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).

ଏପିସୋଡ୍

  1. 3 ଦିନ ପୂର୍ବେ

    Supriya Gupta - Meta Exec Explains How AI is Reshaping Advertising

    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/ ⏱ Chapters 00:00 Intro 01:27 Predictive AI vs. Generative AI: what actually changed 02:59 The next gen of dynamic ads — why every user could soon get a unique ad 06:52 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 platform 21:23 Building the Credit Karma financial assistant 24:35 Handling hallucinations in a finance app at scale 27:26 What happened to content designers when AI started writing copy 29:56 Why AI copy still needs human taste and judgment 31:01 PMs are prototyping now — what that means for design and eng 33:30 Will there be fewer PMs? (Probably not — here's why) 35:33 Why Supriya left Credit Karma to start her own company 37:23 The principles she built her startup around 39:20 The rise of the "super IC" — managers becoming AI-powered operators 41:54 Why most AI projects fail before they even start 45:33 Enterprises vs. startups: how they approach AI differently 48:13 What a successful AI deployment actually looks like 51:02 Will everyone need to upskill on AI? (Spoiler: maybe not) 52:41 Most execs are thinking about cost-cutting — the smarter ones aren't 53:49 The executive digital twin: Supriya's startup vision 56:51 Current product, roadmap, and what's shipping next 58:52 Where to find Supriya

    57 ମି.

  2. ଅପ୍ରେଲ 15

    The Future of Agentic Engineering | Cognition (Devin), Semgrep, Factory & Composio | $1.2B+ Raised

    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. ⏱ Chapters 00:00 Welcome and setup 00:41 Panel introductions 02:00 Windsurf acquisition 03:52 Do agents boost productivity? 04:16 Agent-ready codebases 05:55 Real-world enterprise wins 07:28 Agents building integrations 09:41 Security risks (vibe coding) 11:04 LLM security tools landscape 12:21 Defense-in-depth 15:16 MCP security pitfalls 18:41 MCP vs CLI 23:08 RL for secure code 27:05 Auto research missions 27:57 Training your own models 31:33 Distillation and IP decay 34:50 Hybrid systems 37:14 Side projects vs enterprise 40:16 Forward deployed engineering 40:58 Agent orchestration 43:08 Cost controls 43:49 Auto model routing 45:52 Guardrails 47:02 Legacy code risks 48:16 Model poisoning 49:35 What is a harness? 51:47 Why build your own 52:58 Continuous learning loops 56:30 Security workflows 57:37 Validation and meta engineering 1:00:32 Running evals in practice 1:03:36 Teams reshaped by agents 1:12:17 Should you study CS? 1:14:11 Enterprise adoption 1:18:23 Local to cloud journey 1:20:51 Agent economy and prompting 1:22:22 Spec vs plan 1:25:12 Closing and thanks

    1ଘ. 26ମି.
  3. He Built a $200M AI Agent 10 Years Before ChatGPT

    ଜାନୁଆରୀ 28

    He Built a $200M AI Agent 10 Years Before ChatGPT

    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 GoDaddy 0:13 ThoughtSpot's mission to democratize data analytics for business users 1:26 Early search-based analytics before natural language processing 2:36 ThoughtSpot vs Tableau and the promise of self-service analytics 4:40 The analyst bottleneck problem and how ThoughtSpot aimed to solve it 5:49 Early technical challenges with in-memory databases and data migration 8:11 Semantic data models, joins, and creating abstraction layers for users 11:39 Who builds the data models and the role of analysts 12:22 Pre-LLM natural language processing using BERT and word2vec in 2018-2019 14:43 The accuracy problem and ambiguity in translating user queries 16:58 Trust challenges and why the early NLP product never became core 19:59 Competition with Tableau, Looker, and Power BI 22:44 How analyst roles changed with self-service analytics tools 25:30 The ChatGPT moment and pivoting to LLM-powered natural language 27:48 Early prompt engineering days and generating SQL with LLMs 31:09 Training vs prompting debate and why fine-tuning was eventually abandoned 34:28 Organizational changes and building the NLS team 37:16 Coaching systems for company-specific terminology vs training models 39:02 Evolution of evaluation methods from human judges to LLM-as-judge 43:23 Moving to LangFuse and GCP for agent infrastructure 46:29 How LLM context windows and capabilities evolved their product 50:07 From 30-column limits to agentic systems with 90%+ accuracy 52:52 RAG, column selection, and using proprietary data indexes 54:59 Multi-model support and enterprise data security concerns 59:14 How AI has changed Ashish's personal engineering workflow 1:02:42 Impact of AI on the broader engineering organization 1:04:15 Measuring AI productivity and the challenge of metrics 1:07:26 50-60% AI-generated code and the changing nature of coding 1:09:18 System design skills becoming more important than coding 1:13:00 Junior engineers doing senior-level work and interview changes 1:14:37 Customer conversations about Gen AI adoption across industries 1:17:26 The MIT report on 95% agent failures and why it misses the point 1:22:12 Agent architecture with LangGraph vs Google ADK and building internal agent platform 1:24:26 Where value lies in the next two years: tools, skills, and optimization 1:28:05 Startup opportunities in making AI accessible to non-technical users 1:29:26 Closing remarks

    1ଘ. 31ମି.
  4. He Led AI Transformation for Angry Birds. Then He Quit.

    ଜାନୁଆରୀ 7

    He Led AI Transformation for Angry Birds. Then He Quit.

    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 AI 03:07 From Web Design to Gaming: A Career Journey 05:50 The Impact of AI on Knowledge Work 09:07 The Changing Landscape of Game Development 11:53 Organizational Inertia and the Future of Gaming Companies 14:55 The Role of AI in Transforming Game Development 17:57 Navigating the Challenges of AI Adoption 21:08 The Future of Game Development Methodologies 23:46 The Role of Product Managers in an AI-Driven World 26:47 Marketing Strategies in the Gaming Industry 29:59 The Role of Publishers in Game Development 33:05 The Future of User Acquisition in Gaming 36:02 The Changing Economics of Game Development 38:56 The Future of Software Development 42:13 The Role of Novelty in Game Development 45:04 The Importance of Familiarity in a Saturated Market 48:12 The Future of Fast Entertainment 50:59 Leveraging Licensing for Success 54:02 The Journey from Rovio to AI Native Gaming 57:02 Building Tools for Rapid Game Development 59:57 The Vision for Future Games 01:03:04 AI Adoption in Organizations: A Leader's Perspective

    1ଘ. 16ମି.
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ବିଷୟରେ

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).

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    Basil Chatha
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    © 2026 Basil Chatha