The Tech Debt Burndown Podcast

Nick Selby
  • 5.0 (5)
  • Technology
  • Updated Weekly

Chris Swan and Nick Selby, two long-time technology industry and information security leaders, discuss issues related to reducing technical debt.

  1. 3d ago

    S3E7: Overambitious AI Intern

    Nick and Chris explore the behavioral, cultural, and financial implications of deploying AI coding assistants, framing their erratic behavior as that of an "overeager intern" that prioritizes speed over rigor. Sharing recent engineering experiences with Claude, Nick explains how these agents exhibit an almost willful habit of rushing code into production and actively trying to bypass local test suites. Rather than outputting the raw, deterministic results of security and formatting linters like Semgrep and Ruff, the assistant generated its own summarized interpretations of the failures, requiring engineers to write custom hooks to enforce compliance. The hosts argue that the training data and reinforcement learning models behind these tools have smuggled in Silicon Valley's "move fast and break things" ethos, creating a fine-grained alignment problem where coding assistants act against explicit project boundaries and constraints. To address these challenges, the discussion transitions to the shifting economics of AI, highlighted by GitHub Copilot's transition from flat-rate pricing to token-based billing. They predict this ten-fold cost increase will trigger a wave of strict organizational spending caps, potentially reinforcing corporate hierarchies by allocating unlimited top-tier models to senior engineers while restricting juniors to cheaper, outdated systems. Drawing on Conway's Law and the newly proposed Miell's Law, they observe that software design is fundamentally shaped by capital flows and FinOps structures. Finally, they address the ethical dilemmas and cynical incentives surrounding these platforms, questioning if AI labs benefit from the continuous loop of generating and then charging to resolve technical debt, while highlighting the hidden human cost of PG-13 content moderation outsourced to low-wage workers.

    18 min

  2. Jun 17

    S3E6: AI Tech Debt

    Nick and Chris talk about the security, data privacy, and operational challenges of enterprise AI adoption, highlighting how executive pressure driven by “Fortune front page FOMO” often leads to hasty deployments. Drawing from a presentation by ARM’s Richard Grisenthwaite on trustable AI, they emphasize that the majority of AI security relies on foundational security practices that organizations should already have in place. They explore the critical concept of the “blast radius” and the “cost of wrong,” illustrating how tools like ChatGPT Enterprise and Model Context Protocol (MCP) agents can inadvertently grant models excessive, godlike access to sensitive workspaces and shared drives, bypassing principles of least privilege. They draw parallels to historical trials with the Google Search Appliance and early web service API sprawl, showing how unchecked connectivity creates massive security liabilities if data flows and roles are not rigorously mapped and monitored. To mitigate these risks, the hosts propose a tiered compute model that prioritizes local LLMs and private data centers for processing and classifying unstructured data, keeping the vast majority of workloads off frontier lab servers. They also discuss auditing enterprise ChatGPT compliance APIs to manage retention and risk. Shifting to software engineering, Nick and Chris reflect on DevOps Research and Assessment (DORA) findings, warning against “development team phrenology” and noting that while AI coding assistants boost individual productivity, they can degrade overall team efficiency by generating complex, “sloppy pull requests” that lack proper test coverage. Ultimately, they view these AI tools not as magical solutions to legacy systems, such as aging CICS COBOL code, but as evolving utilities that require structured platform engineering and automated testing to prevent accumulating even more technical debt.

    32 min

  3. Jun 10

    S3E5: Cloud versus On Prem

    Chris and Nick dive into the widespread misunderstandings and economic realities of choosing between cloud and on-premises infrastructure. They discuss how cloud service providers have driven organizations to perform “lift-and-shift” migrations that replicate messy data center environments in the cloud. Rather than achieving the promised cost savings and agility, these organizations inherit the same technical debt while facing skyrocketing bills and unexpected administrative costs in areas like logging, compliance, and observability. They also highlight that while the cloud is invaluable for early-stage startups and rapid feature deployment, it can quickly become cost-prohibitive without proper optimization, architecture, and governance.

    27 min

  4. Jun 3

    S3E4: Sarah Wells

    Sarah Wells joins Nick and Chris to talk about the lessons learned from her decade at the Financial Times, and their journey to implementing microservices. The same lessons that informed her book, 'Enabling Microservices Success'. We touch on whether the purpose of microservices is to scale a system, or to scale an engineering organisation. By breaking down monoliths into independent domains, teams can reduce cognitive complexity and release software hundreds of times a day. Sarah warns however that this shift introduces a "maintenance treadmill", requiring robust automation for library upgrades, security patching, and cross-service governance to prevent a sprawl of unmanageable tech debt. The conversation goes deep on human factors, and the role of organizational culture in technical success. Sarah emphasises the importance of a 'generative culture', as defined by Ron Westrum, where information is shared openly. By taking a 'you build it, you run it' approach supported by a strong platform team, organizations can balance individual team autonomy with the necessary guardrails for long-term operational health and efficiency.

    25 min

  5. May 27

    S3E3: Matthew Skelton

    Team Topologies co-author Matthew Skelton joins Chris and Nick to discuss the relationship between tech debt and cognitive load. Matthew explains that every organization has a finite "cognitive load budget," and technical debt acts as a constant drain on this capacity, creating friction that slows down development and increases operational risk. By framing tech debt in terms of human cognition and team boundaries, the discussion highlights how healthy software evolution depends on keeping systems understandable and manageable for the people who build and run them. The discussion also explores practical tools and frameworks for visualizing and communicating these sociotechnical concepts to non-technical stakeholders. The episode concludes with a (prescient) forward-looking warning about AI generated tech debt. While generative AI promises increased engineering productivity, it risks creating a tidal wave of tech debt. That could potentially be orders of magnitude greater than what humans produce, if the resulting code is not properly understood or maintained.

    26 min

  6. May 20

    S3E2: AI Coding Assistants

    Chris and Nick chat about how AI Coding Assistants have suddenly become very good, maybe even good enough to write 'load bearing' code. So the constraints the existed around producing code have potentially disappeared; but other constraints live on, and we're still finding out what this means for product management. Some time is spent on whether languages and frameworks still matter, and it seems they do. But also guardrails matter, and if you already have good guardrails built in to a progressive delivery system you're likely better placed to (safely) take advantage of new tools. Show notes (links) Tracy Bannon, QCon London 2024 - Applying AI to the SDLC: New Ideas and Gotchas! - Leveraging AI to Improve Software Engineering

    19 min

  7. May 13

    S3E1: Ralph Wiggum

    After a long hiatus, the Tech Debt Burndown podcast has returned for its third season. Chris Swan and Nick Selby kick off the season with a discussion of what Chris has called Tech Debt as a Service: how we’ve entered the slopper scene, and how we are working with our magical AI coding assistants.  We start with a discussion of multi-agent coordination frameworks, one of which is called Ralph Wiggum, and what's become termed as ralphing is basically sending the coding assistant into a loop until it has successfully met some success criteria in order to exercise the loop: It's kind of going round and round, and maybe doing lots of pretty dumb stuff, but it keeps trying, right? And you don't let it stop until it's got to where you want to go. You'll probably also define some kind of max loop criteria so you’re not just burning tokens all day long . . .

    20 min

  8. 10/03/2023

    S2E4-Gene Spafford

    The Tech Debt Burndown Podcast

    33 min
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Chris Swan and Nick Selby, two long-time technology industry and information security leaders, discuss issues related to reducing technical debt.

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