The CTO's Ephemeral Approach

Why treating AI-generated code as disposable will rewire your brain for innovation

I'm sitting at my desk, staring at lines of code in my Cursor editor. It's 2 AM. My brain is fried. I've been trying to build an encryption application that uses some novel approaches I discovered in a research paper. Cursor, with its AI assistant, promises to be my partner in this digital dance.

"Generate a secure key derivation function that uses the Argon2id algorithm," I prompt.

The AI spits out some beautiful-looking code. It looks right. The comments are thorough. But when I try to run it, my terminal floods with red error messages.

ImportError: No module named 'argon2'

Of course. The AI generated code for a library I don't have installed. I run pip install argon2-cffi and try again.

AttributeError: module 'argon2' has no attribute 'argon2id'

Wait, what? I check the documentation. The AI suggested a function that doesn't exist in the current version of the library. It's referencing an older version or maybe a different library altogether.

This has been a night of wrestling with prompts and trying to build complex encryption systems with tools I've never used, in languages I'm barely familiar with. Coding feels like navigating a foreign country without a map, armed only with an AI translator that sometimes answers in riddles.

The most frustrating part isn't the coding itself. It's the environment setup, configuring paths to find these new AI-suggested libraries, installing the correct versions, and deciphering what the AI actually meant when it generated that beautiful but non-functional code.

I'm ready to slam my laptop shut and call it a night. But something stops me. I take a deep breath and look at what's happening with fresh eyes.

I'm not failing at coding. I'm failing at embracing the ephemeral nature of AI collaboration. I've been treating it like a magic code genie when what I need is an entirely new mindset.

I clear my terminal, open a new file, and start again, but this time, I change my entire approach.

The Ephemeral Mind

What I discovered that night changed my entire relationship with AI coding assistants. The breakthrough didn’t come from refining prompts or crafting more specific requests. It came from embracing the ephemeral nature of AI-generated code and ideas.

The core revelation: AI-assisted coding requires a completely different mental model, one built on ephemeral thinking, contextual depth, and true partnership.

Most programmers approach AI tools with permanence in mind. We want them to be perfect coding machines that translate our vague ideas into flawless, production-ready code. When they fail, we blame the AI. "This thing is useless," we mutter, before returning to our traditional methods.

But what if the problem isn't the AI? What if it's our refusal to embrace the ephemeral?

Nothing Is Permanent, Especially Code

Traditional programming culture values permanence. We build systems that last for years. We carefully craft architectures, optimize for performance, and create maintainable codebases.

AI-assisted coding demands we embrace impermanence. The code it produces should be viewed as sand, not stone – something to be reshaped with each tide, or washed away entirely when necessary.

I've spoken with dozens of CTOs who rejected AI coding assistants after brief trials. Almost universally, they approached the tools with rigid expectations: "Generate a perfect authentication system" or "Build me an API for user management."

When the AI produced code with bugs or misunderstood requirements, they concluded the technology wasn't ready. They were right about the output, but wrong about the approach.

Research from Stanford's AI Index Report shows that while code generation has improved dramatically, it still performs best when treated as a collaborative partner in an iterative process rather than a one-shot solution generator.¹

The Plastic Brain

Neuroscience has long shown that our brains are shaped by repetition; each habit reinforces neural pathways until they become default modes of thinking. Dr. Michael Merzenich, a pioneer in neuroplasticity, put it succinctly: “The brain changes physically, functionally, and chemically, as you acquire any ability or skill.”² In other words, the way you think becomes the way you keep thinking.

This is precisely why working with AI coding assistants can feel so strange, especially for experienced programmers. It demands a different kind of cognition. Instead of marching linearly from problem to solution, developers must adapt, iterate, and co-create with a non-human intelligence. Writing code has evolved into something more fluid—an act of shaping thought in motion.

In the emerging field of LLMology—the study of how humans interact with large language models—researchers are beginning to see these tools as cognitive partners, not just productivity hacks. Early findings suggest that the constant back-and-forth with AI can nudge the brain toward greater cognitive flexibility and open-ended exploration.

No wonder seasoned developers often feel off balance. They’re bringing decades of hardened, deterministic logic to a medium that rewards plasticity and play. It’s like trying to build a sandcastle with concrete—great intentions, wrong material. The future of coding may not belong to those who know the most, but to those most willing to unlearn.

The Ephemeral Protocol

After my late-night epiphany, I developed what I call the "Ephemeral Protocol" for AI-assisted coding. It consists of five principles that have transformed my effectiveness:

1. Hold Everything Lightly

Every piece of code the AI generates is temporary. Even code that works perfectly today might be discarded tomorrow as requirements evolve or better approaches emerge. What looks like failure is actually part of the collaborative process.

I've learned to value the learning process over the code itself. Sometimes, the most valuable output isn't the code but the understanding I gain by working through different approaches with the AI.

2. Context Is Everything

AI lacks the years of experience and implicit knowledge that human programmers accumulate. What seems obvious to you isn't obvious to the AI.

I now spend more time explaining context than specifying code.

Instead of:

"Write a function to validate user passwords"

I say:

"We're building a banking application that requires extremely secure passwords. Write a function to validate user passwords that enforces at least 12 characters, a mix of uppercase and lowercase letters, numbers, special characters, and rejects common password patterns and dictionary words. The function should return specific error messages explaining which requirements weren't met."

The difference in output quality is remarkable.

3. Deep Understanding Over Skimming

Many programmers skim AI-generated code, looking for obvious errors. This surface-level review misses subtle issues and learning opportunities.

I've learned to read AI-generated code line by line, understanding not just what it does but why the AI chose that approach. This careful reading reveals assumptions the AI made and often teaches me techniques I wouldn't have considered.

4. Test-Driven Ephemerality

The most powerful technique I've discovered is having the AI generate tests before implementations. This creates a stable foundation in an otherwise ephemeral process.

I ask the AI to:

1. Write comprehensive tests for the functionality

2. Explain what each test is verifying

3. Only then implement code that passes those tests

The results are remarkable. The tests serve as a detailed specification, ensuring both the AI and I are aligned on expectations. When the implementation passes all tests, there's a satisfying certainty that we've built what we intended, at least for now.

5. Sleep On It

Some of my best AI collaborations span multiple days. I'll work with the AI to generate a solution, then sleep on it. Often, my subconscious mind identifies issues or improvements that weren't obvious during the initial session.

This deliberate pause leverages our brain's background processing capabilities. According to sleep researchers, our brains continue to work on problems while we sleep, often making new connections that weren't apparent during conscious thought.

Neural Flexibility

There's compelling scientific evidence that this ephemeral approach enhances effectiveness and is beneficial for your cognitive health.

Dr. Lara Boyd, a brain researcher at the University of British Columbia, explains that challenging our brains with novel approaches creates new neural connections and strengthens existing ones. "When we force ourselves to think differently about familiar problems, we're actually rewiring our brains," she notes.³

Working with AI coding assistants requires constant adaptation. No two interactions are identical, and the process demands flexibility in how we formulate problems and interpret solutions.

A 2022 study from the Max Planck Institute found that professionals who regularly engage with AI systems showed enhanced cognitive flexibility compared to peers who didn't. The researchers concluded that "the unpredictable nature of AI interactions forces the brain to maintain a state of adaptive readiness, potentially slowing cognitive decline."⁴

This aligns with broader research on brain health. Neurologists have long observed that people who continuously learn new skills and adapt to changing environments tend to maintain cognitive function longer as they age.

The Skeptics' Challenge

I can hear the objections already. Two CTOs sit across from me, arms crossed, eyebrows raised.

"This ephemeral approach sounds inefficient," says the first. "Why waste time on code you might throw away?"

It's a reasonable concern. But in the ephemeral approach, nothing is truly wasted. Every iteration builds understanding, even if the code itself is discarded. This understanding makes future iterations faster and more effective.

Think of it like sketching before painting. Artists don't consider preliminary sketches a waste, even though they're ultimately covered or discarded. They're an essential part of the creative process.

"What about security and reliability?" challenges the second. "How can ephemeral code be trusted in production?"

This highlights the importance of the test-driven aspect of the protocol. Tests provide stability amidst the ephemerality. By defining clear expectations through tests, we ensure that even as implementations change, the core functionality and security properties remain intact.

Additionally, the ephemeral approach actually enhances security by encouraging multiple implementations. Instead of becoming attached to a single approach that might contain hidden vulnerabilities, we explore diverse solutions, potentially identifying more robust options.

The Future-Flexible CTO

The landscape of software development is changing rapidly. According to GitHub's Octoverse report, over 41% of code on their platform now involves some form of AI assistance. This percentage is growing every quarter.⁵

CTOs who cling to permanent thinking risk being left behind. Those who embrace ephemerality without structure risk chaos. The balanced approach, embracing ephemerality while maintaining core principles, positions you and your team for whatever comes next.

The real power of the ephemeral approach lies in its adaptability, not just its productivity. Every interaction with an AI coding assistant exercises your ability to think flexibly, communicate clearly, and evaluate critically. These skills transcend any particular technology or trend.

Neuroscientist Dr. David Eagleman has extensively studied how novelty promotes brain health: "The brain requires novelty to stay sharp. New experiences create new neural connections, while repetition can lead to cognitive stagnation."⁶

In a field that changes as rapidly as technology, cognitive flexibility isn't just beneficial – it's essential.

Your First Ephemeral Experiment

Ready to start your journey with the ephemeral approach? Here's how to begin:

1. Pick a side project – Choose something interesting but non-critical for your first deep collaboration

2. Set ephemeral expectations – Remind yourself that you'll likely discard most of what you create

3. Practice contextual prompting – Provide rich background and requirements

4. Try the test-first approach – Have the AI generate tests, then implementations

5. Be willing to sleep on it – Give your brain time to process between sessions

6. Document your learning, not just your code – What patterns are you noticing?

7. Reflect on your adaptability – How comfortable are you becoming with change?

The most important thing is to approach the process with curiosity. You're not just learning a tool; you're developing a new way of thinking about programming itself.

As for me, I eventually finished my encryption application. It took longer than I expected, but the solution was more elegant and robust than what I would have built alone. More importantly, I understand encryption techniques now that would have taken months to learn through traditional means.

The code we create with AI may be ephemeral, but the neural pathways we form are lasting. And in a field that changes as rapidly as ours, these flexible pathways – and the ability to constantly form new ones – are our most valuable asset.

So, are you ready to embrace the ephemeral?

1. https://hai.stanford.edu/ai-index/2025-ai-index-report

2. https://pmc.ncbi.nlm.nih.gov/articles/PMC8383338/

3. https://www.interact123.com/post/neuroplasticity-changing-brains

4. https://maxplanckneuroscience.org/artificial-intelligence-from-a-psychologists-point-of-view/

5. https://www.elitebrains.com/blog/aI-generated-code-statistics-2025#:~:text=AI%20tools%20are%20writing%20massive%20amounts%20of,GitHub%20Copilot%20have%20become%20in%20developers'%20workflows.

6. https://brainworldmagazine.com/exploring-the-brain-changing-magic-of-novelty/