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Software engineer and content creator Theo drops some uncomfortable truths about AI coding tools that most developers aren’t ready to hear. While everyone’s obsessing over how fast AI can generate code, they’re completely missing the point. Theo challenges the fundamental assumption driving the AI coding revolution and reveals why traditional software engineering processes are fundamentally broken, regardless of how fast you can type.

Key Insights

  • Code was never the bottleneck in software development - the real constraints are code reviews, mentoring, testing, debugging, and human coordination processes
  • AI tools should optimize for learning velocity and “time to next realization” rather than just code generation speed, focusing on rapid insight discovery
  • Teams must distinguish between throwaway prototyping code and production code, using different quality standards and workflows for each type
  • Traditional waterfall development cycles of 6-18 months can be compressed to weeks through AI-powered rapid prototyping and continuous user feedback loops
  • Small teams consisting of 1 engineer, 1 designer, and 1 PM using AI prototyping can outmaneuver large teams stuck in traditional development processes
  • The real competitive advantage is failing fast and learning what to build before investing months in development, not building faster once requirements are known
  • Professional developers spend only 32% of their time writing new code, with 68% consumed by maintenance, meetings, testing, and operational tasks
  • AI-generated code increases review burden because someone must understand, test, and take responsibility for code they didn’t write
  • Teams generate more code than ever with AI tools but often move slower overall due to drowning in review overhead for unfamiliar code

The Code Generation Myth

Theo observes that developers are writing significantly more code today than a few years ago due to AI tools, but engineering teams aren’t shipping more features or building better products. This disconnect reveals a fundamental misunderstanding about software development bottlenecks.

Research supports Theo’s observation: a 2019 Tidelift survey of nearly 400 professional developers found they spend only 32% of their time writing new code or improving existing code. The remaining 68% breaks down as 35% managing code (maintenance, testing, security), 23% in meetings and operational tasks, and 10% on other activities.

The bottleneck was never typing speed or code generation capability. Teams spend most of their time on code reviews that drag on for days, debugging sessions that stretch for hours, coordinating with other teams, and understanding requirements that change frequently throughout development cycles.

In larger organizations with over 500 developers, maintenance activities consume even more time (32% vs 19%), suggesting the problem compounds as teams scale. Raw code generation represents a small fraction of total development time and effort.

Human Processes Are the Real Bottleneck

The actual constraints in software development are code reviews, knowledge transfer through mentoring and pairing, testing, debugging, and human coordination overhead. Theo draws from his experience at Twitch fixing projects that fell behind deadlines, noting that delays were never about coding speed.

Traditional processes designed to drive quality often slow teams down more than the act of writing code itself. Tickets, planning meetings, and agile rituals create layers of coordination overhead that dwarf the time spent on actual implementation.

The marginal cost of adding new software approaches zero with AI tools, but the price of understanding, testing, and trusting AI-generated code is higher than ever. When AI generates 1,000 lines of code in 10 minutes, someone must still review those lines, understand their implications, and take responsibility for long-term consequences.

This creates a paradox where teams generate more code than ever but move slower overall because they’re drowning in review overhead for code they didn’t write and don’t fully understand. The review burden actually increases rather than decreases with AI assistance.

AI’s Sweet Spot: Rapid Prototyping

Theo advocates optimizing for insights and learning velocity: “How quick can you go from an assumption to a new learning?” This approach compressed traditional 6-18 month development cycles into weeks at Twitch.

Traditional waterfall process follows: Research → Design → Spec → Build → Ship → Pray → Deprecate (6-18 months). Theo’s process: Identify → Prototype → Collect feedback → Repeat until good → Spec → Build → Ship (weeks).

One engineer, one designer, and one PM working part-time on rapid prototyping can save months or years of work by validating ideas before full development. Building rough prototypes in 3 days to 2 weeks and showing them to actual users either kills bad ideas entirely or creates much better specifications based on real feedback.

The real value isn’t in the code itself but in how quickly teams can validate or invalidate assumptions before investing months in the wrong direction. Most product failures happen because teams spend months building features users don’t want, not because they couldn’t code fast enough.

The Throwaway vs Production Code Problem

AI has created a dramatic cost difference between throwaway code and production code, but most developers still treat all code the same way. Throwaway code includes scripts for benchmarks, sandboxes, and prototypes for new features. Production code encompasses core infrastructure, libraries powering millions of applications, and features built by large teams.

Previously, the cost difference between these two approaches wasn’t significant. Now AI has made throwaway code incredibly cheap to generate, but teams must develop discipline to distinguish between rapid prototyping and production development workflows.

The skill becomes knowing when to keep AI-generated code and when to start fresh with proper architecture for production systems. Many developers spin up enterprise-level infrastructure stacks for simple side projects, applying production standards where throwaway approaches would be more appropriate.

Rapid cycling between throwaway prototypes and production builds allows teams to use each approach for different purposes: prototypes for learning and validation, production code for scalable, maintainable systems.

The Typewriter Problem

Legal history provides a perfect analogy for AI coding tools. Laws became dramatically longer after three key innovations: the typewriter, word processor (copy-paste functionality), and now AI generation capabilities.

Once generating content becomes easier, organizations end up with much more of it. But if the problem wasn’t the amount of content but the quality, increased generation capacity actually makes things worse.

The same pattern appears with AI coding tools. Teams generate massive amounts of code but don’t necessarily create better software. More code does not equal better applications - often it means the opposite through increased complexity and maintenance burden.

AI tools can be described as “Google searching Stack Overflow and copy-pasting code on steroids.” If a team’s instinct is to solve problems by adding more code, AI will supercharge that counterproductive habit rather than addressing underlying process issues.

Optimizing for Learning, Not Output

The strategic insight involves changing success metrics from code output or feature velocity to learning velocity. Teams should use AI tools to gain more insights and achieve more breakthrough moments during development, not to speed through processes without gaining understanding.

If building anything takes months, teams shouldn’t build uncertain features. If building a prototype takes 6 months, the team isn’t actually building prototypes but full implementations without validation.

Theo’s approach embraced throwing away weeks of work if it generated valuable learning. Building and validating (or invalidating) ideas in days makes discarding code a feature rather than a failure, as long as the process produces actionable insights.

Organizations that understand this principle will have enormous advantages over those drowning in AI-generated complexity without corresponding learning improvements. The focus shifts from generating more code to discovering what should be built before building it.

Key Quotes

”The harsh reality is that code was never the bottleneck."

"The marginal cost of adding new software is approaching zero, especially with LLMs. But what’s the price of understanding, testing, and trusting the code? Higher than ever."

"We should be optimizing for insights. How quick can you go from an assumption to a new learning?"

"If it takes you 6 months to build a prototype, you’re not building prototypes."

"I think it’s fair to say that a lot of people are writing way more code today than they were a few years ago. But I don’t feel like I’m seeing a lot more shipping happening."

"It almost feels like there’s two types of code where we have throwaway code and we have production code.”