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PERSON DIRECTORY

David George

David George appears in 11 indexed conversations across The a16z Show, 20VC, Invest Like the Best. This directory brings every appearance, source, TL;DR, digest, and transcript into one searchable feed.

11 EPISODES3 SHOWS
8 episodes1 active
Language
The a16z ShowEN · 52 min

AI, Infrastructure, and the Next Investment Cycle

David GeorgeSarah WangAlex ImmermanSantiago Rodriguez

AI’s rally is being driven by earnings rather than multiple expansion, with the S&P 500 below 20 times earnings while growing about 15%.Capex is projected to rise from $416 billion in 2025 to about $780 billion in 2026 and exceed $1 trillion annually from 2027; enterprise adoption remains concentrated in pilots, with inference-cost declines and workflow deployment as catalysts.

The a16z ShowEN · 48 min

Why Investors Are Rethinking Everything for the AI Era

Jen KhaDavid GeorgeAram Verdiyan

AI is making capital itself compound advantage, pushing venture’s power law beyond prior tech cycles as compute improves products and inference demand remains unlimited.Only 20 of 3,000 US VC firms produced consistent 3x net returns over two decades, supporting concentrated LP portfolios and 5–10%+ late-stage sizing in category-defining companies.Meanwhile, traction is harder to parse and pre-ChatGPT software LBOs face repricing, leaving diffusion, liquidity, and power access as key risks and catalysts.

The a16z ShowEN · 74 min

Why AI Demand Is Outrunning Compute Supply

David GeorgeGavin Baker

AI demand accelerated through July and August, with usage concentrated among possibly sub-10 million heavy users and no leader identifying a worsening quantitative metric.Nebius suggests nine-to-ten-month paybacks on $50B-per-gigawatt builds, supported by 50-60% customer prepayments; undersupply through ’28 could lift token prices, while shifting capacity to training could cut lab revenue from $480B to $120B.

The a16z ShowEN · 33 min

The New Rule for Picking AI Winners | The a16z Show

David GeorgeDavid Clark

OpenAI and Anthropic now add more monthly revenue than Meta, Google, or Microsoft despite AI diffusion below 5%, making “in the token path” George’s rule for application winners.Five frontier labs could lower token prices, but Chinese models reportedly offer 80% of frontier capability at 10% of the cost; scarce capacity lasts through early 2029, with algorithmic breakthroughs the key reversal risk.

The a16z ShowEN · 48 min

AI Markets: Deep Dive with a16z's David George

Jen KhaDavid George

AI-native companies grow more than 2.5x faster, with top performers reaching 693% year-over-year growth and $500,000-$1 million of ARR per employee.Engagement and operating evidence includes Navan handling 50% of travel interactions with AI and expanding gross margins 20 percentage points, while enterprise change management remains the key execution risk.

The a16z ShowEN · 64 min

The Biggest Bottlenecks For AI: Energy & Cooling

Jen KhaDavid George

AI infrastructure is becoming a utility layer: big-tech capex annualizes near $400 billion, model-access costs fell more than 99% in two years, and ChatGPT reached 365 billion searches in two years.Energy is likely the next five-year bottleneck, followed by cooling, while application durability depends on 90% or more retention, easy acquisition, and workflow depth rather than model access alone.

20VCEN · 67 min

a16z's David George on the Most Controversial Bet at a16z & Do Margins and Revenue Matter in AI?

Harry StebbingsDavid George

a16z’s strongest-performing fund was a $1B vehicle, with Databricks returning 7x and Coinbase 5x in DPI alone as private markets surpassed $5T.AI is shifting budgets from labor to technology, with CH Robinson reporting 40% productivity gains and 680bps of operating-margin expansion, while retention and engagement—not SaaS margins—become the crucial proof of AI demand.

Invest Like the BestEN · 67 min

How a16z Growth Invests

Patrick O'ShaughnessyDavid George

George argues consumer AI’s monetization ceiling remains unproven: ChatGPT likely reached Google’s scale roughly 4x faster, with a billion users but fewer than 50 million monetized.His broader signal is that markets still underprice growth above 30%, while AI’s winners may be concentrated at the leader or fragmented across the model layer, making business models and robotics timelines key risks to monitor.