About
I'm passionate about building awesome products, driving breakthrough research, and creating great businesses. I enjoy putting together the teams and organizations that make a habit of doing these things.
I've taken numerous products from 0->1, and enjoy building and innovating from scratch. I've also scaled a few 1B+ MAU and a number of 100M+ MAU products, and understand the innovation and operational discipline needed for that. I'm always excited to push innovation and creativity across the dimensions of technology, research, business, art and media while finding ways to delight customers and creators.
I now lead Product Management, UX and UX Research for an applied AI team that pushes bleeding edge cross-disciplinary ML capabilities through building truly transformative products across Google including on Android, Chrome, XR, Search, YouTube, Cloud etc.
I previously worked on YouTube Shopping in particular our efforts around Live Shopping, Search and Discovery and Consumer experiences. In previous roles I also led the product team responsible for the Google Discover Feed Experiences and Corpus, and before that spent eight years building up the teams on Google Search responsible for Media (TV, Movies, Music, Books, Games etc.) and Events experiences, while also leading the Delight (Doodles, Easter Eggs etc.) programs for Google Search. On Search, I also at times led the product team for features, UI experiences, quality and policy for the Google Knowledge Graph and Answers for Google Assistant and Search.
I was also the founding PM for both YouTube Live and Games on Google+/Play Games and led product initiatives on Payments and Ads, as well as served as Chief of Staff for the YouTube Tech org.
I advise startups and founders in spaces and on topics I can be helpful. I often speak about building products, technology, user experience and management, and guest lecture at a few graduate schools including Stanford, Chicago, Cornell, Carnegie Mellon, Wharton and Michigan.
I enjoy puns, emojis, and gratuitous smileys in professional emails. :-)
Articles by Satyajeet
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Why not take more swings? You miss a 100% of the shots you don't take...
Why not take more swings? You miss a 100% of the shots you don't take...
Happy New Year! After a while, we had the chance to have a longer vacation and travel. It was wonderful spending time…
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Unintended consequences: Why your songs got shorter and your videos got longer
Unintended consequences: Why your songs got shorter and your videos got longer
These are my favorite examples of unintended consequences that I happened to walk someone through last week. Years ago,…
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Avengers Endgame: how trivial passions can nudge a career
Avengers Endgame: how trivial passions can nudge a career
The Internets reminded me that today was exactly 2 years since Avenger's Endgame was released. I remember that day…
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20 years to the date!
20 years to the date!
I'm bad at remembering dates. Like, really bad.
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Even if the insight is right, it’s all in the execution: TikTok and Quibi
Even if the insight is right, it’s all in the execution: TikTok and Quibi
I’m spending a lot more time on TikTok recently, and so found myself reflecting a little longer than usual after…
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Can Netflix own childhoods? And do they want to?
Can Netflix own childhoods? And do they want to?
A line about Disney that has really stuck with me over they years is “Disney owns your childhood”. Across the…
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“Decades in Weeks” and “Both Jordan and Will Perdue played basketball”: 2 observations while educating a Kindergartener
“Decades in Weeks” and “Both Jordan and Will Perdue played basketball”: 2 observations while educating a Kindergartener
Here’s trying to get back into the writing habit again… Just 2 observations, and 2 quotes, as I try to get back into…
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Materials from my Product Management workshop
Materials from my Product Management workshop
One of the things I've really enjoyed doing over the last year is teaching and presenting on the work I do more. I do…
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People are selfish, shameless and free-riders.. and other lessons from a playground. :-)
People are selfish, shameless and free-riders.. and other lessons from a playground. :-)
Almost every weekend I end up at a playground in a park near where I live. It's a really nice playground in a really…
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Measure f-ing everything, and assume f-ing nothing!! - Or how mentoring ruined lives :-(
Measure f-ing everything, and assume f-ing nothing!! - Or how mentoring ruined lives :-(
I've been really enjoying the Freakonomics podcast of late. This episode and the lesson we should take a away from it…
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Activity
29K followers
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Satyajeet Salgar shared thisEvery role in every company will change... Full conversation/episode of Product Faculty's AI CXO podcast here, or wherever you like your podcasts https://lnkd.in/ghTQSmux
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Satyajeet Salgar posted thisSo "post-economic" is officially a way to describe people now? :-)
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Satyajeet Salgar shared thisTurns out if you have enough walking 1:1s outside, one fine day you show up in filler footage of your CEO being interviewed by Time magazine. :-) I don't even remember seeing any cameras that day...
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Satyajeet Salgar shared thisEvery year around Jan/Feb, I think "oh, there's not too much we're doing for Google I/O this time", but then May rolls around and we have a bunch of things cooking yet again.... 14 days to go. :-) https://io.google/2026/
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Satyajeet Salgar shared thisStill talking too much with my hands but grateful for a a couple of The University of Chicago Booth School of Business connections this week - to chat with students as part of the Kilts Center for Marketing's event and get an alum feature in the Chicago Booth Review.
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Satyajeet Salgar shared this💯 👇Satyajeet Salgar shared thisWe just wrapped up an engineering hire at Floto. One thing I did differently this time: I added a Product Sense interview to the loop. 19 years ago, I interviewed for my first PM role at Google. Part of that loop was an engineering interview — they wanted to know if the PM actually understood the technology being built. Now it feels like the inverse matters more. With AI writing all of our code, the constraint has shifted. What I need from an engineer today isn't just someone who can build — it's someone who understands *what* they're building and *why*. Who can hold the user in their head while they're making implementation decisions. So we tested for that. And it really changed who stood out. I don't think this is universally true yet. But for a small team building a product-led experience, an engineer who gets product is a force multiplier in a way that a purely technical hire isn't anymore. Curious if others are rethinking what they test for in engineering loops.
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Satyajeet Salgar reposted thisSatyajeet Salgar reposted thisMost people think agentic commerce just leaps to "AI buys stuff for you." But it isn't as simple as it sounds. Commerce breaks into three parts: discovery, brand interaction, and transaction. Agents are going to eat each one on a different timeline: 1. Crawl: AI replaces the blue links. ChatGPT, Claude, and Copilot become the new search bar. 2. Walk: Agents bundle products across merchants, pre-fill your info, and hand you a cart. 3. Run: Agents transact for you, but check in at every step. 4. Fly: You hand an agent a budget and an outcome. It comes back with the goods at your door. We are somewhere between crawl and walk today. Most of the industry is building for fly and telling consumers to expect that as the only worthy solution. Then they all wonder why nothing is "working yet" or taking so long. It requires reimagining many systems of the internet. I recently sat down with Satyajeet Salgar to discuss this future of agentic commerce on the AI CXO podcast by Product Faculty, given the pilot's seat we're in at Shopify. We got into many topics that are definitely worth your listen. Check out the full conversation: - YouTube: https://lnkd.in/emGyvu-R - Apple Podcasts: https://lnkd.in/ebwFF6sz - Spotify Podcats: https://lnkd.in/ejuPd3bk Now, the mental model I see everyone getting wrong: "The agent replaces the merchant relationship." It doesn't. It splits it. Merchants now sell to two personas: the human, and the agent representing the human. Those are somewhat different customers. They need different data, different interfaces, and different trust signals. The merchants who figure this out first aren't the ones with the best AI. They're the ones whose product data, policies, and checkout logic are structured cleanly enough for an agent to leverage and navigate them. Shopify's led from the front inventing all the parts that make this possible for the agentic era. The Shopify Catalog, Universal Commerce Protocol and Embedded Checkout Protocol, the Shopify Knowledge Base app: it's all commerce infrastructure built to make humans and agents traverse the world of shopping alike, without the merchant ever losing the customer. And the most amazing opportunity with agentic commerce is brand loyalty. It doesn't die in this world. It gets stronger, because when an LLM is surfacing facts instead of whoever shouted the loudest in advertising, word of mouth becomes a stronger signal, and emerging brands can rise faster than ever. The product that actually delivers wins more, not less. It's exposed through the truth seeking nature of LLMs. The best brands win. They have for centuries, and will forever more.VP Shopify: "Your Future Customers Will Be Humans, and Agents"VP Shopify: "Your Future Customers Will Be Humans, and Agents"
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Satyajeet Salgar reposted thisSatyajeet Salgar reposted thisEveryone tells you agents are going to be your future customers. But no one tells you how to actually build for them. Until now. In the first episode of Product Faculty’s AI CXO Podcast, Satyajeet Salgar sat down with Mani Fazeli (VP Product at Shopify) to answer probably the most important product question right now: "What do you actually do when your customer is no longer the human… but the agent acting on their behalf?" Here's the actionable insights for you: 1 - You’re no longer building for one user, you’re building for two. There’s still the human on the other side, but now there’s an agent sitting in between, interpreting your product, comparing it, and often making the decision before a human ever sees it. That means: → Your product needs to be interpretable → Your value needs to be comparable → Your differentiation needs to be explicit Make your product legible to machines. 2 - Your product now needs a second interface, one that humans will never see. For years, we obsessed over UX, copy, and flows designed for human consumption. But agents don’t scroll, skim, or get emotionally persuade... they parse structured data, clear logic, and consistent signals. 3 - Your growth strategy is about to break The old playbook: Ads → Landing page → Funnel → Conversion The new reality: Agent → Evaluates → Selects → Executes You’re no longer competing for clicks. You’re competing for selection. It’s the difference between being on a shelf and being picked by the shopper who never walks the aisle. 4 - Then comes the part that hits your day-to-day role directly: execution is no longer the constraint. AI has made it dramatically easier to build, test, and ship ideas at a pace that would have felt unrealistic just a few years ago. But that doesn’t make product development easier, it makes it noisier. When everything is possible, the real challenge becomes deciding what is worth doing. The bottleneck shifts from “can we build this?” to “should we build this?” And that’s where judgment, taste, and prioritization stop being soft skills and become the core of your leverage as a product leader. 5 - The biggest trap I see companies falling into is treating AI as a tool adoption problem instead of an operating system shift. The companies that win won’t just use AI better, they’ll redesign how work flows, how decisions are made, and how their systems expose information to both humans and agents. Exactly what we train companies to do at Product Faculty. Link in comments.
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Satyajeet Salgar shared thisI’m delighted my first episode hosting Product Faculty's AI CXO podcast was this one! As we move to an agent-mediated world, shopping is evolving dramatically and CXOs need to urgently start preparing for this world - a world where their customers co-exist with agents. I had a bunch of questions including: - Who owns the customer relationship when an AI agent is the one checking out? - How much does brand matter in a world where an LLM decides what to buy? - What happens to checkout, discovery, and CAC when the buyer isn't human? I sat down with Mani Fazeli, Shopify's VP of Product, who shared his playbook and framing to help make sense of this. He’s incredibly insightful! You can watch the full conversation here: https://lnkd.in/ehWAbzp8 and wherever else you like your podcasts. :-) Here are some takeaways you can share with your team: 1. Audit your product data through an agent's eyes. Open ChatGPT or Claude and run two tests. First, ask "what should I buy in X" for your industry and see if your brand even shows up in the list. Second, ask specific questions about your products, variants, return policies, and shipping rules and see if the answers are accurate. If you fail the first test, you're invisible. If you fail the second, you're unreliable. Either way, your catalog isn't ready for agentic commerce. Fix that before anything else. 2. Treat your FAQ and policy pages as an API, not marketing copy. Agents pull from structured, authoritative sources. If your return policy lives inside a hero image or a PDF, it's invisible. Rewrite it as clean, machine-readable text with clear Q&A pairs. This is an opportunity. 3. Defend your checkout. When agents offer to "handle the transaction" for you, the right answer is to let them bring the buyer to your checkout, not to hand over the payment relationship. Stay the merchant of record. That's where your business logic, margins, and customer data actually live. 4. Brand loyalty might actually matter more in this agentic world and it’s possible for new brands to emerge more easily. 5. Start running simulated tests before real ones. Shopify just launched SimGym for this exact reason. Whatever your stack, AI buyer personas can now pressure-test your storefront, copy, and funnel before you spend real traffic on it. Most teams still aren't doing this. Sellers who figure this out in the next 18 months won't just move faster. They'll be operating in a different category entirely.
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisThe absolute best week of the year! It's a wrap on three incredible days with the Bread and Butter Ventures family. We hosted our AGM and our Founders Summit at the beautiful Target Field, home of the Minnesota Twins. An amazing gathering of learning, connection, and community. We brought together our Founders, LPs, and Corporate Partners to celebrate the Bread & Butter family. We had sessions on AI, sales strategy, fundraising, M&A, storytelling, term sheets. We had peer coaching and time to reflect on leadership. And we broke lots of 🥖 (and 🧈 ) together. A special highlight was Dug Song and Skylar Bennethum visiting from Michigan and Dug sharing his insights on his founder journey and the story of building Duo Security, Michigan's first unicorn and first multi-billion dollar exit. Thank you so much to our sponsors and partners who made this possible: J.P. Morgan, Koley-Jessen, Augeo, Target, the Minnesota Twins, 10x Management and Ramp. My heart is filled with gratitude for this special community and the bright future ahead. I'm thankful to be on this journey with Brett Brohl, Stephanie Rich, Anna Willgohs (special thank you to Steph and Anna for their enormous hard work in pulling these together!). To the Bread & Butter family! 🎉
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisGreat to see Entravision (NYSE: EVC) back in growth mode. Happy to see the focus on their Ad Tech & Services businesses (which is why I’ve been spending time in their headquarters in Barcelona!) paying off. Kudos to Michael Christenson, Mark Boelke, Jordi de los Pinos, Emre ATALAY 🔜 MAU Vegas, Mauricio Arnal, and the rest of the full team for all the work that led to this point. More details on the latest results: https://lnkd.in/g4CNj6wZEntravision (EVC) jumps nearly 90% after blowout Q1 results driven by ad-tech surge | EVC Stock NewsEntravision (EVC) jumps nearly 90% after blowout Q1 results driven by ad-tech surge | EVC Stock News
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisAwesome opportunity alert!!!! Be the head of machine learning @ CNN! And work w/ the awesome Chris Wiggins (one of the best) https://lnkd.in/gG6KANXsDirector, Machine Learning Engineering, CNN Digital Products & ServicesDirector, Machine Learning Engineering, CNN Digital Products & Services
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisCouple days away until Beats & Build. I've been thinking about what I want to build. A few ideas bouncing around, might try to automate the spec review workflow that's been bugging me for months. That's the beauty of these hackathons. You finally have the excuse to build the thing you've been sketching on napkins. What would you build if you had 3 hours, a team, and zero meetings? Also, we have an announcement to make about our judges! Mandar Parikh has spent 15+ years in classic enterprise SaaS product management — at Salesforce, Workday, and Zuora — building products like Zuora's Commerce platform, Salesforce's Revenue Cloud, scaling CPQ, and launching AI-driven Revenue Intelligence. At Turing, he heads Forward Deployed Research for Agentic Coding, partnering with frontier labs to build post-training data, eval systems, and RL environments. Mark Liu investor at UpScaleX, focused on early applied AI and agentic systems; Ex-software PE investor May 10, Palo Alto: https://luma.com/pb0079hn Second Axis PMs in AI
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisClosed out my first Team '26 watching Mike Cannon-Brookes (Atlassian co-founder/CEO) interview Alexis Ohanian Sr. (Reddit, Inc. founder). They covered a wide range of topics. A few interesting tidbits I learned: 🔹 Alexis started his founder journey by walking out of the LSAT exam 🔹 Alexis was an early supporter of women's sports (shout out to Nadia Rawlinson, another early advocate) 🔹 Alexis asks founders to send a Loom in advance of a meeting so that the sync time can focus on discussion 🔹 Mike wishes he could listen to people at 2x speed during meetings 🔹 The human touch will become more important and appreciated Amazing conference. Thanks everyone for coming and spending the time with us.
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Satyajeet Salgar liked thisSatyajeet Salgar liked thisWe just announced Meridian GeoX on the road to Google Marketing Live! 🎉 If you're looking for a better way to measure causal marketing impact, Meridian GeoX is our brand-new, open-source geo-based incrementality solution that's coming soon. On a personal note, this has been an incredible multi-month journey and I couldn't be more excited for what our team is building. Dig into the early details here: 👉 https://lnkd.in/gagcF_62 👉 https://lnkd.in/gnjpguR6Turn your data into decisions: 3 things your business needs for growth in the AI eraTurn your data into decisions: 3 things your business needs for growth in the AI era
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Satyajeet Salgar liked thistl;dr – I’m honored to see this in print, and as we delegate increasingly high-level tasks to AI, understanding the models is more relevant than ever Frankly, I'm overwhelmed seeing this in print at JAR and am deeply grateful to all those who encouraged me to write it, guided me along the way, and stress-tested the inferences. In the paper, I show that much of the superior performance of AI models can be attributed to artifacts of the modeling itself—rather than to mechanisms grounded in economics—and should therefore be applied judiciously. The response to this paper from across industry and academic fields has been more engaging and intellectually curious than I could ever have expected. Written in Sharpie on the whiteboard in my office are the words “Be Useful.” When I wrote this paper, I wanted it to be constructive, not merely cautionary. To do that, I included a detailed but approachable introduction to the fundamental mechanisms behind LLMs and transformers. While we have been using this at Booth for two course cycles, I am now hearing from external folks about how they used this in their own courses and PhD seminars. This is exactly what “Be Useful” means to me, and I hope it continues to be constructive—although your favorite neighborhood LLM is probably a better learning tool now! As I reflect on roughly 18 months of conversations with academics and practitioners, I hope that the paper will serve less as a stop sign and more as a roadmap. The lesson is not that we should avoid LLMs. It is that we need to understand how the models work, what they know, where they fail, and what this means for the inferences we can draw when using them. In this moment—as we increasingly delegate high-level tasks to the models and distance ourselves from the low-level work—I believe it is more relevant than ever.Satyajeet Salgar liked thisEarly View Alert - Caution Ahead: Numerical Reasoning and Look-Ahead Bias in AI Models by Bradford Levy https://lnkd.in/gB4bvuj7Caution Ahead: Numerical Reasoning and Look‐Ahead Bias in AI ModelsCaution Ahead: Numerical Reasoning and Look‐Ahead Bias in AI Models
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Steering Committee Member, Kilts Center for Marketing
The University of Chicago Booth School of Business
- Present 2 years 2 months
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Center for Applied AI’s (CAAI) Advisory Council Member
The University of Chicago Booth School of Business
- Present 1 year 7 months
Science and Technology
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Konkani
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English
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Marathi
Limited working proficiency
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Swahili
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OpenAI's $6.5B Acquisition of io: A Landmark Acquihire? OpenAI's recent acquisition of io, the hardware startup founded by former Apple design chief Jony Ive, for $6.5 billion, is huge news. With a team of just 55 employees and no commercial products to date, should this move be viewed as the most significant "acquihire" in history? What are your thoughts on this acquisition? Could this be the most significant acquihire in tech history? How do you see this influencing the future of AI-integrated hardware? #OpenAI #JonyIve #Acquihire #AIHardware #Innovation
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Jeremiah Owyang
Why does the SF Bay dominate the tech landscape? There's a network effect, like a gravity well that creates compounding benefits from those that are within the region. Here's one of my slides from my TEDAI San Francisco speech on why community and culture play a key role in the AI ecosystem development in our region.
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Mario Ribera
We hosted Bret Taylor (Chair of OpenAI and Co‑Founder of Sierra) in San Francisco for a live taping of The Verge’s Decoder with Alex Heath. I had the privilege of co‑moderating a closed‑door roundtable on agentic AI and the future of enterprise software with industry leaders and investors. The discussion was candid on what’s working, what’s stalling AI at scale, and what leaders do next. Key themes: – AI tech is ready; organizations aren’t. Trust, governance, and culture are the real unlocks. – Agentic AI is exploding in internal use. In revenue‑critical customer workflows, leaders are far more cautious. – SaaS pricing models are under real pressure as AI reshapes value; buyers want more control, not surprises. – Auditability, transparency, and human‑in‑the‑loop design are non‑negotiable for agentic trust. – Executives and teams must become AI orchestrators. Upskilling is now a leadership responsibility. The Decoder conversation with Bret and Alex is live here: https://lnkd.in/gNjWuutD Grateful to Bret Taylor, Alex Heath, OpenAI, Sierra, and our AlixPartners team for making it happen.
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Brett Ungashick
Netchex CEO Abhinav Agrawal's first stop in tech was at Zynga, where he led product for FarmVille and Words with Friends. It might sound like a circuitous path to HR tech, but the core lessons translate surprisingly well. Zynga was obsessed with data, analytics, and constant iteration. Products shipped live, updated daily, and evolved based on how people actually used them. The goal was creating experiences people wanted to come back to, which now informs how Abhinav is building the Netchex platform. One other benefit of the Zynga experience: Zynga was the case study company for successfully riding the early wave of social and mobile. As we navigate the next major technology transformation with AI, that experience of thriving through paradigm shifts is a real asset.
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Alex Salkever
My thoughts on the Great Waymo Outage and what it means for human acceptance of AI/technology. Two quick hits. Waymo needs a more graceful failure mode. Just pulling to the roadside might work in a normal power outage but not in, say, a wildfire or in the aftermath of a quake when people need to get out of harm's way. People also need to reconcile that the big, scary outages are easy to fixate on but mask the overall higher levels of safety and reliability we will be seeing from mature (note the word) AI systems. https://lnkd.in/gwnUA_hJ
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Haroon Choudery
Elie Schoppik has spent his career at the intersection of education and engineering. He built early ed-tech tools that helped people actually retain knowledge, founded a Bay Area coding bootcamp that trained thousands of developers through practice and feedback, and now leads Technical Education at Anthropic. Despite all that experience, Elie says most teams are using AI wrong: they’re treating it like an all-knowing oracle instead of a tutor. This is why Elie designs prompts, examples, and workflows that treat AI as a teacher, instead of a shortcut. In our conversation, we discuss: • Why relying on AI for “fast answers” erodes judgment and verification • How to turn prompts into guided lessons • Why context engineering is the most underrated skill in 2025 • Practical exercises any team can run tomorrow to raise AI fluency • What leaders should steal from Anthropic’s approach to technical education Episode is live now — full conversation linked in the comments.
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Ryan Green
Monthly earnings is up in SF—while every other AV city saw drops. What do you think happened? San Francisco was one of the earliest AV markets—Waymo went fully commercial there in June 2024. Yet despite a drop in ride density and pay per unit, monthly earnings increased. San Francisco stands apart from every other AV-active market in our dataset. While driver pay fell in Austin, Phoenix, and LA—monthly earnings in SF increased, even as trips per hour, utilization, and unit pay all declined. That raises questions. Are fewer drivers staying active—and working longer hours? Is platform supply adjusting to early AV rollout? Or is demand shifting in ways that concentrate earnings? Our AV Impact Report compares these city-level trends to the national baseline—and helps clarify where AV activity might be playing a role. 📊 Austin, SF, LA, Phoenix 📉 Year-over-year changes in pay, volume, and utilization 📍 Local vs national baselines Download the report for a deeper look into the insights: https://lnkd.in/drHnjtCS
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