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Shopify CEO Tobi Lutke explains Goodhart’s law and why he doesn’t like KPIs or OKRs

“Goodhart’s law is real. The moment a metric becomes a goal, it’s no longer a useful metric… No metric by itself is a complete heuristic for a complex business. There’s a million different tensions in a company, and you can’t keep all of them in harmony by optimizing for one thing.”

For this reason, Shopify doesn’t use KPIs or OKRs. But as Tobi explains, this doesn’t mean they don’t value data and metrics.

“We are extremely data informed. We have invested enormous amounts of money and time into systems that give us basically everything at our fingertips… But what Shopify attempts to do is just not over-fit for what’s quantifiable.”

People love optimizing for highly-quantifiable things because there’s immediate gratification that comes from seeing a number go up. But Tobi thinks that the most important aspects of a product are rarely quantifiable:

“The overlap of the most valuable things you can do with a product and the things that happen to be fully quantifiable are like maybe 20%. Which leaves 80% of a value space unaddressable by the people who only look at quantifiable things.”

He continues:

“Shopify is comfortable with unquantifiable things like taste, quality, passion, love, hate… The sort of deep satisfaction that a craftsperson feels when they’ve done a job well is actually a better proxy if you allow it to be.”

They then have robust analytics systems that tell the company if something’s wrong or a new rollout breaks something.

“We think about it as a cockpit for a pilot. The decisions are still made by pilots, and we think this leads to better results… I think there needs to be more acceptance in business of unquantifiable things… And then metrics take a support function.”

Video source: @lennysan (2025)

WOW 🤯
Ex-OpenAI researcher Leopold Aschenbrenner raises $1.5B for hedge fund. 💰

AI themed hedge funds are pulling in big capital and strong returns. Leopold Aschenbrenner’s Situational Awareness reached $1.5B and delivered 47% in H1.

THE WAVE IS HERE 🚀

His playbook is tight, long the AI value chain, chips, data center gear, and power producers, plus small startup stakes, then offset with smaller shorts in likely losers. He pitches a brain trust on AI, and many investors agreed to multi year lockups.

The risk is concentration. Only a small set of public names touch AI infrastructure, so trades crowd fast, Vistra shows up across portfolios.

January’s DeepSeek shock hit leaders showed that.

Cheap model release from DeepSeek in January briefly knocked big AI stocks down, then those prices rebounded soon after.

That pattern signals crowding risk because many funds were holding the same small set of AI winners, so one shock made a lot of them sell at the same time, which magnified the drop. It also shows volatility, big price swings in short windows when sentiment flips fast.

This moment is weirdly open, and it favors people who actually understand the AI stack. An ex-researcher with real signal can raise $1.5B, post 47% in H1, and do it faster than most pedigreed traders ever could.

Why now. Markets are trying to price bottlenecks that sit far outside classic macro and factor models, like High Bandwidth Memory supply, advanced packaging, data center build cycles, and power grid constraints. If you have hands-on context for chips, training runs, cluster design, and energy, you can connect capex guidance to real throughput and latency gains, then map that to winners in semis, networking, and power. That is an information edge, not a vibe. Funds built around that edge are scaling fast, including Turion at >$2B with 11% YTD through July.

Networks also matter. Ex-researchers often sit close to founders, labs, and compute vendors, so they see demand, pricing, and failure modes sooner, and sometimes get into private rounds that stack on top of public longs. Situational Awareness mixes listed plays with select startup stakes, and its backers include heavy hitters from tech and AI, which compounds the pipeline of ideas and diligence.

Yes, there is risk. The field is crowded because there are only so many liquid AI adjacents, which means drawdowns can be sharp when a new model or cost curve shock hits. January’s DeepSeek release punched the leaders, then they bounced, a clean example of crowding plus reflexivity. The key is sizing, hedges, and knowing where the bottleneck actually moves next.

In my opinion, this is not a passing fad in manager backgrounds, it is a regime change in what counts as “edge.” The hedge fund seat used to belong to the banker with flow, now it tilts to the builder who can read wafers, racks, and megawatts, then express that view cleanly in markets. If you can translate model roadmaps into cash flows, you will outrun most traditional generalists over the next few years. Situational Awareness is proof of concept, and it will not be the last.

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wsj. com/finance/investing/billions-flow-to-new-hedge-funds-focused-on-ai-related-bets-48d97f41