Forward Deployed

Basil Chatha

Discover how leading enterprises and professionals turn AI into real products. Hear candid conversations with executives and builders who deploy AI at scale and learn what works (and what doesn't).

  1. 4d ago

    Tony Gentilcore - Glean, the $7.2B Startup Sam Altman Warned Investors About

    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 Setup 00:56 Glean Origin Story 03:08 Enterprise Search Signals 05:07 LLMs Inflection Point 08:08 Early Product Workflow 09:45 Search Evals and Privacy 12:05 From Chatbots to Agents 13:45 Agent Use Cases 17:08 Lessons and Puck Direction 19:08 Bot sitting, bot shitting, and slop 24:44 Token Budgets and ROI 30:59 AI Trends and Moats 35:03 Training and Fine Tuning 38:03 Why Enterprises Fear China Models 39:58 Sovereignty Backlash Watch 40:26 The time Altman called out Glean 41:19 Org Roles Become Builders 44:10 Future Work Voice First 48:17 Vibe Coding vs Quality 52:20 SaaSpocalypse Evolution 54:19 Pricing Tokens Win 55:37 Outcome Pricing Doubts 57:02 Audience AI Review Overload 58:53 Subsidies and Model Choice 01:01:18 Context Layer Interoperability 01:04:52 Build vs. buy: rolling your own Glean 01:07:47 Shadow AI and the coming security mess 01:12:22 Who actually competes 01:13:36 Wrap-up and thanks

    Tony Gentilcore - Glean, the $7.2B Startup Sam Altman Warned Investors About

  2. Jul 27

    Russ Salakhutdinov - Kimi K3 CEO’s PhD Advisor Predicts the Future of AI Agents

    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 - Intro 1:26 - Bumping into Hinton on the street 3:25 - When neural nets were the third choice 5:21 - Generating digits before it was cool 6:59 - AlexNet breaks computer vision 10:18 - Teaching models to describe what they see 12:10 - Early text-to-image (and the toilet seat that beat Google) 16:52 - Hallucination is a feature 19:36 - Selling Perceptual Machines to Apple 22:45 - Self-driving: 0 to 80 in a year, stuck for 5 30:30 - Inside FSD and Waymo's architecture 34:18 - Building Visual Web Arena at CMU 39:07 - Why he joined Meta Superintelligence 40:09 - The agent that plans your faculty job hunt 42:00 - Paying people for their browser history 42:45 - The coupon-hunting agent 43:35 - Why agents still fail 46:14 - 60% when you need 99.9% 47:04 - Agents on your phone 50:12 - No secret architecture at the frontier labs 51:20 - Why coding and math got solved first 54:01 - Models that smell and touch 56:14 - The future of software engineering 59:41 - Founding Sooth Labs 1:00:07 - The 13% graduation prediction 1:05:55 - Why ChatGPT can't forecast 1:08:12 - Agents first, decision systems next 1:09:23 - The Wikipedia contamination story 1:13:43 - Can AI beat prediction markets? 1:16:26 - AI replaces McKinsey 1:18:20 - Why the crowd is hard to beat 1:19:30 - China's open source models rise 1:24:26 - Why the US needs its own open models 1:25:53 - RL environment businesses won't last 1:28:35 - The end of SaaS, LLMs as commodities 1:32:55 - What's next: self-improvement, forecasting, robots 1:35:34 - The only useful robot is the Roomba 1:37:53 - What he'd study in college today 1:41:20 - Adapt or get left behind 1:44:07 - Wrapping up

    Russ Salakhutdinov - Kimi K3 CEO’s PhD Advisor Predicts the Future of AI Agents

  3. Jul 17

    Andy Hock - How Cerebras Plans to Kill Nvidia

    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. Chapters 00:00 Intro 01:06 What Cerebras Actually Builds 03:41 Before LLMs Existed 06:09 The GPT Wake-Up Call 10:45 First Principles of AI Compute 15:40 One Giant Chip 18:43 So Why Do We Still Use GPUs? 20:59 Why Inference Took Over 22:27 Where Fast Tokens Win 25:07 Is AI Infra a Bubble? 29:56 The Memory Shortage, Explained 32:35 The Energy Problem 35:34 Rolling Their Own Data Centers 38:20 What a Chip Actually Costs 40:18 AI Designing AI Chips 41:40 The Supply Chain Reality 43:45 Cheap Tokens vs Fast Tokens 46:11 Why Every Millisecond Matters 48:56 Inside the OpenAI Deal 51:56 The Gigawatt Future 54:00 Selling to the Government 58:36 Exporting the US AI Stack 01:03:17 Should We Sell Chips to China? 01:06:00 Why Europe Is Falling Behind 01:07:59 Why Enterprise AI Moves Slow 01:13:05 "I Haven't Read Code in Months" 01:14:32 The Next Cerebras Chip 01:16:50 Closing Thoughts

    Andy Hock - How Cerebras Plans to Kill Nvidia

  4. Jul 10

    Leo Mehr - Ramp’s $44B Bet on Services

    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! Chapters 00:00 Intro 01:11 Meet Leo Maier 01:46 Building Ramp FDE 02:43 Why FDE Exists 04:23 Early Mandate Fires 06:07 FDE and Core Engineering 07:48 Enterprise Bet Pays 11:10 Ramp Monetization 16:05 Services Are the New Software 18:11 AI Agents and APIs 23:11 Pricing for Outcomes 26:35 Human Labor TAM 32:50 VCs and Rollups 36:50 Common AI Workflow Pain 38:04 Agent Data Context 38:55 Go-to-Market Motion 40:36 Customer Engagement Lifecycle 43:51 Finance Intelligence Layer 47:20 Who You Compete Against 48:09 The Future of Consulting 51:44 Will Humans Still Code? 54:37 How Engineering Teams Will Evolve 57:36 Hiring Change 59:36 Should You Study Computer Science? 01:06:10 The Bull Case and the Risks 01:13:44 Why Ramp

    Leo Mehr - Ramp’s $44B Bet on Services

  5. Jul 3

    Siddharth Nanda - Microsoft Engineer Reveals how Engineering Will Never be the Same Again

    "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. Chapters 00:00 Intro 01:16 What engineering looked like at Microsoft before ChatGPT 03:08 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 hand 06:08 Why teams are flattening 08:26 Why managers need to get back to writing code 11:51 How the management layer actually changes 14:07 What happens to PMs and designers 16:38 Why everyone becomes a builder now 20:07 How to actually learn agentic engineering 22:42 Finding new tools on X before anyone else 23:54 AI adoption is showing up in performance reviews 25:45 Building a culture that shares tools daily 27:40 The AI productivity paradox, explained 29:35 Interviewing with agents instead of against them 31:00 Why LeetCode is dead and fundamentals aren't 34:11 Who wins and who loses from here 35:36 Codex vs Claude Code: what he actually uses 36:06 What harness engineering really means 38:21 Evals and benchmarks that matter 39:59 Desktop apps, Devin, and running agents in parallel 43:30 Remote environments and working in monorepos 44:34 Skills, plugins, and hooks 47:57 Parallel worktrees and staying focused 50:42 Agent teams vs subagents 53:06 The Finch mission and wrap up

    Siddharth Nanda - Microsoft Engineer Reveals how Engineering Will Never be the Same Again

  6. Jun 30

    Voice AI - The Next Frontier | Decagon, Retell, Vapi, Smallest AI, Daily

    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 Intro 00:26 Meet the panel 01:23 Daily, WebRTC, and 20 years of building voice 03:49 How Smallest AI made real-time TTS work 05:23 Why Decagon moved into voice 08:16 How Vapi and Retell think about the stack 11:14 Forward deployed vs solutions engineering 16:48 Voice agent architecture, explained simply 22:11 Cascade vs speech-to-speech: the real tradeoff 28:11 Hybrid pipelines and mixing models 32:16 Accents, multilingual, and getting Singlish right 35:32 Prompts vs workflows, and the latency fight 44:26 How you actually evaluate a voice agent 49:04 Simulation-based evals 49:49 What production metrics really look like 51:53 Building a QA framework that scales 54:53 Evaluating speech-to-speech 58:10 Open source benchmarks 59:57 Why picking a voice is so subjective 01:02:03 Personalization and custom voices 01:03:20 Voice quality is solved, GPU efficiency is the new war 01:06:23 Why outbound calls work better than you'd think 01:10:38 Deploying in regulated industries (HIPAA, retention, audits) 01:12:43 Turn-taking, the hardest unsolved problem in voice 01:18:53 Where voice agents go in the next year 01:27:55 Audience Q&A: inside Smallest's Hydra model 01:32:15 The deployment problems nobody has solved yet 01:37:46 Closing thoughts and thanks

    Voice AI - The Next Frontier | Decagon, Retell, Vapi, Smallest AI, Daily

  7. Jun 23

    AI Agents in the Enterprise | Sierra, Mercor, Intercom, Turing | $2.8B+ Raised

    (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 below 00:00 Intro 00:21 Meet the panel 01:57 What everyone's actually using agents for day to day 06:10 The reality of forward deployed work 09:28 What agents couldn't do a year ago that they can now 12:29 Why you have to tell agents what NOT to do 16:26 What a harness actually is 22:03 RL environments explained 28:46 Does the data-labeling and RL environment business even last? 37:02 Why benchmarks don't tell you what works in production 38:12 Agent engineering vs forward deployed engineering 41:38 Deploying into 100-year-old enterprise systems 44:25 Why AI adoption is an org problem, not a tech problem 45:36 Hiring for judgment when engineers aren't really coding anymore 48:16 Why agents are a new kind of software 50:46 The first 90 days of an enterprise deployment 53:20 Why compliance environments break normal testing 56:58 Layering AI on AI to get to 99% accuracy 01:00:56 New models aren't always better — the swap problem 01:02:35 Improving agents without waiting for a new model 01:06:36 Does agent performance secretly degrade over time? 01:09:40 Why one model is never enough: the constellation approach 01:11:14 Building resilience when inference providers go down 01:13:48 When fine-tuning actually makes sense 01:14:53 Why voice-to-voice still isn't production-ready 01:16:25 The cascaded pipeline that real voice agents use 01:21:45 Audience Q&A: managing change inside the enterprise 01:23:24 Why inference getting cheaper makes things more expensive 01:26:54 Charging for outcomes instead of conversations 01:30:19 What the real moat is when everyone uses the same models 01:37:04 Synthetic data and where the data wall actually is 01:38:50 Closing thoughts

    AI Agents in the Enterprise | Sierra, Mercor, Intercom, Turing | $2.8B+ Raised

  8. Jun 10

    Vince Signori: Inside LangChain's Growth Strategy from $200M to $1.25B

    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 Intro 01:38 From HashiCorp to LangChain 03:20 Cloud wave vs AI agent wave 05:30 Open source vs enterprise 06:23 How LangChain's product stack evolved 07:37 Why agents are finally in production 08:57 What companies build with LangGraph 10:12 LangSmith and Engine explained 13:23 The Agent Development Lifecycle 16:36 Build vs buy on voice agents 19:48 Why owning your data and memory layer matters 22:12 How open source users become paying customers 26:49 Why LangChain hired forward deployed engineers 31:34 What go-to-market looked like with 3 reps 35:31 From 39 employees to hypergrowth 36:37 Transitioning away from founder-led sales 37:07 Why the CEO joined every early call 37:38 The sandwich sale strategy explained 39:28 Signals that an open source user is ready to buy 41:08 Why outbound controls the narrative in enterprise 43:33 Why in-person selling still wins 45:57 Building an internal GTM agent to scale 47:37 What the GTM agent actually does 50:16 Why AI moves 50x faster than cloud did 53:46 The vendor consolidation wave that's coming 56:14 How to win the platform standardization deal 59:41 Why staying model-agnostic beats the hyperscalers 01:01:23 How Vince onboards new reps today 01:04:19 The sales and engineering feedback loop 01:09:49 Signals an account is ready to expand 01:12:57 How to prove early value before full commitment 01:14:40 How enterprises actually measure agent ROI 01:16:27 Why automation is expanding beyond support 01:17:59 Which industries are adopting agents fastest 01:19:07 Healthcare agent use cases live today 01:21:17 AI agents in finance and payments 01:22:40 The Visa partnership 01:25:17 What it takes to scale a sales team right now 01:27:44 How the GTM agent is changing the SDR role 01:32:06 Why the human element in sales still matters 01:34:28 Platform deals vs point solutions 01:36:43 Vince's predictions on memory and consolidation 01:39:23 How Engine helps teams iterate on agents faster 01:43:19 Forward deployed engineers vs Engine 01:46:20 Where to find Vince and LangChain's open roles

    Vince Signori: Inside LangChain's Growth Strategy from $200M to $1.25B
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About

Discover how leading enterprises and professionals turn AI into real products. Hear candid conversations with executives and builders who deploy AI at scale and learn what works (and what doesn't).

Information

  • Creator
    Basil Chatha
  • Years Active
    2026
  • Episodes
    13
  • Rating
    Explicit
  • Copyright
    © 2026 Basil Chatha

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