Michael Kronthal

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Michael Kronthal

Michael Kronthal

@mkronthal

Head of Research @ Gametime. Former Head of Product Research @ Hinge Health, Lime, Uber, Twitter, and Yahoo!. 'Daddy' to 4, casual IM triathlete, x-lax player

CA Bay Area Katılım Mayıs 2009
674 Takip Edilen658 Takipçiler
Michael Kronthal
Michael Kronthal@mkronthal·
@shreyas So true yet also so under utilized and often either poorly executed or snubbed because leaders have a hard time believing other humans might behave differently than them and their 5 closest friends - despite the fact that is the entire point of doing it in the first place LOL
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Shreyas Doshi
Shreyas Doshi@shreyas·
A good customer segmentation is worth a thousand strategy offsites.
Gokul Rajaram@gokulr

SEGMENT, ALWAYS SEGMENT Most confounding business problems have the same root cause: you haven't segmented your customers. You look at the top-line number. It's flat, or weird, or inconsistent with what your gut tells you. You poke at it and you can't figure out why. The answer is almost always that you're staring at an average that's hiding two or three very different stories. A few places this shows up: 1. When your high-level metrics look wonky or divergent, break them out by segment. A flat retention curve often hides one cohort churning out violently and another expanding aggressively. A "meh" NPS usually has one segment of fanatics and one segment of detractors cancelling each other out. The average is a lie. The segments are the truth. 2. When your product is trying to be everything to everyone, you need to tailor it per segment. If your roadmap has SMB founders, mid-market IT buyers, and Fortune 500 procurement all fighting for features in the same backlog, that's three products in a trench coat pretending to be one. Pick the segment you're actually building for, and ship accordingly. 3. When your pricing or positioning feels wrong no matter where you set it, it's because one SKU or pitch is spanning segments with wildly different needs or willingness to pay. Enterprise will pay 10x what a startup will for the exact same thing. A single price point either leaves money on the table at the top or closes the door at the bottom. Segment the packaging. Segment the price. The pattern holds every time. Whenever a business problem is hard to reason about, break the population into segments and look again. Nine times out of ten, the fog lifts. Importantly, you don't need to use standard gender or demographic segments. You can build your own! (And AI is a superpower here). One of the best segmentations in real life was done by @davidweiden at TellMe Networks in the early 2000s. TellMe was selling phone automation software into financial services: a half-billion dollar market, and they had almost no traction. David built a custom segmentation framework called Rifle, which scored every prospect on five weighted criteria. Where the customer was in their buying cycle (engage before the RFP, not after). Whether their long-distance carrier was compatible with TellMe's deployment model. Three more criteria with explicit weightings, including negative scores that disqualified prospects outright. The whole company aligned on the scoring. Sales stopped chasing bad-fit accounts. Product stopped building features for customers who would never close. Marketing stopped spraying the market. Over two years, Rifle drove $20M in ARR inside the qualified segment and took TellMe from a loss to a profit. They literally would have failed without the segmentation. . Founders: when a metric confuses you, when your product feels scattered, when your sales pitch or pricing won't land, segment. Segment, always segment.

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Aditya Bandi
Aditya Bandi@bandiaditya·
I’m thrilled to announce we’ve raised $44M to build a new home for product design. Meet @noondesign. No workflow is more broken and fragmented in 2026 than the product designers’. The very same people who care most about building software don’t have software purpose built for them. @kushagrasinha7 and I have lived this problem first hand as designers ourselves. That’s why we built Noon. The first product design tool that works entirely on your product code, so you can design not only how a product looks, but also how it works. With AI at its core that works in seconds, not minutes. For the first time, you can create, iterate, build, test and ship. All in one canvas. No translations or roundtrips to the codebase and back. Comment “Get Noon” and we’ll get you on the list for early access.
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Michael Kronthal
Michael Kronthal@mkronthal·
@aakashgupta I believe that this is why Researchers will play an even more valuable role in the future. Teams don't need speed, they need velocity - speed with direction. The teams that speed in the right direction will win.
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Aakash Gupta
Aakash Gupta@aakashgupta·
The real cost of AI coding tools is the strategic debt they create when your team ships 10x faster with no direction. A typical product team runs $1.4M per year fully loaded. Five engineers, one designer, one PM. That team needs to return $1.4M in profit to justify its existence. The PM's job is to outline the path. Now give those engineers Claude Code and Cursor. Things that took days take hours. The team can ship 3x more features per quarter. Sounds like pure upside until you realize what actually happened: you tripled the speed at which a team with no strategic clarity burns money. I've seen this firsthand. A leadership team I was part of had 14 priorities. Every team cherry-picked different ones. Six months later, nothing was aligned. We cut to 3. Growth immediately accelerated. The pattern repeats everywhere. Go ask any engineer on your team right now: what's your product strategy? 9 out of 10 can't answer. That was survivable when shipping was slow. When your team can prototype in 60 seconds and push code the same day, "no clear direction" compounds into wasted cycles at a rate nobody budgeted for. This is the strategy crisis nobody's talking about. We're drowning in velocity and starving for direction. I broke down my full 7-step framework for building product strategy with Claude Code, including the snap strategy method that gets you from zero to a real strategy doc in 2 hours.
Aakash Gupta@aakashgupta

Product strategy is becoming the most important skill not just for PMs, but software engineers and designers too. I put together the ultimate resource to building one based on my 16+ years of PM experience: news.aakashg.com/p/ai-product-s…

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Michael Kronthal
Michael Kronthal@mkronthal·
@garrytan Sign me up! UX Researchers have been doing that since before it was cool.
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Garry Tan
Garry Tan@garrytan·
AI means people need to stop thinking so much about what their boss wants They need to start thinking much much more about what customers want And that will make a big difference in society: too much of it is caring about Keynesian beauty contests and other such contests that are disconnected from the direct needs of others Less dead weight loss. More actually helping people.
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Michael Kronthal
Michael Kronthal@mkronthal·
@scottbelsky @garrytan Prepare for the rise of the 10x Product (UX) Researcher. This is precisely where we sit, especially those of us who come from the social sciences like anthropology.
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scott belsky
scott belsky@scottbelsky·
another era of product intuition leading the way… “the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral.”
signüll@signulll

the most underrated hire right now is a great product person. when i say product person i'm def not talking about a product manager. perhaps i think there has to be somewhat of a new role. i don't have a good name for it yet but maybe something like "product thinker".. someone with an intuitive grasp of the product as it exists, where it's soft, where it sings, & how to iterate it toward something even sharper. in some sense, this person has to cohesively hold in their head where this product should be 2 years from now & work backwards from that. i say this cuz when building was hard, engineering was the bottleneck & the status hierarchy often reflected that. building is no longer hard. which means the variance in outcomes has shifted almost entirely to judgment on what to build, how to sequence it, & how to talk about it. & the story matters as much as the thing. internally, it organizes the team around a shared model of why. externally, it shapes the interpretive frame users bring to their first experience. you can't retrofit narrative onto a product & expect it to land, it has to be load bearing from the start. the rarest version of this person sits at the intersection of culture & deep technology. someone genuinely bilingual. they know what's technically possible & they know which cultural currents are real vs. ephemeral. that combo is what separates products that feel inevitable from products that feel assembled. before ppl clap back with this person has always been valuable, i know.. i am just saying now they might be the most *important* person in the room. their value compounds like never before.

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Michael Kronthal
Michael Kronthal@mkronthal·
@anishmoonka Layoffs suck, especially these days, but that severance is 🔥showing the company taking care of folks impacted to help them transition to their next thing. If @blocks is being looked at as an example by other companies, they should also follow their example of compassion.
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Anish Moonka
Anish Moonka@anishmoonka·
Block just cut 4,000 people while posting its best quarter in company history. The stock jumped 23%. But the real story is what made this possible. Block built an open source AI agent called Goose (powered by Anthropic’s Model Context Protocol) and deployed it across the entire company. One engineer says 90% of his code is now written by Goose. Non-technical teams are using it to write SQL queries, close support tickets, and manage inventory without waiting for engineers. Block’s CTO told Lenny’s Newsletter it saves employees 8 to 10 hours per week. When you multiply that across thousands of people, you start to understand how a company can look at its org chart and realize half the seats are redundant. The financial proof is hard to argue with. Q4 gross profit hit $2.87 billion, up 24% year over year. Cash App grew 33%. Operating income went from $13 million to $485 million in twelve months. Block raised its 2026 outlook to $12.2 billion in gross profit. All of that growth came while the company was already quietly shrinking, down from 13,000 employees in 2023 to 11,000 by late 2025. Now Dorsey is taking it to its logical conclusion. Block with 6,000 people generates roughly the same revenue as Block with 13,000. That’s not a guess anymore, the Q4 numbers proved it. Revenue per employee just doubled overnight. The company goes from ~$2.2 million per employee to ~$4.2 million, putting it closer to the efficiency ratios of companies like Shopify and Stripe. Three weeks ago Bloomberg reported Block was cutting “up to 10%.” Three weeks later: 40%+. Dorsey saw Q4 numbers strong enough to absorb $450 to $500 million in severance costs and went all in. He’s betting that smaller teams with AI tools will outperform larger teams without them. And Block is one of the few companies that actually built the AI tooling internally before making the cut, rather than waving at “AI transformation” as a vague justification. The severance package (20 weeks salary plus tenure bonuses, equity through May, 6 months healthcare, $5,000 stipend) is above average for tech. The company ended 2025 with $9.2 billion in liquidity. Dorsey kept communication channels open through Thursday and hosted a live farewell session. For a cut this deep, the execution was more transparent than most. This is probably the first major case of a public company explicitly restructuring around AI productivity gains it can actually measure. If Block’s bet works, every CEO with an AI roadmap and a bloated org chart is going to be watching very closely.
jack@jack

we're making @blocks smaller today. here's my note to the company. #### today we're making one of the hardest decisions in the history of our company: we're reducing our organization by nearly half, from over 10,000 people to just under 6,000. that means over 4,000 of you are being asked to leave or entering into consultation. i'll be straight about what's happening, why, and what it means for everyone. first off, if you're one of the people affected, you'll receive your salary for 20 weeks + 1 week per year of tenure, equity vested through the end of may, 6 months of health care, your corporate devices, and $5,000 to put toward whatever you need to help you in this transition (if you’re outside the U.S. you’ll receive similar support but exact details are going to vary based on local requirements). i want you to know that before anything else. everyone will be notified today, whether you're being asked to leave, entering consultation, or asked to stay. we're not making this decision because we're in trouble. our business is strong. gross profit continues to grow, we continue to serve more and more customers, and profitability is improving. but something has changed. we're already seeing that the intelligence tools we’re creating and using, paired with smaller and flatter teams, are enabling a new way of working which fundamentally changes what it means to build and run a company. and that's accelerating rapidly. i had two options: cut gradually over months or years as this shift plays out, or be honest about where we are and act on it now. i chose the latter. repeated rounds of cuts are destructive to morale, to focus, and to the trust that customers and shareholders place in our ability to lead. i'd rather take a hard, clear action now and build from a position we believe in than manage a slow reduction of people toward the same outcome. a smaller company also gives us the space to grow our business the right way, on our own terms, instead of constantly reacting to market pressures. a decision at this scale carries risk. but so does standing still. we've done a full review to determine the roles and people we require to reliably grow the business from here, and we've pressure-tested those decisions from multiple angles. i accept that we may have gotten some of them wrong, and we've built in flexibility to account for that, and do the right thing for our customers. we're not going to just disappear people from slack and email and pretend they were never here. communication channels will stay open through thursday evening (pacific) so everyone can say goodbye properly, and share whatever you wish. i'll also be hosting a live video session to thank everyone at 3:35pm pacific. i know doing it this way might feel awkward. i'd rather it feel awkward and human than efficient and cold. to those of you leaving…i’m grateful for you, and i’m sorry to put you through this. you built what this company is today. that's a fact that i'll honor forever. this decision is not a reflection of what you contributed. you will be a great contributor to any organization going forward. to those staying…i made this decision, and i'll own it. what i'm asking of you is to build with me. we're going to build this company with intelligence at the core of everything we do. how we work, how we create, how we serve our customers. our customers will feel this shift too, and we're going to help them navigate it: towards a future where they can build their own features directly, composed of our capabilities and served through our interfaces. that's what i'm focused on now. expect a note from me tomorrow. jack

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Michael Kronthal
Michael Kronthal@mkronthal·
@Saboo_Shubham_ Thanks for sharing this. What does this portend for UX Researchers? Does this open a bridge for them to transition into this new type of PM. Few know or empathize with their users more.
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Shubham Saboo
Shubham Saboo@Saboo_Shubham_·
SPEC IS BECOMING THE PRODUCT An Anthropic engineer gave Claude a spec, pointed it to an Asana board and left for the weekend. Claude broke it into tickets and spun up a team of agents. The agents started picking up tasks on their own. No one told them to. They just did.
Shubham Saboo@Saboo_Shubham_

x.com/i/article/2008…

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Michael Kronthal
Michael Kronthal@mkronthal·
@aakashgupta Love that you featured Caitlin's work! But, not doing customer research due to time has always been a red herring. The real bottlenecks are prioritizing output over outcome, reluctance to speak truth to power, and arrogance over curiosity.
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Aakash Gupta
Aakash Gupta@aakashgupta·
Everyone talks about AI replacing jobs. The more interesting shift is AI replacing the excuses teams use to skip the hard work. User research is the most skipped step in product development. PMs know they should do it. They have the frameworks. They've read the books. They still ship features based on 3 Slack conversations with friendly customers. The real blocker was never methodology. It was labor. Transcribing, coding, synthesizing, cross-referencing, verifying patterns across dozens of participants. That workflow took weeks and cost thousands in research ops headcount. This walkthrough compresses that entire pipeline into Claude. Load context, run per-participant analysis, verify patterns, audit the AI's reasoning. Four steps that used to require a dedicated research team. The PMs who adopt this won't just ship better products. They'll make decisions so fast that competitors still running 6-week research cycles can't keep up. Speed of insight is becoming the new moat.
Aakash Gupta@aakashgupta

You should be researching your users more. So I got an expert to give me a masterclass in how to do it with AI: Step 0: Load Context Into Claude - 8:22 Step 1: Per-Participant Analysis - 16:12 Step 2: Verification - 26:06 Step 3: Auditing AI - 51:31

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Michael Kronthal
Michael Kronthal@mkronthal·
Great advice for Researchers and Designers too. And don't sleep on #15 "...I'm probably pitching product value without the org-political piece" #Facts
George from 🕹prodmgmt.world@nurijanian

I fell down a Reddit thread about PM storytelling and honestly the advice was better than most PM books. Here's what stood out: 1. Storytelling isn't a soft skill, it's an efficiency metric. You'll know it's working when meetings get shorter, fewer people argue past each other, and decisions happen faster. If that's not happening, your story isn't working regardless of how compelling it sounds. 2. I used to bury the decision in slide 10. Then I tried opening with "This meeting is to decide X" and suddenly people stopped asking "wait, what are we deciding?" 3. I started tracking where people interrupt me. When it happens at the same moment across different audiences, that's usually a structural problem in my narrative (basically A/B testing for stories), not just difficult people. 4. You're not trying to entertain people, you're trying to get them to remember what you said. Are they repeating your framing back to you? If they're using different words to describe the problem, you haven't told the story well enough. If they're quoting you verbatim, you've created a shared mental model. 5. The book StoryBrand is mentioned, but it is missing something critical for PMs: "what's in it for me?" for internal stakeholders. The framework works for customer-facing stories but doesn't address org-political dynamics. Engineering needs to know resourcing impact, sales needs customer benefit proof, execs need financial outcomes. 6. Specific customers work better than aggregate stats. "Sarah from enterprise account X spends 3 hours every Monday doing this manually" lands harder than "many users struggle with this." Nobody asks if one person is representative. 7. I set a timer before meetings and try to say the entire pitch in 60 seconds. If I can't finish, my narrative is too complex. 8. What reads well doesn't equal what sounds good. I read my deck script out loud before meetings now. You catch awkward phrasing, run-on sentences, and unclear logic that looked fine written down. 9. Storytelling quality might be an org design problem, not a skills problem. If poor storytelling creates drag (repeated meetings, misalignment, wasted resources), why does your org allow low-quality narratives to reach exec reviews? Elite companies might have forcing functions that prevent bad stories from consuming bandwidth. 10. The runway metaphor for capability sizing: frame customers as planes (Cessna to A380), frame your product as a runway. "We're trying to land enterprise A380s but our billing system runway can only handle a regional jet." Everyone immediately visualizes big plane on small runway = crash. 11. Before/after framing helps. What's broken today, what changes if we ship this. Makes complex product changes easier to grasp. 12. If people aren't repeating your framing weeks later, your story didn't stick. The real buy-in metric is whether your metaphor becomes shorthand in planning meetings without you being there. 13. The "charisma trap" is real. Most orgs claim to value substance over style, but meeting culture rewards performance. Amazon's 6-page memo culture explicitly removes charisma as a variable by forcing written narratives. 14. Bad storytelling might just be bad strategy in disguise. If you can't explain the stakes in one sentence, maybe the stakes aren't clear. Storytelling struggle is often thinking struggle. No amount of narrative polish fixes directional uncertainty. 15. I write down what each stakeholder actually gets from this. What does the eng lead get? What does sales get? What does the exec get? If I can't answer these, I'm probably pitching product value without the org-political piece.

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Cody Schneider
Cody Schneider@codyschneider·
talked to my friend, he's a PM at a big company it told me that almost everybody in the product side of the work is using AI for prototyping but they're just building things things that nobody wants it's engineering again building for the sake of build not because the buyer wants it if the cost of product goes to zero if the cost of code goes to zero building the thing is what doesn't constrain you anymore building the right thing is what constrains you and the right thing is what the market wants to buy and there are so few people that know how to identify what the market wants to buy
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Michael Kronthal
Michael Kronthal@mkronthal·
@aakashgupta Does this mean Product/UX Researchers are the new PMs? We excel at understanding unmet user needs, routinely generate divergent ideas to solve them, and are trained to objectively evaluate and iterate them. Yea, I think so.
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Aakash Gupta
Aakash Gupta@aakashgupta·
Naval just told 3M+ people that PMs who can vibe code are the new power players in tech, and most of them don’t realize that’s what he said. “Vibe coding is the new product management” means the person who understands the user problem, frames the right prompt, and evaluates whether the output actually solves it just became the highest-leverage role on every team. 78% of dev teams already use AI-assisted coding. Carnegie Mellon replaced wireframe assignments with vibe-coded prototypes this year. Collins Dictionary named “vibe coding” its 2025 Word of the Year. The entire stack compressed in 12 months. And PMs were built for this compression. One PM at Observe built Salesforce-to-Notion automations with Claude Code that saved his team hours per week. His biggest lesson: AI needs management like a junior employee with zero context. Clear goals, explicit expectations, accountability for outputs. That’s a product manager’s entire job description. The best PMs are already becoming what some companies call “Full-Stack Product Leads” who own everything from user insight to working prototype. The best engineers are becoming “product engineers” who own features end-to-end. Both roles are converging on the same skillset: product judgment plus the ability to ship. A METR study found apps built purely through vibes were 40% more likely to have critical security flaws. The industry calls it the “Vibe Coding Hangover.” Functional zombie apps everywhere, built by people who could prompt but couldn’t evaluate. The gap between someone who can vibe code and someone who can vibe code with product judgment is the gap between a demo and a business. Naval is right. Vibe coding is the new product management. And that means product managers who learn to vibe code own the next decade.
Naval@naval

Vibe coding is the new product management. Training and tuning models is the new coding.

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Shreyas Doshi
Shreyas Doshi@shreyas·
Perfect alignment between all cross-functional teams in a fast scaling company is a fantasy that will never come true. Founders of such companies would be wise to internalize this, because most of them are constantly being fooled by senior managers who blame lack of perfect alignment as the reason their products are not achieving the expected outcomes. Nearly every meeting goes back to some version of “we need teams to be more aligned” with some action items captured to create that alignment, along with occasional re-orgs to create more alignment, and yet this lack of alignment is never solved, for 5 years, 10 years, and yet the insanity continues with the next wave of senior managers again blaming lack of alignment for what is really a skill issue at its core.
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Michael Kronthal
Michael Kronthal@mkronthal·
U Miami fans gonna know #HYDR by the end of the game. Hotty Toddy chants already echoing throughout the concourse #fiestabowl Go Rebels!!
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Michael Kronthal
Michael Kronthal@mkronthal·
@hnshah 💯 it takes a special type of product leader (emotionally secure, principled decision making, structural support) to acknowledge this and create safe spaces for it internally. Afterall, humans are complicated. Leaning into that complexity is where the real break throughs live.
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Hiten Shah
Hiten Shah@hnshah·
One more layer that’s worth naming. Even when teams talk to customers with good intent, most companies structurally make understanding hard to sustain. The incentives quietly punish it. Deep understanding creates friction. It slows momentum and introduces ambiguity right when leadership wants certainty. So the system teaches people which kinds of “customer insight” are acceptable. Over time, people adapt because they’re paying attention to what gets rewarded. They learn what survives review. Real understanding rarely produces clean answers. It surfaces tradeoffs, second-order effects, and questions about whether the direction itself is wrong. That kind of insight is inconvenient. It doesn’t compress well. It often dies in the middle layers of decision-making. That’s why customer understanding is an organizational behavior problem. The companies that actually understand customers design for prolonged uncertainty. They tolerate confusion longer than competitors by protecting dissenting signals. They’ll even slow decisions when the data feels “off,” even if that looks irrational from the outside. This becomes their competitive advantage.
Shreyas Doshi@shreyas

The main problem with the “talk to customers” mantra in *top tier companies* is that the vast majority of your people who talk to customers are going into customer conversations for confirmation & performative heroics and not for learning & insight. So, they will cherrypick customer feedback, not actually listen to everything the customer is saying, follow certain threads for confirmation and ignore others that might point to disconfirmation, and then present their “findings” in a sexy & neat package that’s irresistible to the CEO & execs. There is no great solution to this problem, because most people, including >80% of highly intelligent people (in any company), are just wired to seek confirmation & validation. They are not wired with the desire to win in the market (even though everyone is sure they are). Now, if you must have a mantra to motivate & guide the masses in your company, make your mantra to “understand customers” rather than “talk to customers”. Talking is easy, understanding is rare. ‘Understanding’ is a much higher bar than ‘talking’, and that is precisely the kind of high bar a top-tier company must meet. And the best solution is leading from the top by yourself embodying what understanding customers truly means and holding an extremely high bar for insight that’s presented to you as the leader. As you nod along and confirm to yourself ‘I am already doing this’, consider that perhaps you aren’t, because if you were truly doing this in proportion to your ambition for your team & company, many more of your recent features and products would’ve won than has been the case. So perhaps the best place for you to start, as a leader, is to seriously consider how you yourself are falling short of the ideal that you wish for others on your team.

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Michael Kronthal
Michael Kronthal@mkronthal·
@shreyas But 3) if company culture and internal power dynamics are not also aligned, it doesn't matter how well customers are understood by those who do. They will be at best ignored or at worst designated as "difficult" or "not team players" for speaking truth to power.
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Michael Kronthal
Michael Kronthal@mkronthal·
@shreyas This is precisely why companies should hire dedicated Researchers: 1) They are professionally trained to acknowledge and minimize bias throughout the process, and 2) Since they are not directly accountable for a feature's success, they are less susceptible to confirmation bias.
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Shreyas Doshi
Shreyas Doshi@shreyas·
The main problem with the “talk to customers” mantra in *top tier companies* is that the vast majority of your people who talk to customers are going into customer conversations for confirmation & performative heroics and not for learning & insight. So, they will cherrypick customer feedback, not actually listen to everything the customer is saying, follow certain threads for confirmation and ignore others that might point to disconfirmation, and then present their “findings” in a sexy & neat package that’s irresistible to the CEO & execs. There is no great solution to this problem, because most people, including >80% of highly intelligent people (in any company), are just wired to seek confirmation & validation. They are not wired with the desire to win in the market (even though everyone is sure they are). Now, if you must have a mantra to motivate & guide the masses in your company, make your mantra to “understand customers” rather than “talk to customers”. Talking is easy, understanding is rare. ‘Understanding’ is a much higher bar than ‘talking’, and that is precisely the kind of high bar a top-tier company must meet. And the best solution is leading from the top by yourself embodying what understanding customers truly means and holding an extremely high bar for insight that’s presented to you as the leader. As you nod along and confirm to yourself ‘I am already doing this’, consider that perhaps you aren’t, because if you were truly doing this in proportion to your ambition for your team & company, many more of your recent features and products would’ve won than has been the case. So perhaps the best place for you to start, as a leader, is to seriously consider how you yourself are falling short of the ideal that you wish for others on your team.
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Lenny Rachitsky
Lenny Rachitsky@lennysan·
My biggest takeaway from @chipro: 1. The biggest improvements to your AI product come from talking to users and understanding their feedback, not from adopting the latest models or staying glued to AI news. Many companies waste time debating which technology to use, when the real wins come from better user experience and data preparation. 2. Most AI product problems aren’t AI problems. When companies think they have an AI performance issue, it’s usually a user experience problem, an organizational communication gap, or a data quality issue. One company thought their AI lead scoring system was broken, but the real issue was that the marketing team wasn’t asking the right questions to get useful data. 3. How you prepare your data matters more than which database you choose. Companies see their biggest AI performance gains from better organizing and preparing their information—breaking content into the right size chunks, adding summaries, converting content into question-and-answer format—rather than agonizing over which technical infrastructure to use. 4. Your best performers benefit most from AI tools. In a controlled experiment, the highest-performing engineers got the biggest productivity boost from AI coding assistants, not the lowest performers. Senior engineers who already knew how to solve problems used AI to work even faster, while low performers often just copied and pasted code they didn’t understand. 5. Fine-tuning should be your last resort. Before investing in fine-tuning a model, try simpler solutions first: improve your prompts, add basic post-processing scripts, or fix your data pipeline. One company caught 90% of its model’s mistakes with a simple script. Fine-tuning creates ongoing maintenance headaches and should only be used when everything else has been maxed out. 6. You don’t need to be perfect to win. Many successful companies choose “good enough” over perfect when implementing AI systems. They calculate whether investing two engineers to improve accuracy from 80% to 85% is better than using those same engineers to launch an entirely new feature. Often, the new feature provides more value. 7. AI productivity is nearly impossible to measure. Companies invest heavily in AI coding tools but can’t clearly prove they work. When forced to choose between expensive AI subscriptions for their team or hiring one additional person, many managers choose the person, not necessarily because AI doesn’t help but because headcount feels more tangible. 8. Systems thinking matters more than coding. As AI automates more coding tasks, the ability to understand how different components work together becomes the most valuable skill. According to Stanford’s CS curriculum chair, coding is just a tool—the real skill is understanding how systems work and designing step-by-step solutions to problems. 9. We’re running out of internet text to train on. The world has essentially exhausted publicly available text data for training AI models. This means future improvements will come less from feeding models more data and more from better training techniques, human feedback, and finding new data sources like audio and video. 10. Many people don’t know what to build despite having powerful tools. Even with AI tools that can build almost anything, many employees face an “idea crisis”—they simply don’t know what to create. The best approach: spend a week noticing what frustrates you in your daily work, then build small tools to solve those specific pain points.
Lenny Rachitsky@lennysan

What people think improves AI products vs. what actually works with @chipro Chip Huyen was a core developer on @Nvidia’s Nemo platform, a former AI researcher at @Netflix, and taught AI at @Stanford. She’s a two-time founder and the author of two widely read books on AI, including "AI Engineering", which has been the most-read book on the O’Reilly platform since its launch. We discuss: 🔸 What people think makes AI apps better vs. what actually makes AI apps better 🔸 What is pre-training vs. post-training, and why fine-tuning should be your last resort 🔸 How RLHF (reinforcement learning from human feedback) actually works 🔸 Why high performers are seeing the most gains from AI coding tools 🔸 Why data quality matters more than which vector database you choose 🔸 Why most AI problems are actually UX issues 🔸 Much more Listen now 👇 • YouTube: youtube.com/watch?v=qbvY0d… • Spotify: open.spotify.com/episode/0LhAVO… • Apple: podcasts.apple.com/us/podcast/al-… Thank you to our wonderful sponsors for supporting the podcast: 🏆 @dscout — The UX platform to capture insights at every stage: from ideation to production: dscout.com 🏆 @justworks — The all-in-one HR solution for managing your small business with confidence: justworks.com 🏆 @withpersona — A global leader in digital identity verification: a former machine learning instructor

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Shreyas Doshi
Shreyas Doshi@shreyas·
@signulll Because most humans need something to look forward to, something they must be doing (or be seen doing), and something to tell interesting stories about. World travel is also a scam but few are ready to see that.
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signüll
signüll@signulll·
why do people enjoy going to see music artists in big venues? like dua lipa performing at some giant arena or whatever. like what are you getting out of this experience? i would much rather listen to the album on spotify. i only attend live music for small artists in small intimate venues.
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Michael Kronthal
Michael Kronthal@mkronthal·
@lennysan @nikitabier And yet the tech industry has systematically decimated their user research teams the past several months if not years. You interview plenty of Product leaders. How would you explain this apparent paradox?
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