SUMM

More code is being written today thanks to AI tools, but there isn't a corresponding increase in product features or innovation.
The actual bottleneck isn't writing code; it's the other processes in software development like code reviews, mentoring, testing, debugging, and team coordination.
New AI tools can generate code quickly, but understanding, testing, and trusting that code is harder than ever.
The human overhead of meetings, planning, and communication often slows things down more than coding.

Typical bottlenecks include code reviews, knowledge transfer, debugging, and collaborative processes.
Improving the speed of writing code doesn't address these core issues.
The marginal cost of code generation is dropping, but the cost of integrating, reviewing, and maintaining code is increasing.

The speaker used a method of shipping projects quickly by hitting fake early deadlines, then using extra time for polish and bug-fixing.
Prototyping and rapid cycles of user feedback allowed for better, more user-focused products.
Initial fast builds let teams iterate on real user needs rather than theoretical requirements.
The real value is in quickly discovering what works and what doesn’t, not in building perfect products on the first attempt.

Traditional product cycles involve long steps: problem identification, user interviews, design, long specs, presentations, and eventual engineering.
This process can take 6-18 months before real work even begins, leaving teams at risk of building the wrong product.
Often, ideas get far in the process before it becomes apparent they're not useful or even harmful.

Instead of following the traditional linear process, the speaker advocates for rapid prototyping: identify, prototype, collect feedback, and repeat until a good solution is found.
Most time-consuming steps like early specs and presentations can be reduced or eliminated if a working prototype is available early.
Early demos make the process more efficient and better aligned with user needs.

AI coding tools should be used to empower rapid prototyping, not to simply build production code faster.
If used to generate early prototypes, these tools can save months or even years and quickly validate (or invalidate) product ideas.
Using AI tools to simply accelerate traditional processes doesn’t solve root problems and may even make reviews and team understanding harder.

There are two types of code: throwaway/prototype code meant for learning, and production code meant to be maintained.
Historically, the effort to write both kinds of code was similar, leading to over-engineering prototypes.
AI coding tools increase the risk of blending these two, but distinguishing their uses is crucial for productive development.
Throwaway prototypes should be fast and disposable; production code needs careful review and maintenance.

Rapid code generation increases the burden on reviewers and mentors, potentially stressing teams and slowing down quality assurance.
Shared context and trust within teams are more important than ever for managing fast-changing codebases.
LLMs (large language models) don't fix fundamentals like alignment, mentoring, or deep product understanding.

Prototypes can be much faster and cheaper to build and iterate on, allowing for quicker learning and reducing wasted effort on bad ideas.
AI tools should help teams get to critical product insights faster rather than just producing more code.
A smaller team iterating on a prototype is more effective and communicative than a large team locked into a massive, assumption-filled spec.

True productivity gains come from optimizing the time to learn new product insights, not from producing larger quantities of code.
AI tools are most beneficial when they shorten the feedback loop to user insights, not when they support outdated, spec-heavy processes.
Teams must distinguish between throwaway and production code, using AI to rapidly experiment but maintaining rigor and care for code meant for maintenance.
Shared understanding and clear communication remain the key bottlenecks in shipping software, regardless of how code is produced.

You're using AI coding tools wrong