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What I look for when hiring senior PMs

Jen Huang

Co-founder & CPO, Fluxon

July 30, 2026

Contents

Summarize this article

I have hired PMs at Google, at Stripe, and now at Fluxon. The kind of PM who did well changed at each company but three qualities always matter:

They've taken a product or feature from 0 to 1
They care deeply about the problem they’re solving and their user
They know their craft, but stay curious

Beyond that, the right candidate depends entirely on the product, company, and stage. A few examples:

Google Search Ads: analytical operators

I led the Ads Targeting team at Google. We built the products advertisers used to decide where their ads showed up: keyword matching, website matching, Dynamic Search Ads. Search Ads was already running at enormous scale, and one mistake could cost billions. There, PMs there needed to:

Study the data closely and use it to improve performance
Work across a large, complex organization
Build alignment across teams before launch

A good idea was not enough. You had to prove it and then bring people along.

Stripe: creative thinkers with design taste

Then I joined Stripe as their fifth product manager. Stripe's success came from making something complex, accepting payments, feel refreshingly simple for users. There was often no established playbook, so we needed PMs who could:

Stay close to customers and understand their challenges, pain points and needs
Show strong UX judgment and design taste
Bring a desire to change the conventional approach and build something innovative for users

The best candidates got their hands dirty to make the product better.

Fluxon: sharp, adaptable generalists

At Fluxon, I look for sharp, adaptable generalists. As a consultancy, we can't always predict what our next customer will need. The market moves fast, and our PMs have to enter an unfamiliar industry, earn client trust, and start adding value quickly. That means people who can:

Manage expectations and hard conversations
Help teams separate real P0 priorities from distractions
Learn a new market without being intimidated by it
Lead well without having all the answers on day one

Plenty of senior PMs lead well once they know the domain, but the ones we need can run a call in week one of a project, in an industry they've never worked in.

What I listen for now

I pay less attention to where someone has worked and more to whether they can explain why the way they worked made sense in that company.

A PM who tells me their last company had too much process, or not enough of it, is describing a preference. A PM who can tell me what that process was protecting, and when it stopped being worth the cost, is describing judgment. The biggest lesson I have learned is that there is no universal template for the perfect PM. Hire for the environment they are entering, not just someone you have seen succeed somewhere else.

I wrote about the other side of this for Forbes a few years ago: how a founder picks the right product manager for a job. This piece is the same problem seen from the hiring manager's perspective.

Jen Huang

Co-founder & CPO, Fluxon

125k

Of users across Google

1230m2

Of square feet analyzed

Of buildings across Google

Sanitized example of a non-technical employee's AI growth profile

How do you tell whether an AI prediction is credible?

Run three tests. Does the claim name a date and a number you could check, does the forecaster publish their misses as well as their hits, and what do they lose if the claim turns out wrong. Most industry commentary fails the first test, which makes the other two unnecessary.

How are forecasts scored?

Forecasting competitions score each prediction with a number. A perfect forecast scores zero, pure guessing scores 0.5, and being reliably wrong scores 2.0. The useful part is that a score only works on claims specific enough to grade, which rules out most public predictions straight away.

Are expert predictions better than random guesses?

Slightly better, and the detail matters. Philip Tetlock gathered close to 30,000 expert predictions and found that experts "edged out the dart-tossing chimp but their margins of victory were narrow". The popular version of this finding, that experts do no better than chance, goes further than the research does.

What makes a forecaster unreliable?

Tetlock found the worst performers shared one habit. They had one big idea and repeated it, whatever new evidence arrived. That habit is easy to spot in AI commentary, where much of the loudest output is a single 2023 idea with newer examples attached.

Why do the forecaster's incentives matter so much?

Because incentives shape forecasts more reliably than intelligence does. An AI lab predicting rapid growth has a company valuation resting on that expectation, and a consultancy predicting that the value lies in implementation sells implementation. Neither is necessarily wrong, but a reader should discount both.

Did Fluxon really predict that AI labs would sell services?

We told our own team about 18 months before it happened that service companies would capture much of the value, because the building work is the hard part. In 2026 Anthropic announced a services company and OpenAI launched a deployment company with $4 billion behind it. We also had a commercial interest in that outcome, which readers should weigh.

What is wrong with citing only your successful predictions?

Someone making many loud claims will get some right by chance alone, so a single hit says almost nothing about skill. Without the full set of calls, including the wrong ones, a genuinely good record and a lucky one look identical from outside.

How should a company publish a prediction record?

Write the claim down before the outcome, with a date and a number attached, and publish the losses as prominently as the wins. A record put together afterwards from memory is close to worthless, because memory quietly edits out the calls that did not work.

Is "AI will replace junior developers" a real prediction?

Not in the form it usually appears. It names no date, no measurement, and no definition of "replace", so no outcome can prove it wrong. Rewrite it as a specific claim about job postings falling by a stated amount by a stated date and it becomes gradeable. Far fewer people are willing to say that version.

What should I do with a prediction that fails all three tests?

Read it as commentary rather than foresight, which is a perfectly reasonable thing to read. The mistake is not reading it. The mistake is treating it as insight and letting it shape a roadmap, a hiring plan or a budget.

What is Fluxon's current prediction?

That by the end of 2027, most production AI work at the companies we serve will run on open models you can download and run yourself, rather than on paid access to a frontier model. We expect frontier models to be kept for a small share of genuinely hard work. If the paid models pull far ahead again, we will have been wrong.

How often should predictions be revisited?

Whenever evidence arrives that would have changed the original call, which is a different trigger from a date in the calendar. The best forecasters update when new information appears. The worst treat sticking to their earlier position as a virtue.