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).

Episodes

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

    1d ago

    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

    1h 16m
  2. Siddharth Nanda - Microsoft Engineer Reveals how Engineering Will Never be the Same Again

    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

    54 min
  3. Voice AI - The Next Frontier | Decagon, Retell, Vapi, Smallest AI, Daily

    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

    1h 38m
  4. AI Agents in the Enterprise | Sierra, Mercor, Intercom, Turing | $2.8B+ Raised

    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

    1h 42m
  5. Vince Signori: Inside LangChain's Growth Strategy from $200M to $1.25B

    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

    1h 47m
  6. Shrivu Shankar - How a $5B Cybersecurity Company Runs on AI Agents

    May 11

    Shrivu Shankar - How a $5B Cybersecurity Company Runs on AI Agents

    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 Intro 00:58 Who is Shrivu and what is Abnormal AI 01:39 Why cybersecurity and machine learning 03:13 Intern to VP in 4 years — how it actually happened 05:44 What Abnormal AI does and how it started 09:10 The vendor fraud attack so convincing the victim didn't believe it was real 10:45 What GPT-3 changed for cybersecurity 13:01 Using synthetic data to train models — and how they measured it 16:49 How a 1,500 person company actually adopts AI internally 19:50 How engineering, PM, and platform roles are changing right now 23:17 The biggest AI misconception Shrivu keeps hearing 27:35 What Shrivu's day actually looks like as VP of AI Strategy 28:53 Engineers stopped writing code. Here's what they do instead. 32:28 Why product teams are getting much smaller 34:31 Why context engineering beats prompt engineering 36:31 Spec-driven development and how Nora Tech Plan works 39:14 How to scale context engineering across an entire eng org 40:30 What the manager role looks like in the agent era 42:17 What skills actually matter for managers now 43:29 AI is making orgs flatter. Is that a good thing? 45:08 How the C-suite is getting closer to the work 46:41 What agents actually are and how tool calling works 48:05 How agents improved Abnormal's detection pipeline 50:56 The AI phishing coach — how it works and why it matters 53:30 The internal AI data analyst agent 56:13 Dozens of internal agents — the ones Shrivu is most proud of 57:15 Where agents fail (it's usually not the model) 58:52 What Shrivu would tell a CEO just starting with agents 01:00:19 Sending sensitive security data to LLMs — how they handle it 01:01:47 What becoming AI native actually means in practice 01:03:37 What most people still get wrong about AI in the enterprise 01:04:31 How to write documents with AI without it sounding like AI 01:06:40 Claude Code vs Codex — which one and why 01:09:27 How Shrivu stays ahead and his take on MCPs 01:11:33 How the team uses Claude Code skills 01:12:47 Using hooks for shift-left validation in large codebases 01:13:42 How to manage context in a massive monorepo 01:14:56 Building tool-agnostic rules across Claude, Cursor, and Code Rabbit 01:16:55 Why infra teams are becoming agent harness teams 01:17:57 Wrap up

    1h 18m
  7. Supriya Gupta - Meta Exec Explains How AI is Reshaping Advertising

    May 4

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

    Apr 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

    1h 26m
  9. He Built a $200M AI Agent 10 Years Before ChatGPT

    Jan 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

    1h 31m
  10. He Led AI Transformation for Angry Birds. Then He Quit.

    Jan 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

    1h 16m
  • 10 Episodes

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).

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  • Creator
    Basil Chatha
  • Years Active
    2K
  • Episodes
    10
  • Rating
    Clean
  • Copyright
    © 2026 Basil Chatha

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