Forward Deployed

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