To The Moon

The internet of the past 20 years and the multi-trillion dollar tech companies we see today are built on two assumed truths.

• The addressable markets are nearly infinite in size

• Marginal costs round to zero

This is why $Meta Platforms, Inc.(META)$ Facebook was trading for 100x earnings in 2014 and 2015. Investors knew it could eventually scale profits to the $55.5 billion in net income we see today.

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It’s why $Uber(UBER)$ could raise tens of billions of dollars chasing ride-sharing.

It’s why

winner-take-all markets are valuable

. It’s why the

Smiling Curve

works.

Margins are 80% to 90% for most of these companies. The marginal cost isn’t important. What’s important is growing. Fast.

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AI may not look like this. AI may look more like a utility or a manufacturing business.

The business model won’t be selling seats of software at an 80%-90% margin.

Pricing will be based on usage.

Usage will be based on the best model for the job and the ROI of the job being done.

There’s far less leverage on development and operating expenses than there was previously.

It looks like models are quickly commoditized and so are software developers building those models.

What happens if Silicon Valley’s margins go to 30%, or lower?

OpenAI’s gross margin on its most expensive product is negative, so this isn’t a wild projection.

Artificial Intelligence’s Massive Impact and Disappointing Economics

The impact for investors may be massive improvements in efficiency and better products and features for business.

And lower profits that we need to judge based on earnings (the return of the P/E multiple) and not just growth (out with the P/S multiple).

Use $Salesforce.com(CRM)$ and $Microsoft(MSFT)$ as examples.

Is Salesforce a better business charging $2 per conversation for Agentforce than it was charging $3,960 per year per employee for the Unlimited SaaS plan?

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Salesforce Agentforce pricing.

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Salesforce standard pricing.

Maybe. But Salesforce (likely) won’t have an 80% margin on AI agents as it does on software seats.

Margins will compress and Salesforce will need to “make it up in volume”.

Hyperscalers like Microsoft are also spending tens of billions — $80 billion in 2025 — to build out infrastructure to serve AI models and run inference. But returns on capital are falling from tech-like levels to energy-like levels as the scale of the investment goes up.

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This looks more like an oil & gas company or a utility, the opposite of what tech used to be with unlimited leverage and sky-high margins.

Something to Think About

The new paradigm rarely looks like the old paradigm.

We don’t know exactly how AI businesses will develop, who will be disrupted, or what the economics look like. And I want to acknowledge that costs of inference are coming down rapidly and will continue to improve at an exponential pace.

My caution is not to assume the high profit margins and operating leverage Silicon Valley has benefitted from for 30 years will exist in the new paradigm.

AI may be more of a commodity product with lower gross margins and fewer massive winners. If that’s the case, we need to rethink how we value AI-focused companies across the value chain.