Episode #90: Nobody Knows What Software Is Worth Anymore

In this episode of the Stewart Squared podcast, host Stewart Alsop II connects from Tangier, Morocco while his son Stewart Alsop III digs deep into the technical challenges of building video conferencing software, specifically tackling the notorious lip sync problem that's consumed his last two months. The conversation moves from mutation testing and DevOps to exploring the future of software consulting, examining why Silicon Valley has long held a visceral distrust of consultants while contractors thrive, and what AI-powered development means for how software gets built and sold in the coming years. Stewart III shares his journey from "vibe coding" to implementing scientific methods in his development process, while his father draws on decades of experience as both a journalist and investor to contextualize the shifting landscape of enterprise software, touching on everything from the rise of SaaS to why companies like Riverside raised $80 million while Stewart III builds competing technology solo in his head.

Timestamps

00:00 Welcome from Morocco, Stewart Senior joins from Tangier with Middle Eastern backdrop, Stewart Junior deep in AI development learning mutation testing, integration tests, unit tests, red to green testing
05:00 Discussion of vibe coding evolution to scientific method coding, working on lip sync white whale problem for two months, building pipeline from recording to post-production using FFMPEG diagnostics
10:00 Explanation of how recording works with separate audio and video streams, discovery that browser clocks using tiny crystals don't keep accurate time, learning about MediaRecorder API versus WebCodecs advantages
15:00 Debate about competing with Riverside's 80 million dollar funding, discussion of building specialized software versus SaaS products, exploring turnkey podcasting solutions and business models
20:00 Deep dive into consultancy business model, Stewart Senior's visceral hatred of consultants, discussion of business school graduates becoming consultants or bankers, Microsoft's deliberately small consulting practice
25:00 Exploration of conflict of interest in journalism and investing, disclosure requirements, comparison to New York Times OpenAI lawsuit, discussion of father's unpaid consulting role in DC power centers
30:00 History of consultancies like Arthur Andersen and PricewaterhouseCoopers, role in mergers and acquisitions, example of David Ellison buying Paramount and pursuing Warner Brothers Discovery
35:00 Difference between contractors and consultants, discussion of outsourcing to India, Cloud Factory in Nepal, Ronald Coase economics, Infosys as first big software engineering consultancy
40:00 Stewart Junior's ability to understand code concepts without reading code, using scientific method and chaos monkey development, Netflix streaming techniques, debugging through sufficient motivation
45:00 Sales challenges and negotiation skills in family, working with mentor Zavant on sales frameworks, generosity versus transactional relationships, Turkish bazaar negotiation culture comparison
50:00 Discussion of value creation and belief in sellability, the 80/20 rule of product completion, Adam Neumann and Travis Kalanick examples, Elon Musk as builder not salesman creating entire systems

Key Insights

1. The challenge of solving technical problems reveals the importance of understanding methodologies over mastering code itself. Stewart Alsop III spent two months wrestling with a lip sync problem in his video recording system, learning about mutation testing, integration tests, and DevOps along the way. The key insight is that he does not need to read or write code directly anymore. Instead, he needs only a conceptual understanding of frameworks like the scientific method or chaos engineering to direct AI systems to solve complex technical problems. This represents a fundamental shift where domain knowledge and problem articulation matter more than programming expertise.
2. Modern video conferencing systems create synchronization challenges because different computers use tiny crystals to keep time, but these crystals do not maintain perfect accuracy, especially when network conditions fluctuate. The problem is not simply about recording separate audio and video streams and reassembling them. Instead, systems create containers with audio and video together while also recording separate audio tracks, and all these different clocks drift apart from each other. This explains why lip sync issues plague even well funded platforms like Riverside, and why solving this problem requires sophisticated diagnostic systems and conversion pipelines using tools like FFMPEG.
3. The evolution of software business models reflects changing technological constraints and market conditions. In the 1990s and early 2000s, software was sold as one time purchases, often on physical media like cartridges or floppy disks. The shift to Software as a Service in the 2010s happened because it was considered better for customers who did not have to pay large upfront fees and because cloud infrastructure made it feasible. Now, with AI enabling individuals to build complex software themselves, we may be entering another transition period where the SaaS model itself becomes obsolete, though what will replace it remains unclear.
4. Programming represents the first domain where artificial general intelligence has effectively arrived because programming consists entirely of text. Unlike domains involving physical manipulation or subjective judgment, code can be completely represented in language, and decades of open source code provide massive training datasets. This explains why tools like Claude have become so powerful so quickly in programming contexts, and why Anthropic claims that most of its models are now generated by AI systems themselves. The recursive nature of AI writing code to improve AI represents a fundamental breakthrough that does not yet exist in other domains.
5. Consultancies emerged to solve problems that companies could not efficiently solve themselves, but their value proposition is eroding. Large consulting firms like the Big Seven accounting firms grew powerful by integrating complex enterprise software and managing mergers and acquisitions. However, as software becomes easier to build and modify through AI, and as the difficulty of integration decreases, the justification for expensive consultancies diminishes. The antipathy toward consultants in Silicon Valley stems from a belief that they represent companies paying others to think for them rather than developing internal capabilities, and this critique becomes more valid as technical barriers fall.
6. The distinction between contractors and consultants matters for understanding business models and value creation. Contractors are individuals or small teams hired for specific projects who sell their labor directly. Consultancies are businesses built around winning large contracts and then deploying teams to execute them, often with substantial markup. The emergence of platforms like Upwork and the phenomenon of outsourcing to places like India, Nepal, and Kenya created hybrid models where individual profiles often mask small consultant operations. Understanding these distinctions helps clarify what kind of business model makes sense for someone developing new technical capabilities.
7. Believing in the value of what you create is a prerequisite for being able to sell it, and products must be truly finished before they have sellable value. The last twenty percent of any project, whether writing, programming, or product development, represents the hardest work because it involves transforming something functional into something polished and complete. Until the lip sync problem is definitively solved, the video recording system remains a prototype rather ...