutes the broader 'AI bubble' thesis by comparing current valuations to the dot-com peak and highlighting rising ROIC and physical supply constraints.

▶Baker's assertion that the operational value-add from many venture capital firms is 'wildly overstated' and unappreciated by founders directly challenges the VC industry's prevailing narrative and fee structures.Apr 2026

▶The leadership in frontier AI model development is highly contested. Baker notes Google's temporary cost and hardware advantage with TPUs, but also predicts xAI will be first with a Blackwell-trained model and that NVIDIA's future chips will re-establish a wider performance gap.Apr 2026

Key themes

▶The AI Hardware Arms Race

Baker's analysis is intensely focused on the foundational layer of AI competition: the hardware. He frames the market as a primary battle between NVIDIA's GPUs and Google's TPUs, with both companies accelerating their roadmaps to an annual release cycle to outpace the development of custom ASICs by competitors.

This focus suggests that the primary value capture and most durable competitive moats in the current AI wave are being built at the silicon level, making analysis of chip architecture, manufacturing capacity, and data center infrastructure paramount for investors.

▶The Crossover Investing ImperativeApr 2026

Baker argues that the AI industry is unique because public and private companies are direct competitors at every level of the technology stack, from frontier models (OpenAI vs. Google) to infrastructure. He believes this dynamic makes a crossover investing strategy, which spans both public and private markets, not just relevant but 'paramount' to truly understand the landscape.

This implies that traditional public-only or private-only investment funds may have a critical blind spot, unable to fully grasp the competitive dynamics and technological shifts occurring across the public/private divide in AI.

▶AI's Economic Transformation and Creative Destruction

Baker posits that AI forces new, and often painful, economic realities on existing businesses. He repeatedly warns that traditional SaaS companies must accept lower gross margins to survive, while noting that the large companies investing heavily in AI are already seeing significant increases in their return on invested capital.

Baker's analysis points to a period of significant turmoil for incumbent software companies, where failure to adapt to AI-native, agent-based models could be an existential threat, analogous to brick-and-mortar retailers who failed to embrace e-commerce.

▶Physical World Constraints on a Digital RevolutionApr 2026

A core tenet of Baker's thesis is that the AI buildout is fundamentally limited by real-world resources like electricity, data center rack space and weight limits, and semiconductor manufacturing capacity. He argues these bottlenecks will moderate the investment cycle and may prevent the kind of speculative oversupply seen in the dot-com era's fiber optic boom.

This perspective provides a contrarian check on simplistic 'bubble' narratives, suggesting that the AI investment cycle's trajectory will be dictated as much by engineering and energy constraints as by market sentiment.

Source episodes

Sentiment over time

Not enough data for timeline

Changes over time

1996

Performs an early analysis of Cisco, which he notes increased twenty-fold in the subsequent four years, marking an early point in his tech analysis career.

Pre-2019 (Fidelity Tenure)

As a portfolio manager at Fidelity, demonstrates his crossover investing approach by leading three consecutive funding rounds in the private company Roku, despite market concerns about competition from tech giants.

2019

Founds Atreides Management, a firm managing $7 billion across public, private, and crossover strategies, institutionalizing his investment philosophy.

2022-Present

Public commentary becomes heavily focused on the AI technology stack, comparing the emergence of ChatGPT to Netscape Navigator and framing it as the beginning of a multi-decade investment cycle.

2024

Articulates a highly detailed thesis on the AI hardware market, identifying Google's TPU as NVIDIA's main competitor, detailing the physical constraints of the Blackwell transition, and making specific predictions about the future of AI models and infrastructure.

Suggested prompts

How does Gavin Baker's 'physical constraints' thesis (watts and wafers) challenge traditional models of tech bubble formation and collapse? &nearr;If Baker is correct that Google is NVIDIA's primary competitor, what are the strategic implications for the rest of the semiconductor industry (e.g., AMD, Broadcom)? &nearr;Analyze the potential contradictions in Baker's view of AI as a 'sustaining innovation' for incumbents while also predicting massive disruption for traditional SaaS companies. &nearr;Evaluate the viability of Baker's prediction that space-based data centers will be a critical AI infrastructure development, considering the technological and economic hurdles involved. &nearr;

Key concepts

AI Hardware Competition 3 ep NVIDIA 4 ep Google TPU 2 ep Crossover Investing 1 ep AI Scaling Laws 2 ep SaaS & AI Economics 3 ep Market Bubbles 2 ep Data Center Infrastructure 1 ep Humanoid Robotics 2 ep Frontier AI Models 3 ep

Notable quotes

“NVIDIA's biggest competitor, it's not AMD, it's not Broadcom, it's certainly not Marvell, it's not Intel, it's Google.”

Gavin Baker · "Is there an AI bubble?” Gavin Baker and David George

“If ChatGPT is to AI as Netscape Navigator was, we're in year three of what should be a 20, 25, 30 year really exciting cycle or trend.”

Gavin Baker · Gavin Baker – Truth-Seeking and Crossover Investing at Atreides (EP.489)

“It is definitionally impossible, given what we just discussed, to succeed in AI without gross margin pressure.”

Gavin Baker · "Is there an AI bubble?” Gavin Baker and David George

“The problem is when algorithms and quantitative investors came in, they took all that alpha because they don't feel embarrassment. They don't feel shame. They don't feel emotion. And that's why a lot of human-driven fundamental value strategies really struggled.”

Gavin Baker · Gavin Baker – Truth-Seeking and Crossover Investing at Atreides (EP.489)

Report last updated: Apr 21, 2026

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