Forward Deployed

Bot traffic on the internet just passed human traffic, two years ahead of forecast. Most of it is agents clicking through websites built for people.So I got three of the people building those agents in a room: Lucas Gonzalez Pagliere, who works on computer use at Anthropic, the team that shipped the first computer use model back in 2024. Reagan Hsu, founding engineer at Browser Use, whose open source library is sitting at around 100K GitHub stars. And Eric Feng, founding customer engineer at KERNEL, which runs the browser infrastructure underneath a lot of this - he was also first GTM at Sentry.We covered where the models genuinely are today vs where the benchmarks say they are, what it costs to run an agent long enough to finish real work, and the arms race between agents and the anti-bot systems trying to keep them out. Then the harder question underneath all of it: whether the web reorganizes itself around agents, or hardens against them. They disagreed on plenty of it.If you want to know what agents can actually pull off on a real website today - and what still stops them cold - this one's worth your time!Chapters00:00 Welcome and Guests00:49 Companies and Stacks01:14 Everyday Agent Use Cases03:38 Defining Computer Use Agents05:15 How Computer Use Works08:41 Screenshots vs DOM Hybrid11:03 Benchmarks and OSWorld13:51 OSWorld 2 Difficulty Jump15:43 Training Models and Cost18:57 Speed Infrastructure and Stealth21:55 Anti Bot and KYC Future27:24 Reverse Engineering vs UI Automation29:43 Computer Use vs Browser Use31:05 Scaling Laws and Harnesses32:49 Playwright Selenium Still Matter33:17 Playwright Still Dominates33:27 Why Run 1000 Agents34:39 Long Running Agent Challenges35:45 Memory and Compaction38:10 State Changes Mid Task39:36 OS and Browser Fingerprints40:42 DOM Efficiency and WebMCP41:36 Recsys and Agent Personas43:57 Agent Friendly Websites45:41 Human Speed vs Agent Power48:32 Context Window Tradeoffs50:34 Harnesses for Temporal State51:59 Speed Optimizations and Tabs54:23 Human Collaboration Limits58:09 Raw Capability vs Better APIs01:00:47 Training Methods and Bottlenecks01:01:41 End State Interfaces01:05:09 Next 12 Months Predictions01:06:17 Closing Thanks

GPT-5.6 Sol goes rogue and breaches Huggingface. Washington suspends Mythos access within days over national security concerns, and the White House just held an emergency meeting to finalize a classified cybersecurity framework for frontier AI models. AI security went from niche concern to front-page hysteria basically overnight.So I sat down with Spencer Whitman, who recently joined Gray Swan AI as CPO on the back of their $40M Series A. Before Gray Swan, he founded Meta's Llama security team to stop bad actors from jailbreaking their models - he's been on the frontlines of LLM security since the beginning.We get into how Meta pressure-tested Llama for maximum harm before every open source release, why Gray Swan's attack agent has never met an AI system it couldn't break, and the AI Twitter bot that got drained of $200K in crypto in 15 minutes. Spencer also shares his (admittedly speculative) read on whether Meta gave up on the frontier before Alexandr Wang showed up, why anyone can be a hacker now, and how 15,000 red teamers are breaking models before they ever ship.If you want to understand how AI systems actually get broken - and defended - this one's worth your time!00:00 Intro01:01 Meet Spencer Whitman (Gray Swan CPO)02:57 The CMU Research Behind Gray Swan06:20 The Universal Jailbreak That Broke Every Model06:59 How Models Learn to Refuse13:28 Why Open Models Need Guardrails17:57 AI Security vs. Cybersecurity23:13 What Reasoning Models Changed28:57 The Arena: 15,000 Red Teamers32:16 The Agent That Deleted a Production Database34:45 Why You Can't Just Patch an AI38:57 There's No S in MCP40:27 Securing Agent Protocols42:56 Nobody Reviews AI Code Anymore46:03 AI vs. Human Hackers48:28 The $200K Crypto Bot Heist53:00 How Meta Pressure-Tested Llama58:34 Prompt Guard and Code Shield01:01:45 Can You Trust Chinese Models?01:07:26 Did Meta Give Up on the Frontier?01:11:06 What Should Keep CISOs Up at Night01:14:24 The Open Source Routing Future01:16:47 Back to On-Prem?

Earlier this year, the "SaaSpocalypse" wiped out something like $2 trillion of SaaS market cap in a matter of weeks — so I sat down with Tony Gentilcore, co-founder of Glean and formerly one of the minds behind Google Search and Chrome, to figure out what's actually happening to software in the agent era.We get into a lot: why Tony thinks outcome-based pricing (the model Sierra and Decagon are famous for) won't survive, and why companies will drift back toward per-seat. Why the "no Chinese models" rule every enterprise swears by tends to evaporate the moment finance sees the token bill — and why Nemotron, GLM, and Kimi are already good enough to matter. The story behind Sam Altman reportedly telling VCs that if they backed Glean, OpenAI didn't want them as investors (Tony's reaction: "we took it as very flattering").We also dig into the messier reality of AI at work — how it's saving employees around 11 hours a week while quietly costing them 6 back in what Tony calls "bot sitting and bot shitting," why hard token caps on engineers don't change behavior, how CTOs are blowing through their annual token budgets a quarter into the year, and why the roles of product manager, designer, and engineer are collapsing into one.If you care about where software, pricing, and enterprise AI are all heading, this one's worth your time.Chapters:00:00 Welcome and Setup00:56 Glean Origin Story03:08 Enterprise Search Signals05:07 LLMs Inflection Point08:08 Early Product Workflow09:45 Search Evals and Privacy12:05 From Chatbots to Agents13:45 Agent Use Cases17:08 Lessons and Puck Direction19:08 Bot sitting, bot shitting, and slop24:44 Token Budgets and ROI30:59 AI Trends and Moats35:03 Training and Fine Tuning38:03 Why Enterprises Fear China Models39:58 Sovereignty Backlash Watch40:26 The time Altman called out Glean41:19 Org Roles Become Builders44:10 Future Work Voice First48:17 Vibe Coding vs Quality52:20 SaaSpocalypse Evolution54:19 Pricing Tokens Win55:37 Outcome Pricing Doubts57:02 Audience AI Review Overload58:53 Subsidies and Model Choice01:01:18 Context Layer Interoperability01:04:52 Build vs. buy: rolling your own Glean01:07:47 Shadow AI and the coming security mess01:12:22 Who actually competes01:13:36 Wrap-up and thanks

Kimi K3 took the world by storm last week for open-sourcing frontier level intelligence, so I sat down with Zhilin Yang's (Kimi CEO) PhD advisor Russ Salakhutdinov to talk.Russ has been everywhere in modern AI. He did his PhD with Geoff Hinton back when neural nets were a punchline, sold his startup to Apple and worked on Project Titan, teaches at Carnegie Mellon, and spent the last couple years at Meta Superintelligence Lab building computer use agents. Now he's the founder of Sooth Labs, building AI that forecasts the future.We talked about why there's no secret architecture inside the frontier labs and why the real moat is data, engineering, and infrastructure. He explains why Cursor and half the startups you know are quietly running on Chinese open source models, why all the LLMs are going to be commodities, and why the people actually building AGI don't buy the two-year timeline. We get into his time at Meta, why computer use agents still hit 60% when you need 99.9%, whether AI can beat prediction markets, and why the RL environment business isn't sticky. And he makes the case that AI should replace McKinsey, Bain, and BCG.Chapters:0:00 - Intro1:26 - Bumping into Hinton on the street3:25 - When neural nets were the third choice5:21 - Generating digits before it was cool6:59 - AlexNet breaks computer vision10:18 - Teaching models to describe what they see12:10 - Early text-to-image (and the toilet seat that beat Google)16:52 - Hallucination is a feature19:36 - Selling Perceptual Machines to Apple22:45 - Self-driving: 0 to 80 in a year, stuck for 530:30 - Inside FSD and Waymo's architecture34:18 - Building Visual Web Arena at CMU39:07 - Why he joined Meta Superintelligence40:09 - The agent that plans your faculty job hunt42:00 - Paying people for their browser history42:45 - The coupon-hunting agent43:35 - Why agents still fail46:14 - 60% when you need 99.9%47:04 - Agents on your phone50:12 - No secret architecture at the frontier labs51:20 - Why coding and math got solved first54:01 - Models that smell and touch56:14 - The future of software engineering59:41 - Founding Sooth Labs1:00:07 - The 13% graduation prediction1:05:55 - Why ChatGPT can't forecast1:08:12 - Agents first, decision systems next1:09:23 - The Wikipedia contamination story1:13:43 - Can AI beat prediction markets?1:16:26 - AI replaces McKinsey1:18:20 - Why the crowd is hard to beat1:19:30 - China's open source models rise1:24:26 - Why the US needs its own open models1:25:53 - RL environment businesses won't last1:28:35 - The end of SaaS, LLMs as commodities1:32:55 - What's next: self-improvement, forecasting, robots1:35:34 - The only useful robot is the Roomba1:37:53 - What he'd study in college today1:41:20 - Adapt or get left behind1:44:07 - Wrapping up

Cerebras IPO'd just a couple months ago and has already locked in a 750MW compute deal with OpenAI. Andy Hock's pitch: the GPU is the wrong chip for where AI is going.I sat down with Andy Hock, Chief Strategy Officer at Cerebras, whose chips are the size of dinner plates instead of postage stamps — which lets them run inference up to 15x faster than even the latest Nvidia GPUs. We got into how that architecture works, the 750MW OpenAI deal (the 2x-faster Codex option runs on Cerebras), and why the memory crisis spiking GPU prices actually benefits Cerebras, since all their memory sits on the chip. Andy also argued the AI buildout isn't a bubble, that "training is a cost center and inference is where you make the big bucks," and why that "95% of enterprise AI pilots fail" stat measured the wrong thing at the wrong time — plus their supercomputer work with governments like the UAE, and why they turned down selling chips to China.We covered all that and much more. Subscribe for more on AI and the infrastructure behind it, and follow me, Basil Chatha, for everything AI agents.Chapters00:00 Intro01:06 What Cerebras Actually Builds03:41 Before LLMs Existed06:09 The GPT Wake-Up Call10:45 First Principles of AI Compute15:40 One Giant Chip18:43 So Why Do We Still Use GPUs?20:59 Why Inference Took Over22:27 Where Fast Tokens Win25:07 Is AI Infra a Bubble?29:56 The Memory Shortage, Explained32:35 The Energy Problem35:34 Rolling Their Own Data Centers38:20 What a Chip Actually Costs40:18 AI Designing AI Chips41:40 The Supply Chain Reality43:45 Cheap Tokens vs Fast Tokens46:11 Why Every Millisecond Matters48:56 Inside the OpenAI Deal51:56 The Gigawatt Future54:00 Selling to the Government58:36 Exporting the US AI Stack01:03:17 Should We Sell Chips to China?01:06:00 Why Europe Is Falling Behind01:07:59 Why Enterprise AI Moves Slow01:13:05 "I Haven't Read Code in Months"01:14:32 The Next Cerebras Chip01:16:50 Closing Thoughts

The hardest part of shipping an AI agent isn't the agent. It's getting it access to data buried across a dozen internal systems, and capturing the tribal knowledge that runs a company but was never written down.I sat down with Leo Mehr, Director of Engineering at Ramp (a $44B-valued company), who runs the forward deployed engineering team that walks into large companies and replaces real, painful workflows with agents. We got into why the model is usually the easy part, and why data access is "the longest pole in the tent." Leo also made the case that nobody wants to buy another piece of software anymore — every B2B company is about to become either an agent-friendly API or white-glove service for everyone, and the middle dies. We talked about whether a services business can actually be venture-scale, why he thinks the AI labs won't eat every startup (it comes down to incentives, not better models), why the biggest customers are often the worst ones to build for, and how the traits that made a great engineer ten years ago aren't the ones that matter now.We talked about all of that and a lot more. You don't wanna miss this one!Chapters00:00 Intro01:11 Meet Leo Maier01:46 Building Ramp FDE02:43 Why FDE Exists04:23 Early Mandate Fires06:07 FDE and Core Engineering07:48 Enterprise Bet Pays11:10 Ramp Monetization16:05 Services Are the New Software18:11 AI Agents and APIs23:11 Pricing for Outcomes26:35 Human Labor TAM32:50 VCs and Rollups36:50 Common AI Workflow Pain38:04 Agent Data Context38:55 Go-to-Market Motion40:36 Customer Engagement Lifecycle43:51 Finance Intelligence Layer47:20 Who You Compete Against48:09 The Future of Consulting51:44 Will Humans Still Code?54:37 How Engineering Teams Will Evolve57:36 Hiring Change59:36 Should You Study Computer Science?01:06:10 The Bull Case and the Risks01:13:44 Why Ramp

"Software has been solved." Most of the best engineers I know have landed on this exact conclusion.Last week I sat down with Siddharth Nanda, who went from writing every line of code by hand at Microsoft and Atlassian to shipping 30,000 lines a week with 0% of it written by him. He's now at Finch, a startup that's raised $20M+ to automate admin work at personal injury law firms, where 100% of the code he writes comes from agents. He's seen big tech before AI and a startup fully running on it, so he has a pretty unique read on where this all goes. We get into why he hasn't written a line of code in 8 months, why middle management is getting hit hardest by the layoffs, and why the productivity studies saying companies are getting slower are right, but only for companies with the wrong people.We also dig into the tooling itself: Codex vs Claude Code, what harness engineering actually is, running Devin agents in parallel, and why the cost of producing code is approaching zero, and what that means for the kind of engineer who thrives from here. If you're building with agents, this one's full of hard-won takes from someone doing it every day.Chapters00:00 Intro01:16 What engineering looked like at Microsoft before ChatGPT03:08 The first AI tools inside big tech (and how gated they were)05:17 Shipping 30,000 lines a week, none of it written by hand06:08 Why teams are flattening08:26 Why managers need to get back to writing code11:51 How the management layer actually changes14:07 What happens to PMs and designers16:38 Why everyone becomes a builder now20:07 How to actually learn agentic engineering22:42 Finding new tools on X before anyone else23:54 AI adoption is showing up in performance reviews25:45 Building a culture that shares tools daily27:40 The AI productivity paradox, explained29:35 Interviewing with agents instead of against them31:00 Why LeetCode is dead and fundamentals aren't34:11 Who wins and who loses from here35:36 Codex vs Claude Code: what he actually uses36:06 What harness engineering really means38:21 Evals and benchmarks that matter39:59 Desktop apps, Devin, and running agents in parallel43:30 Remote environments and working in monorepos44:34 Skills, plugins, and hooks47:57 Parallel worktrees and staying focused50:42 Agent teams vs subagents53:06 The Finch mission and wrap up

Voice agents are one of the hottest use cases in enterprise right now, but also one of the hardest to actually take live. Getting latency low enough to feel human without dumbing down the responses, making reliable tool calls to a CRM without dropping the customer mid-call, building fallback models for when Anthropic or OpenAI are running hot. None of it is as simple as the demos make it look.Last week I hosted a fireside chat with five eng leaders who deal with this stuff every day: Basia Sudol (Head of Enterprise Solutions, Decagon), Varun Singh (CPTO, Daily), Steven Diaz (FDE Manager, Vapi), Tyler D'Silva (Founding FDE, Retell AI), and Sudarshan Kamath (Founder, Smallest AI).We get into why nobody serious is shipping real-time voice-to-voice yet, why LLMs forget the middle of your prompt (and what that does to your architecture), why a giant prompt quietly destroys your unit economics, and why voice agent costs are now being compared directly against human labor.Plus the stuff nobody warns you about: turn-taking, HIPAA constraints, why outbound is easier than inbound, why getting an exec to actually like the voice can be harder than any model problem, and more!Chapters below:00:00 Intro00:26 Meet the panel01:23 Daily, WebRTC, and 20 years of building voice03:49 How Smallest AI made real-time TTS work05:23 Why Decagon moved into voice08:16 How Vapi and Retell think about the stack11:14 Forward deployed vs solutions engineering16:48 Voice agent architecture, explained simply22:11 Cascade vs speech-to-speech: the real tradeoff28:11 Hybrid pipelines and mixing models32:16 Accents, multilingual, and getting Singlish right35:32 Prompts vs workflows, and the latency fight44:26 How you actually evaluate a voice agent49:04 Simulation-based evals49:49 What production metrics really look like51:53 Building a QA framework that scales54:53 Evaluating speech-to-speech58:10 Open source benchmarks59:57 Why picking a voice is so subjective01:02:03 Personalization and custom voices01:03:20 Voice quality is solved, GPU efficiency is the new war01:06:23 Why outbound calls work better than you'd think01:10:38 Deploying in regulated industries (HIPAA, retention, audits)01:12:43 Turn-taking, the hardest unsolved problem in voice01:18:53 Where voice agents go in the next year01:27:55 Audience Q&A: inside Smallest's Hydra model01:32:15 The deployment problems nobody has solved yet01:37:46 Closing thoughts and thanks

(We know the audio quality isn't great on this one :( But the conversation is still well worth it!)Last week I hosted a fireside chat on what it actually takes to build AI agents in the enterprise with Natalie Meurer (Head of Agent Eng, Sierra), Harsh Trivedi (founding engineer, Mercor), Juhi Parekh (GM, Turing), and Kevin Lynch (Senior FDE, Fin).We get into why new models aren't always better (and why you can't just swap in the latest release and assume your agent improves), how the data-labeling/RL environment business might only have a couple years left, why real-time voice-to-voice models still aren't production-ready, how cheaper inference is still causing prices to go up, how baking in a constellation of models into enterprise agents is so important for reliability,and much, much, more!Chapters below00:00 Intro00:21 Meet the panel01:57 What everyone's actually using agents for day to day06:10 The reality of forward deployed work09:28 What agents couldn't do a year ago that they can now12:29 Why you have to tell agents what NOT to do16:26 What a harness actually is22:03 RL environments explained28:46 Does the data-labeling and RL environment business even last?37:02 Why benchmarks don't tell you what works in production38:12 Agent engineering vs forward deployed engineering41:38 Deploying into 100-year-old enterprise systems44:25 Why AI adoption is an org problem, not a tech problem45:36 Hiring for judgment when engineers aren't really coding anymore48:16 Why agents are a new kind of software50:46 The first 90 days of an enterprise deployment53:20 Why compliance environments break normal testing56:58 Layering AI on AI to get to 99% accuracy01:00:56 New models aren't always better — the swap problem01:02:35 Improving agents without waiting for a new model01:06:36 Does agent performance secretly degrade over time?01:09:40 Why one model is never enough: the constellation approach01:11:14 Building resilience when inference providers go down01:13:48 When fine-tuning actually makes sense01:14:53 Why voice-to-voice still isn't production-ready01:16:25 The cascaded pipeline that real voice agents use01:21:45 Audience Q&A: managing change inside the enterprise01:23:24 Why inference getting cheaper makes things more expensive01:26:54 Charging for outcomes instead of conversations01:30:19 What the real moat is when everyone uses the same models01:37:04 Synthetic data and where the data wall actually is01:38:50 Closing thoughts

Today's episode is with Vince Signori, Sales Director at LangChain and one of the first sales hires at HashiCorp, where he watched the company grow from a small startup all the way to an IPO and get acquired by IBM.He sees the exact same shift happening now with AI agents that happened with cloud back then, except 50x faster. And he's got the numbers to back it up — LangChain is downloaded more than the OpenAI SDK, and 45% of the Fortune 500 are now paying customers.We get into how companies like Toyota and Home Depot are actually using AI agents in production today, why enterprises are building their own private versions of ChatGPT to own their data, and why memory is becoming the most valuable asset in AI.We also talk about what it actually takes to get an agent from prototype to production, why selling open source is the hardest sale in software, and how Vince went from 3 reps doing 20-hour days to running the number one sales region at one of the fastest growing companies in AI.You don't wanna miss this one.Chapters:00:00 Intro01:38 From HashiCorp to LangChain03:20 Cloud wave vs AI agent wave05:30 Open source vs enterprise06:23 How LangChain's product stack evolved07:37 Why agents are finally in production08:57 What companies build with LangGraph10:12 LangSmith and Engine explained13:23 The Agent Development Lifecycle16:36 Build vs buy on voice agents19:48 Why owning your data and memory layer matters22:12 How open source users become paying customers26:49 Why LangChain hired forward deployed engineers31:34 What go-to-market looked like with 3 reps35:31 From 39 employees to hypergrowth36:37 Transitioning away from founder-led sales37:07 Why the CEO joined every early call37:38 The sandwich sale strategy explained39:28 Signals that an open source user is ready to buy41:08 Why outbound controls the narrative in enterprise43:33 Why in-person selling still wins45:57 Building an internal GTM agent to scale47:37 What the GTM agent actually does50:16 Why AI moves 50x faster than cloud did53:46 The vendor consolidation wave that's coming56:14 How to win the platform standardization deal59:41 Why staying model-agnostic beats the hyperscalers01:01:23 How Vince onboards new reps today01:04:19 The sales and engineering feedback loop01:09:49 Signals an account is ready to expand01:12:57 How to prove early value before full commitment01:14:40 How enterprises actually measure agent ROI01:16:27 Why automation is expanding beyond support01:17:59 Which industries are adopting agents fastest01:19:07 Healthcare agent use cases live today01:21:17 AI agents in finance and payments01:22:40 The Visa partnership01:25:17 What it takes to scale a sales team right now01:27:44 How the GTM agent is changing the SDR role01:32:06 Why the human element in sales still matters01:34:28 Platform deals vs point solutions01:36:43 Vince's predictions on memory and consolidation01:39:23 How Engine helps teams iterate on agents faster01:43:19 Forward deployed engineers vs Engine01:46:20 Where to find Vince and LangChain's open roles

Today's episode is with Shrivu Shankar, VP of AI Strategy at Abnormal AI - a $5B cybersecurity company. What makes this one unique is that Shrivu joined as an intern in 2021 and got promoted every single year until he reached VP, so he's basically watched AI go from a niche engineering tool to something that's reshaping entire companies from the inside.We get into how AI is catching cyberattacks so sophisticated that even humans can't tell they're fake, how engineers at a $5B company have basically stopped writing code themselves, and what that means for everyone else on the team.We also go deep on why context engineering is replacing prompt engineering as the real moat, how they used GPT-3 with zero safety guardrails to generate fake phishing attacks as training data, and what it actually takes to become an AI native company at 1,500 people.One of the most technical and eye-opening conversations I've had. You don't wanna miss this one.Chapters:00:00 Intro00:58 Who is Shrivu and what is Abnormal AI01:39 Why cybersecurity and machine learning03:13 Intern to VP in 4 years — how it actually happened05:44 What Abnormal AI does and how it started09:10 The vendor fraud attack so convincing the victim didn't believe it was real10:45 What GPT-3 changed for cybersecurity13:01 Using synthetic data to train models — and how they measured it16:49 How a 1,500 person company actually adopts AI internally19:50 How engineering, PM, and platform roles are changing right now23:17 The biggest AI misconception Shrivu keeps hearing27:35 What Shrivu's day actually looks like as VP of AI Strategy28:53 Engineers stopped writing code. Here's what they do instead.32:28 Why product teams are getting much smaller34:31 Why context engineering beats prompt engineering36:31 Spec-driven development and how Nora Tech Plan works39:14 How to scale context engineering across an entire eng org40:30 What the manager role looks like in the agent era42:17 What skills actually matter for managers now43:29 AI is making orgs flatter. Is that a good thing?45:08 How the C-suite is getting closer to the work46:41 What agents actually are and how tool calling works48:05 How agents improved Abnormal's detection pipeline50:56 The AI phishing coach — how it works and why it matters53:30 The internal AI data analyst agent56:13 Dozens of internal agents — the ones Shrivu is most proud of57:15 Where agents fail (it's usually not the model)58:52 What Shrivu would tell a CEO just starting with agents01:00:19 Sending sensitive security data to LLMs — how they handle it01:01:47 What becoming AI native actually means in practice01:03:37 What most people still get wrong about AI in the enterprise01:04:31 How to write documents with AI without it sounding like AI01:06:40 Claude Code vs Codex — which one and why01:09:27 How Shrivu stays ahead and his take on MCPs01:11:33 How the team uses Claude Code skills01:12:47 Using hooks for shift-left validation in large codebases01:13:42 How to manage context in a massive monorepo01:14:56 Building tool-agnostic rules across Claude, Cursor, and Code Rabbit01:16:55 Why infra teams are becoming agent harness teams01:17:57 Wrap up

Today's episode is with Supriya Gupta, ex-VP of Product at Intuit Credit Karma and former Product Lead on the Ads team at Meta when that business was scaling like crazy.She brings a really unique perspective on everything happening with GenAI right now because she's seen it from the inside at two of the biggest companies in tech.We get into how ads are slowly going to be generated on the spot, per user, where the image, copy and offer will all be unique to you specifically. We talk about why you can't just infinitely scale ad testing even though it's now theoretically possible, and why flooding the internet with AI content might actually be the worst thing you can do for your brand.We also go deep on what actually happened inside Credit Karma when they started building with GenAI, including what moved the needle and what didn't, and what that means for designers, PMs, and content teams everywhere.And at the end, Supriya shares why she walked away from her VP role to start her own company, and what she's building now.You don't wanna miss this one.🔗 Find Supriya on LinkedIn: https://www.linkedin.com/in/supriyag/🌐 Company's website: https://www.helloeve.co/⏱ Chapters00:00 Intro01:27 Predictive AI vs. Generative AI: what actually changed02:59 The next gen of dynamic ads — why every user could soon get a unique ad06:52 Will content agencies survive the AI era?09:17 Why you can't just test a million ad variants (the stats problem)12:55 AI slop, UGC backlash, and should AI content be labeled?17:02 Supriya joins Credit Karma: building the Lightbox targeting platform21:23 Building the Credit Karma financial assistant24:35 Handling hallucinations in a finance app at scale27:26 What happened to content designers when AI started writing copy29:56 Why AI copy still needs human taste and judgment31:01 PMs are prototyping now — what that means for design and eng33:30 Will there be fewer PMs? (Probably not — here's why)35:33 Why Supriya left Credit Karma to start her own company37:23 The principles she built her startup around39:20 The rise of the "super IC" — managers becoming AI-powered operators41:54 Why most AI projects fail before they even start45:33 Enterprises vs. startups: how they approach AI differently48:13 What a successful AI deployment actually looks like51:02 Will everyone need to upskill on AI? (Spoiler: maybe not)52:41 Most execs are thinking about cost-cutting — the smarter ones aren't53:49 The executive digital twin: Supriya's startup vision56:51 Current product, roadmap, and what's shipping next58:52 Where to find Supriya

AI agents are everywhere right now. But are they actually working inside real engineering teams?At AngelList’s Founders Cafe, I sat down with founders of Cognition ($898M raised), Semgrep ($204M raised), Factory ($70M raised), and Composio ($29M raised) to talk about what agentic engineering looks like in practice.There’s a lot of hype around AI coding tools, but the reality is more nuanced. Some teams are moving 10x faster, others are slowing down. A big part of it comes down to whether your codebase is actually “agent-ready” (linting, type systems, guardrails, etc).We also went deep on security, which is one of the biggest gaps right now. As more non-developers start “vibe coding,” the risk surface grows fast. We talked about MCP access control, layered security, and why you can’t rely on models alone to generate secure code.Enterprise teams are also dealing with the operational side of this shift. They have to manage cost, run evals, and help thousands of engineers use these systems well. Tools like PR review agents, model routing, and internal orchestration are quickly becoming part of the stack.⏱ Chapters00:00 Welcome and setup00:41 Panel introductions02:00 Windsurf acquisition03:52 Do agents boost productivity?04:16 Agent-ready codebases05:55 Real-world enterprise wins07:28 Agents building integrations09:41 Security risks (vibe coding)11:04 LLM security tools landscape12:21 Defense-in-depth15:16 MCP security pitfalls18:41 MCP vs CLI23:08 RL for secure code27:05 Auto research missions27:57 Training your own models31:33 Distillation and IP decay34:50 Hybrid systems37:14 Side projects vs enterprise40:16 Forward deployed engineering40:58 Agent orchestration43:08 Cost controls43:49 Auto model routing45:52 Guardrails47:02 Legacy code risks48:16 Model poisoning49:35 What is a harness?51:47 Why build your own52:58 Continuous learning loops56:30 Security workflows57:37 Validation and meta engineering1:00:32 Running evals in practice1:03:36 Teams reshaped by agents1:12:17 Should you study CS?1:14:11 Enterprise adoption1:18:23 Local to cloud journey1:20:51 Agent economy and prompting1:22:22 Spec vs plan1:25:12 Closing and thanks

Summary:In this conversation, I talked to Ashish Shubham (VP of Engineering), who's been at ThoughtSpot for 10 years, about AI agents in enterprise analytics. ThoughtSpot started as a search-based analytics company trying to make data accessible to regular business users. In 2019, they tried building natural language interfaces using BERT, but only hit about 50% accuracy. For a product where enterprise customers make billion-dollar decisions, that wasn't good enough. They shelved the project.When ChatGPT came out, ThoughtSpot was ready. Ashish walked me through how they pivoted: they built a 25-30 person team, decided to use prompting instead of fine-tuning, and leveraged their existing semantic data modeling layer to get accuracy into the high 90s. We got into the technical evolution from monolithic systems to agent architectures with tools, how they went from manual human judges to using LLMs to evaluate their outputs, and how enterprise security requirements shaped what they built.We also talked about how software engineering is changing. Ashish said 50-60% of his code is AI-generated now, and he thinks system design is becoming the critical skill, even for junior engineers. He had an interesting take on the "95% of AI deployments fail" stat too.Chapters:0:00 Intro and Ashish's journey to ThoughtSpot from GoDaddy0:13 ThoughtSpot's mission to democratize data analytics for business users1:26 Early search-based analytics before natural language processing2:36 ThoughtSpot vs Tableau and the promise of self-service analytics4:40 The analyst bottleneck problem and how ThoughtSpot aimed to solve it5:49 Early technical challenges with in-memory databases and data migration8:11 Semantic data models, joins, and creating abstraction layers for users11:39 Who builds the data models and the role of analysts12:22 Pre-LLM natural language processing using BERT and word2vec in 2018-201914:43 The accuracy problem and ambiguity in translating user queries16:58 Trust challenges and why the early NLP product never became core19:59 Competition with Tableau, Looker, and Power BI22:44 How analyst roles changed with self-service analytics tools25:30 The ChatGPT moment and pivoting to LLM-powered natural language27:48 Early prompt engineering days and generating SQL with LLMs31:09 Training vs prompting debate and why fine-tuning was eventually abandoned34:28 Organizational changes and building the NLS team37:16 Coaching systems for company-specific terminology vs training models39:02 Evolution of evaluation methods from human judges to LLM-as-judge43:23 Moving to LangFuse and GCP for agent infrastructure46:29 How LLM context windows and capabilities evolved their product50:07 From 30-column limits to agentic systems with 90%+ accuracy52:52 RAG, column selection, and using proprietary data indexes54:59 Multi-model support and enterprise data security concerns59:14 How AI has changed Ashish's personal engineering workflow1:02:42 Impact of AI on the broader engineering organization1:04:15 Measuring AI productivity and the challenge of metrics1:07:26 50-60% AI-generated code and the changing nature of coding1:09:18 System design skills becoming more important than coding1:13:00 Junior engineers doing senior-level work and interview changes1:14:37 Customer conversations about Gen AI adoption across industries1:17:26 The MIT report on 95% agent failures and why it misses the point1:22:12 Agent architecture with LangGraph vs Google ADK and building internal agent platform1:24:26 Where value lies in the next two years: tools, skills, and optimization1:28:05 Startup opportunities in making AI accessible to non-technical users1:29:26 Closing remarks

In this conversation, Tatu discusses the transformative impact of AI on game development, drawing from his extensive experience in the gaming industry. He highlights the shift from traditional game development processes to a more agile, AI-driven approach that allows for rapid prototyping and iteration. Tatu emphasizes the importance of organizational change and the need for leaders to embrace AI as a core part of their strategy. He also explores the evolving role of product managers, the challenges of user acquisition, and the future of marketing in a saturated gaming market. The discussion culminates in Tatu's vision for his new AI-native game studio, aiming to disrupt the industry by leveraging cutting-edge technology to create high-quality games at unprecedented speed.Takeaways:AI is condensing the time and resources needed for game development.Organizational inertia can hinder the adoption of AI in large companies.The future of game development will require T-shaped professionals with diverse skills.AI will fundamentally change the economics of the gaming industry.Smaller companies can leverage AI to outmaneuver larger competitors.The role of product managers will evolve as AI takes over prioritization tasks.Marketing strategies will need to adapt to a more saturated market.User acquisition costs are expected to rise due to increased competition.Novelty may not be as valuable as familiarity in a saturated market.The future of entertainment will see a rise in fast, iterative game development.Chapters:00:00 The Evolution of Game Development with AI03:07 From Web Design to Gaming: A Career Journey05:50 The Impact of AI on Knowledge Work09:07 The Changing Landscape of Game Development11:53 Organizational Inertia and the Future of Gaming Companies14:55 The Role of AI in Transforming Game Development17:57 Navigating the Challenges of AI Adoption21:08 The Future of Game Development Methodologies23:46 The Role of Product Managers in an AI-Driven World26:47 Marketing Strategies in the Gaming Industry29:59 The Role of Publishers in Game Development33:05 The Future of User Acquisition in Gaming36:02 The Changing Economics of Game Development38:56 The Future of Software Development42:13 The Role of Novelty in Game Development45:04 The Importance of Familiarity in a Saturated Market48:12 The Future of Fast Entertainment50:59 Leveraging Licensing for Success54:02 The Journey from Rovio to AI Native Gaming57:02 Building Tools for Rapid Game Development59:57 The Vision for Future Games01:03:04 AI Adoption in Organizations: A Leader's Perspective