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Tech firms are increasingly competing on making AI intelligence cheaper rather than solely bigger, smarter, or faster.

The article's second section argues that AI labs are now racing to make models more efficient and cheaper, not just more powerful, as enterprises grow cost-conscious. ✦ AI generated

Alex Kantrowitz · Big Technology · 2026-07-13 · original ↗

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For years, the AI race has been driven by which AI models are bigger, smarter and faster than their rivals. Now, tech firms are increasingly competing on how they can make intelligence cheaper instead of just better.

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▦ Anthropic ▦ Meta ▦ OpenAI

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In practice · 1

Snowflake pivoted away from building foundation models because they couldn't compete with OpenAI or Anthropic on capital, and instead focused on how AI accelerates value from customer data already in Snowflake.Sridhar Ramaswamy · No Priors · conf 80%

Context · 3

SAP's business model is transitioning from seat-based licensing toward a consumptive and eventually outcome-based model, but this is a joint journey with customers — many enterprises still demand cost predictability and are not yet ready for purely consumptive pricing.Philipp Herzig · No Priors · conf 70%In the age of AI, employee cost should be a dynamic number based on their model usage choices rather than a static salary, creating a new quadrant for evaluating employee cost-effectiveness.Alex Atallah · 20VC · conf 70%Meta's data advantage and walled garden will remain an edge even as AI compute costs fall, because not everyone has access to the treasure trove of user data Meta possesses for ad targeting and product features.Hari Ramachandra · We Study Billionaires · conf 65%

Counterpoint · 1

Customers start from capability first, not cost, because that is where economic value is unlocked, then optimize later.Tuhin Srivastava · No Priors · conf 70%

This moment responds to

supports → The AI industry may be entering an intelligence overhang where raw capability improvements become table stakes and the conversation shifts toward speed, cost, infrastructure, open source, and specific kinds of intelligence.Claire · Lenny's Newsletter explains mechanism → The AI industry is shifting from a large-hub, large-spoke model to a large-hub, medium-hub, distributed-spoke model where enterprises will train and run their own proprietary models on their own hardware.David Friedberg · All-In Podcast supports → Marginal costs are back in a big way for AI, both in the short-term implications of state-of-the-art free/cheap models and in the long-term structure of the industry.Ben Thompson · Stratechery gives example → The AI industry may be entering an intelligence overhang where raw capability improvements become table stakes and the conversation shifts toward speed, cost, infrastructure, open source, and specific kinds of intelligence.Claire · Lenny's Newsletter