Month: August 2025

AI, Paradigm Shifts, and the Future of Building Companies

Over the past few months, I have been constantly reading conversations about how Generative AI will reshape software engineering. On LinkedIn, Twitter, or in closed professional groups, engineers and product leaders debate how tools like Cursor, GitHub Copilot, or automated testing frameworks will impact the way software is built and teams are organized.

But the conversation goes beyond just engineering practices. If we zoom out, AI will not only transform the workflows of software teams but also the structure of companies and even the financial models on which they are built. This kind of change feels familiar – it echoes a deeper historical pattern in how science and technology evolve.

Kuhn’s Cycle of Scientific Revolutions

During my bachelor’s, I read Thomas Kuhn’s The Structure of Scientific Revolutions. Kuhn argued that science does not progress in a linear, step-by-step manner. Instead, it moves through cycles of stability and disruption. The Kuhn Cycle1, as reframed by later scholars, breaks this process into several stages:

  1. Pre-science – A field without consensus; multiple competing ideas.
  2. Normal Science – A dominant paradigm sets the rules of the game, guiding how problems are solved.
  3. Model Drift – Anomalies accumulate, and cracks in the model appear.
  4. Model Crisis – The old framework fails; confidence collapses.
  5. Model Revolution – New models emerge, challenging the old order.
  6. Paradigm Change – A new model wins acceptance and becomes the new normal.

The Kuhn Cycle Applied to Software Development

Normal Science

For decades, software engineering has operated under a shared set of practices and beliefs:

  • Clean Code & Best Practices – DRY, SOLID, Unit Testing, Peer Reviews.
  • Agile & Scrum – Iterative sprints and ceremonies as the “right” way to build products.
  • DevOps & CI/CD – Automation of builds, deployments, and testing.
  • Organizational Structure – Specialized roles (frontend, backend, QA, DevOps, PM) and a belief that more engineers equals more output.

The underlying assumption is hire more engineers + refine practices → better and quicker software.

Model Drift

Over time, cracks began to show.

  • The talent gap – demand for software far outstrips available developers.
  • Velocity mismatch – Agile rituals can’t keep pace with market demands.
  • Complexity overload – Microservices and massive codebases create systems that are too complex for a single person to comprehend fully.
  • Knowledge silos – onboarding takes months, and institutional knowledge remains fragile.

These anomalies signaled that “hire more engineers and improve processes” was no longer a sustainable model.

Model Crisis

The strain became obvious:

  • Even tech giants with thousands of engineers struggle with code sprawl and coordination overhead.
  • Brooks’ Law bites – adding more people to a project often makes it slower.
  • Business pressure grows – leaders demand faster iteration, lower costs, and higher adaptability than human-only teams can deliver.
  • Early AI tools, such as GitHub Copilot and ChatGPT, reveal something provocative – machines can generate boilerplate, tests, and documentation in seconds – tasks once thought to be unavoidably human.

This is where many organizations sit today – patching the old paradigm with AI, but without a coherent new model.

Model Revolution

A new way of working begins to take shape. Here are some already visible in experimenting, we can all see around us –

  • AI-first engineering – using AI agents for scaffolding code, generating tests, or refactoring large systems. Humans act as curators, reviewers, and high-level designers.
  • Smaller, AI-augmented teams
  • New roles and workflows – QA shifts toward system-level validation; PMs focus less on ticket grooming and more on problem framing and prompting.
  • Org structures evolve – less siloing by specialization, more “AI-augmented full-stack builders.”
  • Economics shift – productivity is no longer headcount-driven but iteration-driven. Cost models change when iteration is nearly free.

Paradigm Change

In the coming years, some of the ideas above, and probably additional ideals, could stabilize as the “normal science” of software development and organizational building. But we are not yet there. Once we get there, today’s experiments will feel as obvious as Agile sprints or pull requests do now.

We are in the midst of model drift tipping into crisis, with glimpses of revolution already underway. Kuhn’s lesson is that revolutions are not just about better tools – they’re about shifts in worldview. For AI, the shift might be that companies will no longer be limited by headcount and manual processes but by their ability to ask the right questions, frame the correct problems, and adapt their models of value creation.

We are moving toward a future where the shape of companies, not just their software stacks, will look radically different, and that’s an exciting era to be a part of.

  1. https://www.thwink.org/sustain/glossary/KuhnCycle.htm ↩︎

LLM Debt: The Double-Edged Sword of AI Integration

Have you noticed how half the posts on LinkedIn these days feel like they were written by an LLM – too many words for too little substance? Or how product roadmaps suddenly include “AI features” that nobody asked for, just because it sounds good in a pitch deck? Or those meetings where someone suggests “let’s use GPT for this,” when a simple SQL query, an if-statement, or a much simpler ML model would do the job?

Laurence Tratt recently coined the term LLM inflation to describe how humans use LLMs to expand simple ideas into verbose prose, only for others to shrink them back down. That concept got me thinking about a related phenomenon: LLM debt.

LLM debt is the growing cost of misusing LLMs — by adding them where they don’t belong and neglecting them where they could help.

We’re all familiar with technical debt, product debt, and design debt1 — the shortcuts or missed opportunities that slow us down over time. Similarly, organizations are quietly accumulating LLM debt.

So, what does LLM debt look like in practice? It’s a double-edged liability:

  • Overuse: Integrating LLMs where they’re unnecessary adds latency, complexity, cost, and stochasticity to systems that could be simpler, faster, and more reliable without them. For example, sending every API request through a multimillion-parameter model when a simple regex or deterministic logic would suffice.
  • Underuse: Failing to adopt LLM-based tools where they could genuinely help results in wasted effort and missed opportunities. Think of teams manually triaging support tickets, writing repetitive documentation, or analyzing text data by hand when an LLM could automate much of the work.

Like product or technical debt, a small amount of LLM debt can be strategic: it allows experimentation, faster prototyping, or proof-of-concept development. However, left unmanaged, it compounds, creating systems that are over-engineered in some areas and under-leveraged in others, which slows product evolution and innovation. Same as other types of debt, it should be owned and managed.

LLMs are powerful, but they come with costs. Just as we track and manage technical debt, we need to recognize, measure, and pay down our LLM debt. That means asking tough questions before adding LLMs to the stack, and also being bold enough to leverage them where they could provide real value.

If LLM inflation showed us how words can expand and collapse in unhelpful cycles, LLM debt shows us how our systems can quietly accumulate inefficiencies that slow us down. Recognizing it early is the key to keeping our products lean, intelligent, and future-ready.

  1. I previously wrote about some of those topics here and talked about them in Hebrew here ↩︎

Things I recently built with my daughter

My daughter just finished first grade and is about to turn seven. One day, she noticed me playing a math puzzle on a news site and asked if she could join. I told her that when an easy one came along, I’d be happy to solve it with her.

A few days passed. She asked again.

I didn’t want to say no, so I offered to sketch a similar game on paper. But designing a good puzzle turned out to be more challenging than expected. I scribbled a lightweight version instead—and that sparked an idea. I asked if she’d like to build a game together.

1. Math Grid

🔗 Play Math Grid

This was our first game. It’s a visual puzzle: players are given a grid with some numeric constraints on rows and columns, and the goal is to fill it in correctly. Inspired by logic puzzles like Sudoku and Kakuro, the constraints are simplified for early elementary-level arithmetic, so it stays approachable while still encouraging reasoning.

2. Number Detector

🔗 Play Number Detector

Later, while solving exercises from her school workbook, we came across number riddles. This inspired our second app. In Number Detector, the player is given partial clues (like a number’s sum of digits or a multiple constraint) and has to figure out the correct number from a limited set.

We added randomness and regeneration to keep things fresh, making the practice repeatable and varied.

3. Triangle Tactics

🔗 Play Triangle Tactics

A month later, while playing a pen-and-paper strategy game, she casually said: “Let’s make it into an app—like we did in the old days.” 🙂

Her request blew my mind.

We built Triangle Tactics, a simple turn-based game where players connect dots to form triangles, and the player who forms the most triangles wins. What surprised me most was the technical challenge—not in the game logic, but in rendering the dots and lines correctly. It was solved only after switching to a different LLM model than the default.

Reflections

These experiments turned into more than just games. They became opportunities to:

  • Understand the importance of English: We worked in English—despite it not being our native language—and talked about why fluency matters, especially when working with tools, prompts, or documentation.
  • Explore computers as collaborators: “What do you mean it misunderstood the prompt?”, “How should we ask to get the result we want?”
  • Practice QA thinking: “Why isn’t this button working?”, “Is it behaving the way we expected?”
  • Enjoy quality time: A fun excuse to step outside our usual routines and build something together.