#75 Why Consumer AI Browsers Were Doomed

Guest IntroductionI'm joined by Iuliia Petryshyn Thuen, Co-Founder & COO at Cloudgeni. She comes from McKinsey, and she sells deeply technical cloud infrastructure to enterprises without being the engineer.Episode SummaryWhen every competitor's agents copy your best idea within days, the product stops being the moat. This conversation looks at what still sets a small AI infrastructure startup apart, and why that answer is surprisingly human.Key Points🔥 The only differentiation that agents cannot copy overnight🤖 Why a 5-person team stopped reading its own code for nine months💡 The cheap first sale that came before a single line of code🔓 Why a startup gave away the part that took them a year to build💸 The token bill that every founder should plan for now✍️ What went wrong when the company let AI write its blog🧭 The hiring problem that is harder than finding engineersImportant LinksYou can reach Iuliia Petryshyn Thuen at https://www.linkedin.com/in/iuliiapetryshyn/Vishesh, your host at https://vishesh.spaceCheckout Cloudgeni at https://cloudgeni.aiHands hurt while you use the keyboard or mouse? https://tryneo.devChapters0:00 - Introduction1:07 - The grandma pitch for autonomous cloud management1:42 - From "AI DevOps engineer" to 10X-ing your engineers4:52 - Why AI-written blogs stopped working for a startup8:13 - Writing by hand again: the Substack approach9:40 - How to differentiate when competitors' agents copy you12:28 - Two million lines of code that nobody read15:50 - Why Cloudgeni open-sourced OpenGeni18:21 - Avoiding lock-in to big-lab token bills24:02 - Why the technical co-founder is CEO24:12 - Selling from day one: the $500 first customer30:25 - From technical compliance to the full DevOps job32:35 - The biggest challenge: product-market fit with few customers35:07 - Hiring: why go-to-market is harder than engineering37:35 - Try OpenGeni and send honest feedback38:50 - Where to follow her and the Product Hunt launch

# The Tricks That Hook Gamblers Can Fix Health## Guest IntroductionI'm joined by Olli Rundgren, CEO & Founder at Psyon Games. A former professional poker player, he now builds health games with pharma partners and the WHO, and he runs Rally Pitch, where founders pitch from a rally car at full speed.## Episode SummaryThe loop that keeps a gambler at the poker table is the same loop that can get people to change a health habit, if it serves the player instead of the house. The hard part is not the game design: it is getting a risk-averse industry to pay for prevention before the evidence exists.## Key Points- 🃏 What climbing from penny stakes to high-stakes poker taught about engagement, and where it turns dark- 🧪 Why a pharma buyer wants evidence before you can even run the pilot that produces it- 🎮 Why points, streaks and badges are not a game, and the fun-first rule that is- 💍 What Oura gets right, and the missing step between knowing what to do and doing it- 💶 Why €1 spent on prevention can save €8, yet almost nobody funds it- 🏎️ How a hometown rally weekend became a pitch format now scaling to 15 countries- 🤝 The one recruiting question to ask before anyone joins without a salary## Important Links- You can reach Olli Rundgren at https://www.linkedin.com/in/olli-rundgren-60238267/- Vishesh, your host at https://vishesh.space- Checkout Psyon Games at https://psyongames.com- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:49 - From a broken leg to high-stakes online poker2:20 - Seeing the dark side of gambling up close3:41 - Funding a science degree from the poker tables6:38 - Poker sweat: how intense games change your hormones8:28 - What the poker table teaches founders9:37 - The first game that failed, and the vaccine game that worked13:06 - Why Finland punches above its weight in games14:25 - Selling innovation to a risk-averse pharma industry15:34 - A COVID game with the World Health Organization16:34 - Fun first: designing the ADHD game20:09 - Building with pharma, patients and psychiatrists21:02 - Gamification vs. full games: what founders get wrong22:31 - What Oura gets right about health data25:42 - Awareness, habits and prevention29:01 - Why nobody pays to keep people healthy30:33 - How Rally Pitch started33:06 - Pitching at 180 km/h next to a world champion36:37 - The hard part: momentum when you start from scratch38:05 - Recruit for their life goals, not your needs40:02 - A founder's AI stack and a games + AI patentTopic: Gamification in Healthcare

## Guest IntroductionI'm joined by Charles Maddock, Co-Founder and CEO at Strawberry. His team raised $6 million from EQT Ventures and General Catalyst to build a browser where AI companions work like colleagues. We get into why he picked the browser, not a cloud virtual machine or a desktop app, as their home.## Episode SummaryMost AI agents work from a cloud virtual machine or your local files, far from where most people do their work. The bet here is the browser, where the tabs, logins and context already live. This conversation covers what that choice changes about cost, trust and who gets to use agents at work.## Key Points- 🌐 Why an agent that controls one tab beats an agent that controls your whole computer- 🍓 OpenAI and Perplexity both built AI browsers. Why does he say they aimed at the wrong buyer?- 💸 A $50 agent run against a $100,000 contract: the math behind what web agents cost- ✋ Ten messages to approve instead of a hundred sent: the default that keeps an agent from sending without a yes- 🧑‍💼 One teammate runs 100 agents at a time. What does an entry-level job look like now?- 🔄 Two weeks of flaky routines and thousands of manual apologies: what one cloud migration cost- 🧪 The hiring test that works because it is a little mean## Important Links- You can reach Charles Maddock at https://www.linkedin.com/in/charles-maddock-31798418b- Vishesh, your host at https://vishesh.space- Checkout Strawberry at https://strawberrybrowser.com- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:30 - The pitch: AI colleagues that live in your browser tabs5:35 - Why the big AI browsers went consumer, and where Strawberry goes instead9:46 - What a web agent costs to run, and the pricing tiers11:11 - Strawberry versus Grokbot, Dots, Claude and Codex13:52 - Building a custom companion for your own workflow14:54 - Where teams use it most: sales, talent, and founder inboxes18:23 - The viral beta video and the first customers19:37 - Biggest win: hiring Stockholm talent over San Francisco talent22:56 - Keeping message quality steady across different models23:32 - Why Strawberry asks before it sends anything24:43 - Going broad: why a general platform beats a niche tool27:13 - The hard part: getting people to actually use AI29:21 - How to hire the best team, and the low-ego work trial31:51 - Local models versus cloud, and the new agent architecture33:38 - Where to find Strawberry and CharlesTopic: AI Agents for Business

# Hong Kong vs Shenzhen vs Singapore for Hardware Founders## Guest IntroductionI'm joined by Charudatta Datar, CEO & Co-founder at Astra Optics, a CUHK spin-off that builds ultra-fast nanoscale 3D printers. An MIT-trained mechanical engineer, he left Boston to turn a professor's Science-published breakthrough into a company across Hong Kong, Shenzhen and Hefei.## Episode SummaryFor years, nanoscale 3D printing sold almost only to university labs, because it was too slow for any factory. Now AI data centers are moving from copper to light, and that move needs hair-width lenses that a fast nanoscale printer can prototype in hours. This episode shows how a professor's lab invention becomes an industrial company, and why the money and the factories for that journey are in mainland China.## Key Points- 🔦 Why AI data centers suddenly need lenses as wide as a human hair- ⚡ The one bet that separates this startup from competitors who perfect materials- 💰 The seed offer from Singapore that was 5-6x lower than the offer from mainland China- 🏛️ How Chinese city governments compete to win deep-tech startups- 🎓 Why mainland investors back the professor, not just the startup- 🏭 Hong Kong vs Shenzhen for hardware: where to put the HQ and where to build- 📦 The sourcing rule from Yiwu: find demand first, supply in China follows## Important Links- You can reach Charudatta Datar at https://www.linkedin.com/in/charudatta-datar-667808a/- Vishesh, your host at https://vishesh.space- Checkout Astra Optics at https://astra-optics.com- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction1:05 - From MIT to a professor's nanofabrication lab in Hong Kong4:54 - Why professors make a PhD student the CEO of their spin-off7:23 - Why the headquarters moved from Hong Kong to Hefei8:49 - RAISe+ and Hong Kong's professor-led ecosystem10:01 - How two-photon lithography prints a 3D pixel13:49 - The bet on speed over materials15:19 - Co-packaged optics: why AI data centers are moving from copper to light19:49 - Why prototyping, not mass production, is the first market21:30 - Selling to universities first, then to industry22:25 - Funding: the hardest problem in deep tech23:49 - Government grants in Hong Kong, Hefei and the EU25:30 - Why mainland VCs invest in the professor27:50 - Hong Kong vs Shenzhen vs mainland China for hardware30:19 - The Singapore offer that was 5-6x lower32:19 - How China's local governments compete for startups33:49 - An Indian CEO in China: Shenzhen, the Greater Bay Area and high-speed rail36:42 - Language and culture as a foreign founder38:06 - The pre-Series A round and the pitch to Western investors39:51 - Why hardware moves faster in mainland China41:19 - The biggest unlock: narrowing to a few industry use cases43:48 - Marketing on WeChat vs LinkedIn47:08 - How to source and sell in ChinaTopic: Deep Tech Fundraising in China

# How to Fill a Room With 2,000 People on Zero Budget## Guest IntroductionI'm joined by Vasily Alekseenko, Founder of Rare Founders. He started Rare Founders when his own fundraise stalled, has welcomed more than 25,000 founders and investors through its doors, and is now building SoPhy to help people keep the relationships that matter.## Episode SummaryLondon has 200+ startup events in a single ten-day window, and many of them promise investors who never show up. This episode is a playbook for filling a room with zero budget: free venues earned through partnerships, 60-second pitches, and formats built so nobody gets stuck in a dead conversation. Then it goes further: why 1,000 real relationships beat 13,000 LinkedIn connections, and what it takes to measure the difference.## Key Points- 🍺 How a zero-budget founder booked a free venue for 250 people in two minutes- 💦 Why founders get 60 seconds on stage, and a water gun if they go over- 🚪 The format trick that lets you leave any awkward networking conversation- 💸 Why family offices avoid founders, and the event that got them into the room anyway- 🤝 The partnership rule that gets venues, catering and speakers without paying- 📉 Why a big LinkedIn network can hurt you when you ask for an intro- 🤖 How AI raised user expectations so high that a manual MVP no longer works## Important Links- You can reach Vasily Alekseenko at https://www.linkedin.com/in/alekseenkovv/- Vishesh, your host at https://vishesh.space- Check out Rare Founders at https://www.rarefounders.com- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:58 - What Rare Founders is: 25,000+ founders and investors in two and a half years1:47 - The failed fundraise that started it all3:28 - Why paid pitch slots with no investors in the room felt like a scam4:22 - The pub with a no-show organiser and a free venue for 250 people6:37 - Going full-time and launching London's Largest Demo Day8:52 - Why events are hard to monetise, and the 40+ media partners behind them11:35 - Inside open mic pitching: one minute, raised hands and water guns14:57 - Breaks that let you exit an awkward conversation16:28 - When a volunteer copied the whole format17:28 - What San Francisco events get wrong, and right18:23 - Why UK investors avoid founder events23:49 - Family offices vs VCs: who wants to meet whom25:36 - Moving from open mics to technical and sector events28:42 - How the events business pays the bills, and why it is not the moonshot30:52 - The channels that drive traffic: partnerships, content and word of mouth34:08 - The pub lesson: what a real partnership looks like38:23 - The LinkedIn problem: 13,000 connections, 95% strangers40:57 - Social physics: why well-connected founders reach Series B43:07 - SoPhy's AI agent for your inbox, calendar and WhatsApp49:00 - Can you measure trust between two people?55:32 - Where SoPhy is now, and why launch is limited57:14 - SoPhy vs Boardy and Instinct59:31 - How AI raised user expectations and killed the manual MVP1:03:47 - Using Rare Founders events as SoPhy's go-to-market1:05:14 - SoPhy's one-minute pitchTopic: Startup Community Building

# AI's Real Bottleneck Is Not Chips. It Is the 100-Year-Old Grid.## Guest IntroductionI'm joined by Vikhyat Chaudhry, Co-Founder at Buzz Solutions. He's Forbes 30 Under 30 in Energy and just published a book on why the power grid, not chips, is AI's real bottleneck.## Episode SummaryChips get blamed for every AI bottleneck, but the real constraint is a 100-year-old power grid that takes seven to nine years to build a single new transmission line. This episode breaks down why AI data centers now behave like spiky, unpredictable loads utilities can't model ahead of time, and why a 90% accurate AI model is still only 20% of the job of actually fixing the grid.## Key Points- 🔥 Why AI's real bottleneck has nothing to do with chips- ⚡ Why AI data centers made the grid's load growth jump nearly 6x almost overnight- 🌡️ What a thermal camera on a drone can catch before a transformer blows up- 🏆 How a startup beat GE, Siemens, and Schneider Electric by a 58-point accuracy gap- 🤖 The term he coined to fix AI's biggest misconception in the power industry- 💻 Why "Claude generated it" is not an excuse for bad code in production- 📖 What a college drone project measuring Delhi's air quality has to do with any of this## Important Links- You can reach Vikhyat Chaudhry at https://www.linkedin.com/in/vikhyatchaudhry/- Vishesh, your host at https://vishesh.space- Checkout Buzz Solutions at https://www.buzzsolutions.com- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:13 - What Buzz Solutions does for power utilities2:21 - Why Buzz stays software, not drone hardware3:18 - Writing "Powering Intelligence"5:50 - Gigawatt-scale load swings from AI data centers8:40 - Where new data centers are popping up across the US10:01 - What thermal imaging catches before a transformer fails11:14 - Inside the PowerAI multimodal data platform13:23 - Why there's no public dataset for critical infrastructure AI15:15 - Landing the first utility dataset16:38 - The Delhi drone project that measured an AQI over 50017:36 - Beating GE and Siemens for New York Power Authority19:15 - What makes a utility RFP so grueling21:37 - Finding an RFP partner through Google22:19 - Building a partner program that actually works23:38 - The biggest misconception about AI model accuracy25:54 - Rules for AI-generated code on the engineering team27:33 - Scaling code review with CodeRabbit31:45 - Buzz Solutions is hiring, and where to find the bookTopic: AI Grid Inspection

# What This CTO Checks Before He Merges AI Code## Guest IntroductionI'm joined by Michael Bromley, CTO & Co-Founder at Vendure. He started Vendure as a solo open-source project in 2018, and today he no longer writes code: he runs parallel AI agents and a review agent called Nigel.## Episode SummaryCode is no longer scarce, so an engineer's value moves to review, architecture and taste. This episode goes inside a real agent workflow: an orchestrator session that splits Linear issues into parallel lanes, agents that coordinate over Slack, and a one-file review agent that beats a paid tool. It also asks what that shift does to open-source licensing and to the junior engineer's career path.## Key Points- 🤖 Why a single markdown file in a GitHub Action out-reviews CodeRabbit- 🧵 How two engineers stop their agents from colliding: let the agents talk to each other on Slack- 📜 The one thing worth writing in your AGENTS.md (and why most of it is wasted context)- ⚖️ Why an MIT-to-GPLv3 license change made sense in 2024 and makes less sense now- 🎁 The paid feature Vendure gave away for free, and why it is too late to take it back- ✍️ Why a CTO who lets AI write all his code still refuses to let it write his posts- 🎓 What a junior engineer should do when AI does every junior task better## Important Links- You can reach Michael Bromley at https://www.linkedin.com/in/michael-bromley-ab1704156- Vishesh, your host at https://vishesh.space- Checkout Vendure at https://vendure.io- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction1:33 - What Vendure does for B2B commerce2:13 - From a family art-supply shop to a solo open-source project3:55 - Why Magento, Shopware and WooCommerce were not enough6:41 - From AI skeptic to fully AI-pilled8:22 - Should companies still hire junior engineers?10:38 - The skills that still matter when AI writes the code12:46 - What to put in AGENTS.md to make a codebase agent-ready14:47 - MIT to GPLv3: does licensing matter when code is not scarce?17:58 - How an open-source company makes money19:17 - Michael's dev setup: orchestrators, sub-agents and Linear21:38 - Agents that coordinate with each other over Slack23:20 - Nigel, the review agent that beats CodeRabbit24:58 - The newest change: Codex as the orchestrator28:32 - The channels that bring Vendure customers29:25 - The feature Vendure should have charged for30:52 - Superpower and weakness: taste vs. marketing33:43 - What works on LinkedIn for a developer35:33 - Where to find Michael and Vendure37:11 - Why he still writes his own postsTopic: AI Code Review

# Your AI Agent Does Not Need an API. It Needs a Login.## Guest IntroductionI'm joined by Furqan Kidwai, Co-Founder & CEO of Plouton AI, which builds AI finance co-workers for mid-market CFO teams. A Forbes 30 Under 30 honoree who started his career building credit-risk models at Deloitte, he explains why his agents log in instead of integrating.## Episode SummaryMid-market finance teams do not lack tools. They lose days copying data between them, because most of their ERPs and bank portals have no API. This conversation is about AI agents that log in like a person and finish 10 days of back-office work in an hour, and what it takes to sell automation where every click must be provable.## Key Points- 🔐 Why the API-first automation wave skipped most mid-market finance teams- 🧾 The audit trail that lets an AI agent touch the books without keeping the data- 🤖 How an AI agent gets past MFA prompts the same way a new hire does- 🔀 The outbound pivot that turned a would-be competitor into a sales channel- 🎙️ The webinar playbook that fills a CFO pipeline, and what has to come first- 📉 Why a fintech with 26,000 customers still had to shut down- 🧭 Services as a software: why this AI company will not call itself SaaS## Important Links- You can reach Furqan Kidwai at https://www.linkedin.com/in/furqankarimkidwai- Vishesh, your host at https://vishesh.space- Checkout Plouton AI at https://plouton.co- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:07 - From theatre and computer science to Deloitte credit-risk models1:33 - The grandma pitch: what Plouton AI actually does2:15 - Copilot vs co-executor: agents that finish the work4:47 - Why API-first tools miss mid-market finance teams6:33 - Browser agents in the cloud, like a remote employee7:44 - Handling MFA prompts and session timeouts8:58 - An audit trail without data retention, plus on-prem10:09 - From a 100-person consultancy to services as a software11:22 - The channels that bring in CFO buyers11:50 - The webinar playbook: credibility comes first14:08 - YPay: 26,000 customers, a macro shift, and shutting down16:16 - His founder superpower: staying close to customers17:01 - The pivot to selling through fractional CFO firms18:09 - The founder lesson: stay positive, grow your surface area19:22 - The one product he can't live without20:26 - Where to find Furqan and Plouton AI

## Guest IntroductionI'm joined by Gregor Biljardt, CEO of Mondrio and a former Simon-Kucher pricing lead who spent roughly eight years pricing startups before building an agentic pricing platform. He's seen exactly where founders leave money on the table — and why the AI era just rewrote the rules.## Episode SummaryPricing is one of the highest-leverage levers a founder has, yet most set it once on gut and never touch it again. The bigger shift: AI has turned pricing from a static, annual exercise into a living system that has to move as fast as the product ships — and the seat-based models most teams default to can quietly erode your margins the moment agents start doing the work.## Key Points- 🔥 The one-sentence realization that made him walk away from an 8-year pricing career- 💡 Why founders keep undercharging — and the healthy sign that you're priced right- 🎯 The margin trap that punishes you for shipping better AI- 🚀 Why copying OpenAI's $20 price tag might just be copying someone's guess- 🧭 The pricing move most founders skip at Series A — and pay for later- 💰 What to always ask for before you hand a customer a discount- ⚙️ Who should actually own pricing when you're pre-seed to Series A## Important Links- You can reach Gregor Biljardt at https://www.linkedin.com/in/gregorbiljardt- Vishesh, your host at https://vishesh.space- Checkout Mondrio at https://mondrio.io- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:10 - Why he left 8 years of pricing consulting to build Mondrio1:46 - "You can't do pricing with a report": pricing as a living system2:45 - Pricing moves that matter at pre-seed, seed, and Series A4:46 - Why founders undercharge and leave money on the table8:45 - Copying competitor pricing: the $20/$100/$200 trap11:09 - When is it too early to obsess over pricing?13:30 - How to price AI when agents, not seats, do the work15:15 - Outcome-based pricing: when it actually works16:42 - Why replacing seats with AI can cut your own revenue18:33 - Usage-based pricing and the predictability objection21:22 - Fair-use policies and the million-dollar-bill churn24:34 - Pricing is cross-functional: engineering vs sales blind spots30:41 - Discounting: always ask something back33:43 - The hidden work behind "simple" pricing35:29 - Who owns pricing from pre-seed to Series A37:44 - Changing pricing often without confusing customers42:41 - Mondrio's challenges: mindsets, services, and hiring49:06 - Roles Mondrio is hiring for in SF and Amsterdam50:32 - Book recommendation and where to find Gregor

# How to Land Press With Zero Journalist Connections## Guest IntroductionI'm joined by Kathy Wang, Co-Founder at Press Club. She's an ex-Dropbox designer who built a 10,000-person audience before she had a product, and now lands founders in Forbes, TechCrunch, and Axios entirely through cold outreach, without knowing a single journalist.## Episode SummaryLanding top-tier press has nothing to do with knowing journalists. It's a repeatable cold-outreach system that surfaces the ~200 reporters most likely to say yes and pitches each one a story worth their time. The real multiplier underneath it is distribution you build in public before you ever have a product, plus the discipline to post what inspires and educates instead of streaming your struggles.## Key Points- 🔥 She quit her job a week after one conference talk, with no idea, no co-founder, and nothing lined up- 💡 The 'classic' build-in-public advice that quietly kills your engagement on LinkedIn- 🎯 Why an AI-written cold pitch out-converts the same email after a founder edits it by hand- 🚀 The cold-outreach response rate everyone accepts as normal, and the number her AI agent hits instead- 🧭 Forbes bought him credibility; a niche outlet got him 10x the revenue. Which one you actually need- 📰 A six-day pitch-to-published campaign, and the three boxes you must check to be newsworthy- 🎤 The reason most founders can't get press: they have no idea what's actually interesting about them## Important Links- You can reach Kathy Wang at https://www.linkedin.com/in/kathytwang/- Vishesh, your host at https://vishesh.space- Checkout Press Club at https://pressclub.app/- Hands hurt while you use the keyboard or mouse? https://tryneo.dev## Chapters0:00 - Introduction0:39 - The Press Club pitch: top-tier press with zero journalist connections1:29 - Quitting her job a week after one conference talk3:25 - Six months of daily content before she had an idea4:30 - The hackathon that became Press Club (and a co-founder) in a day6:23 - Does posting every day burn out your audience?7:00 - Top-of-funnel vs bottom-of-funnel content, done with intent11:41 - The build-in-public lesson most founders get backwards15:32 - Building in public vs performing for the algorithm19:50 - Events as Press Club's primary customer-acquisition channel23:19 - Inside the cold-outreach engine: finding the right 200 journalists26:26 - Why AI-written pitches beat hand-edited ones, and the 10% response rate29:09 - A six-day pitch-to-published Axios campaign30:38 - The three criteria that make a founder newsworthy31:14 - When press changes the trajectory of a deal33:36 - Credibility vs customer acquisition: tier-one vs niche outlets35:52 - Manufacturing a newsworthy moment as a pre-seed founder37:38 - The belief about building in public she flipped38:42 - Her secret to never running out of content ideas41:18 - The one thing to do after this episode43:51 - Running a 30-day LinkedIn challenge across the team45:52 - Where to find Kathy