Lenny's PodcastWhy half of product managers are in trouble | Nikhyl Singhal (Meta, Google)
Lenny Rachitsky and Nikhyl Singhal on pM skills are flipping as AI demands builders and judgment.
In this episode of Lenny's Podcast, featuring Lenny Rachitsky and Nikhyl Singhal, Why half of product managers are in trouble | Nikhyl Singhal (Meta, Google) explores pM skills are flipping as AI demands builders and judgment AI is pushing product work away from “information moving” and toward hands-on building plus high-quality judgment about what to ship and why.
At a glance
WHAT IT’S REALLY ABOUT
PM skills are flipping as AI demands builders and judgment
- AI is pushing product work away from “information moving” and toward hands-on building plus high-quality judgment about what to ship and why.
- Companies are likely to shed large parts of their workforce and then rehire fewer, “AI-first” people, creating both opportunity for builders and risk for non-builders.
- Because software iteration is getting dramatically cheaper and faster, PMs will face many more potential changes and must become better at prioritization, systems thinking, and evaluating tradeoffs.
- Career signals are shifting from brand-name resumes and past launches to demonstrable modern tool fluency, current craft, and speed of execution.
- The transition is psychologically hard and exhausting, but many PMs find a “moment of joy” once they build something themselves, which becomes the antidote to burnout and the catalyst for reinvention.
IDEAS WORTH REMEMBERING
5 ideasThe “information-mover PM” is becoming obsolete.
Singhal argues many PMs spent recent years translating status, shaping narratives, and moving docs up chains; AI and new operating models reduce the need for that, favoring people who directly build and decide.
Judgment becomes the highest-paid PM skill as testing gets cheap.
If products can ship 10–100× more experiments, the bottleneck is no longer execution mechanics but deciding what matters, what’s sustainable, and what fits the system and brand.
Expect workforce churn: big layoffs followed by smaller AI-first hiring waves.
He predicts “massive shedding” then rehiring fewer people with modern, AI-native skills—meaning employability hinges on current tool fluency and hands-on capability, not title history.
Your brand and past wins matter less than how modern you are today.
Hiring conversations are shifting toward scenario-based evaluation (tools, thinking, judgment) because prior-era delivery methods may not translate to today’s AI-accelerated product building.
Build internal leverage, not just external features.
A strong near-term move is creating “chief of staff” workflows, agents, and automation that eliminate status reports, reviews, and manual coordination—software that scales your org’s decisioning.
WORDS WORTH SAVING
5 quotesThe information-mover is essentially going to become a dinosaur.
— Nikhyl Singhal
In the next 12 to 24 months, we're gonna see massive shedding of staffs and then massive rehiring.
— Nikhyl Singhal
If you don't love building stuff, you're in trouble.
— Nikhyl Singhal
In two years, I think there won't be any more bad software.
— Nikhyl Singhal
Your goal when you're in your power years is to equally disappoint everyone in your life.
— Nikhyl Singhal
QUESTIONS ANSWERED IN THIS EPISODE
5 questionsHow do you practically define “builder” PM behavior in a way that isn’t just “PM who codes,” and what are concrete signals in interviews?
AI is pushing product work away from “information moving” and toward hands-on building plus high-quality judgment about what to ship and why.
If AI makes it easy to generate 10–100× more product changes, what specific prioritization frameworks or decision filters do you recommend to avoid thrash?
Companies are likely to shed large parts of their workforce and then rehire fewer, “AI-first” people, creating both opportunity for builders and risk for non-builders.
Which parts of ‘alignment’ disappear with AI (status/theatrics) versus remain essential (conflict resolution, strategy tradeoffs), and how should PMs retrain?
Because software iteration is getting dramatically cheaper and faster, PMs will face many more potential changes and must become better at prioritization, systems thinking, and evaluating tradeoffs.
You predict major shedding and AI-first rehiring—what are the top 3 portfolio projects a mid-level PM should complete in 60 days to be in the “rehire” bucket?
Career signals are shifting from brand-name resumes and past launches to demonstrable modern tool fluency, current craft, and speed of execution.
What do you see as the most common failure mode when PMs try to build internal agents/tools for their org (data access, trust, adoption, security)?
The transition is psychologically hard and exhausting, but many PMs find a “moment of joy” once they build something themselves, which becomes the antidote to burnout and the catalyst for reinvention.
Chapter Breakdown
Nikhyl Singhal’s lens on the PM career shake-up
Lenny introduces Nikhyl’s background (Meta, Google, Credit Karma, founder, and Skip communities) and frames the episode as a no-sugarcoating look at what’s changing in product management. Nikhyl sets the tone: almost everything about the PM job is in flux, and the old playbook is breaking.
From “information-mover” PMs to builders: the big shift
Nikhyl contrasts the recent past—PMs spending days moving information up and down org charts—with the emerging present where PMs can build and test directly. He argues this is creating a renaissance for strong builders, even as it raises the bar for everyone else.
Are PMs doing better than 2–3 years ago? Winners, stress, and ‘smiling exhaustion’
Nikhyl says top performers have more choice and are doing better, even if they’re stressed. The stress has shifted from bureaucratic stagnation to fear of falling behind and keeping pace with fast-moving tools and expectations.
What changes next: AI-first orgs, obsoleting the ‘mechanical’ parts, and 10–100× experimentation
Drawing from a CPO meetup, Nikhyl describes how product decision-making and internal operations are becoming “a foreign animal” compared to a few years ago. He predicts AI will obsolete mechanical product work, causing far more tests/changes—and making judgment the scarce skill.
What ‘judgment’ really means—and why ‘bad software’ may disappear
Nikhyl defines judgment as deciding what to build, what not to build, and how changes affect brand, maintainability, and system coherence. He predicts users will tolerate less bad software as AI makes quality improvements and bug fixes cheaper and more accessible.
Company staffing reset: shedding, rehiring, and the rise of ‘AI-first’ talent
Nikhyl forecasts major staff reductions followed by selective rehiring of AI-first employees. He frames this as a productivity reckoning: companies doubled headcount without doubling outcomes, and now want a lighter payload aligned to new workflows.
Why PM roles can still grow: the builder vs. information-mover divide
Despite layoffs, Lenny notes PM job postings are at a 3-year high; Nikhyl explains this by redefining what a “PM” is. The market is hiring builders, while information-movers—often ~half of PMs—face obsolescence unless they reinvent.
The non-builder problem and the ‘should PMs code?’ nuance
Nikhyl warns that people who entered product for communication/process/status coordination are especially exposed. He distinguishes between “PM as the 51st engineer” (low leverage) vs. PMs building internal tools/automation to scale decisioning and eliminate status work.
Adults still matter: wisdom, credibility, and leading through the transition
Nikhyl argues that experience and wisdom remain valuable—especially for founders moving fast—so long as leaders stay hands-on and current. The “adult” advantage is pattern recognition plus the ability to operate in modern AI-first workflows.
Hidden setbacks: diversity, geography, and why brand matters less now
Nikhyl predicts the AI wave could reverse some diversity gains as hiring concentrates in the Bay Area and favors founder-adjacent networks. He also argues logo prestige and past achievements matter less than proof of modern, tool-fluent execution and decision quality.
Why reinvention is so hard: exhaustion, moving targets, and the ‘equal disappointment’ algorithm
Nikhyl explains the psychology behind resistance to change: humans optimize for stability, and mid-career life demands create severe time scarcity. He introduces the idea that people allocate limited time to “equally disappoint everyone,” making sustained upskilling uniquely difficult.
Crossing the threshold: finding joy, increasing pace, and going ego-less
Nikhyl’s core prescription is to “cross the threshold” by committing to reinvention and seeking a first moment of joy building with AI. Practical success requires higher pace, intentional time tradeoffs, and lowering ego about role level—optimizing for the ‘skip job’ rather than the next title.
What the future org looks like: alignment changes, engineering shifts more, and the design plateau
They discuss how alignment remains but becomes less about information theatrics and more about principled decision-making with clearer ground truth. Nikhyl notes engineering is transforming even faster than PM, while design hiring may be plateauing as companies conflate production work with taste-making.
Nikhyl’s AI stack, ‘obsolescence mindset,’ and concrete builds
Nikhyl shares his current preference for Claude (and prior Codex use) and describes building internal tools for his community: matching members, surfacing jobs, automating responses, and training agents on his content. He frames it all as an ‘obsolete yourself’ philosophy—now supercharged by AI.
Closing advice, optimism in chaos, and lightning round highlights
Nikhyl urges people not to wait: the longer you delay, the harder reinvention becomes. They end with lighter topics—TV recommendations, a product pick (Tesla self-driving), a motto reframed for AI, and where to find Nikhyl’s work and communities.
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