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The internet is about to look a whole lot more like the online advertising world.

No, I don’t mean there’ll be more ads. In fact, I think there’ll be far fewer.

But the technology stack for content distribution will mirror the architecture that has been implemented in the online ad world.

As we’ve reached the AI search tipping point, publishers face an existential challenge : ensuring AI systems use their content in answers to maintain relevancy.

OpenAI receives on average 1 query per American per day.

Google receives about 4 queries per American per day.

Since then 50% of Google search queries have AI Overviews, this means at least 60% of US searches are now AI.

It’s taken a bit longer than I expected for this to happen. In 2024, I predicted that 50% of consumer search would be AI-enabled.

But AI has arrived in search.

If Google search patterns are any indication, there’s a power law in search behavior. SparkToro’s analysis of Google search behavior shows the top third of Americans who search execute upwards of 80% of all searches - which means AI use isn’t likely evenly distributed - like the future.¹

In working with AI, I’m stopping before typing anything into the box to ask myself a question : what do I expect from the AI?

2x2 to the rescue! Which box am I in?

On one axis, how much context I provide : not very much to quite a bit. On the other, whether I should watch the AI or let it run.

What happens when technology evolves faster than your sales process can adapt?

The last fifteen years, startups focused on building software around very well understood processes. We had built an assembly line for software sales, SDR to AE to customer success manager. We calculated ratios between these three total cost of sales and drove the factory to ever improved yields.

AI is upending all of that.

The underlying workflows are changing so quickly, software buyers no longer know what the ideal processes are, much less which is the best software to buy.

That little black box in the middle is machine learning code.

I remember reading Google’s 2015 Hidden Technical Debt in ML paper & thinking how little of a machine learning application was actual machine learning.

The vast majority was infrastructure, data management, & operational complexity.

With the dawn of AI, it seemed large language models would subsume these boxes. The promise was simplicity : drop in an LLM & watch it handle everything from customer service to code generation. No more complex pipelines or brittle integrations.

If 2025 is the year of agents, then 2026 will surely belong to agent managers.

Agent managers are people who can manage teams of AI agents. How many can one person successfully manage?

I can barely manage 4 AI agents at once. They ask for clarification, request permission, issue web searches—all requiring my attention. Sometimes a task takes 30 seconds. Other times, 30 minutes. I lose track of which agent is doing what & half the work gets thrown away because they misinterpret instructions.

For the last decade, the biggest line item in any startup’s R&D budget was predictable talent. But AI is pushing its way onto the P&L.

How much should a startup spend on AI as a percentage of its research and development spend?

10%? 30%? 60?

There are three factors to consider. First, the average salary for a software engineer in Silicon Valley. Second is the total cost of AI used by that engineer. Cursor is now at $200 per month for their Ultra Plan & reviews of Devin suggest $500 per month. Third, the number of agents an engineer can manage.

When you query AI, it gathers relevant information to answer you.

But, how much information does the model need?

Conversations with practitioners revealed the their intuition : the input was ~20x larger than the output.

But my experiments with Gemini tool command line interface, which outputs detailed token statistics, revealed its much higher.

300x on average & up to 4000x.

If I have a dollar to invest in a stock or a crypto token, how do I decide? I need to compare across the two.

Historically, that comparison was impossible. Crypto traded on a potent cocktail of hype, narrative, & the promise of a decentralized future. Perception drove valuations.

That’s changing. The word “revenue” is no longer verboten in the world of crypto. It’s becoming the goal.

This trend will unlock the next wave of institutional capital because investors can compare the risk/reward of crypto with the same metrics as other software companies.

Yesterday, Figma filed its beautifully designed S-1.

It reveals a product-led growth (PLG) business with a remarkable trajectory. Figma’s collaborative design tool platform disrupted the design market long-dominated by Adobe.

Here’s how the two companies stack up on key metrics for their most recent fiscal year:

Figma is about 3% the size of Adobe but growing 4x faster. The gross margins are identical. Figma’s 132% Net Dollar Retention is top decile.