I like being critical of things. While some people might tell me to "stop being so negative," I think critical thinking and analysis of existing paradigms is a prerequisite to progress that moves us in the right direction.
One of my current bugbears is how we as a society are approaching the development and use of AI. The TLDR: irresponsibly.
The Acceleration Paradox
Scroll through any social media platform and you'll be immediately pummelled with a particular set of marketing messages relating to AI and Software Engineering.
"I vibe coded my way to $10,000 MRR in 2 weeks"
"I've built more in the last month than I built in the last 2 years"
"Use these methods to 10x / 100x your work rate"
"I'm vibe coding apps through my phone while waiting in line at the grocery store"
"AI does all my marketing"
"I used AI to copy this person's website and business, and now am making almost as much money as them."
"We replaced 10 junior devs on our 20-dev team. We now have 10 devs doing the work that 20 did last year."
The list goes on (the above are all real tweets I have seen), and hey, for some people, maybe these claims are true.
I have some criticisms.
Many seem unrealistic. If you 10x your work rate, that means you can do almost 3 months' worth of work in a week. If you 100x it, you do a whole year's work in that week.
While some of these claims sound good, the reality is probably less amazing than it sounds. I read an article last week about how someone used AI to completely redesign their portfolio site. Wow, I thought. That sounds great. I looked at his website. It sucked.
I Love AI, but I Hate AI Slop
And I'm sorry, but most "AI Experts" don't seem to be able to construct a simple prompt to make their AI-generated slop at least seem vaguely human.
I've read all the Twitter and LinkedIn posts by people in AI who are using AI to "help them produce content." It all looks, sounds, and feels the same. Annoying, difficult to read, and PREDICTABLE. Language models work by generating the next most probable token — the central point of a statistical "what comes next" distribution. So, literally, predictable.
Other claims seem morally bankrupt. I wouldn't be advertising the firing of half of your employees in favour of AI. I'd be ashamed. Another trend that's recently arisen is "cloning someone else's entire SaaS" — copying someone's business and then using AI to try and steal their clients. Call me old fashioned, but that seems downright dishonest.
But marketing is as marketing does, and it's designed to do two things:
- Make you feel inadequate,
- Sell you something that promises to fill that void.
In the 1990s, Greed was Good. Now in the 2020s, Speed is Good.
We don't know where we're going, but damn, at least we're getting there fast!
With people making bold (and premature) claims about Artificial General Intelligence, I think we're gradually starting to smell the difference between marketing hype and reality.
Acceleration as the Product
Those heavily promoting AI because their livelihood depends on it — the big AI companies and those "selling shovels" (the AI tutorial companies) — are promising one thing: increased velocity between where you're at and where you want to be.
It will take less time, do more of the work, and happen faster, if you use <insert AI product here>.
Meanwhile, the people building these tools are telling a very different story behind closed doors. Helen Toner, former OpenAI board member, testified before the US Senate that the companies' own position amounts to this:
"They're saying: we don't have good science of how these systems work or how to tell when they'll be smarter than us. We don't have good science for how to make sure they won't cause massive harm. But don't worry — the main factors driving our decisions are profit incentives and unrelenting market pressure to move faster than our competitors."
Former Facebook/Meta civic integrity researcher David Evan Harris, testifying at the same hearing, described how Big Tech companies think about regulators using what they call "The Bear Metaphor":
"You senators are the bear. The tech companies are people running away from the bear. The moral of the story: just don't be the second slowest. As long as you can point to another tech company doing a worse job on safety, that is the optimal allocation of resources."
I have a few issues with this whole thing.
- It's becoming clear that AI may not actually provide the promised acceleration.
- Even if it does eventually — is moving at that faster pace even desirable?
- Just because you can copy someone's business, their image, style or voice, or use a product of their work without permission — should you?
- What are the consequences of this "Speed is Good" bandwagon — for code quality, for online literature, for copyright, for human satisfaction, for economic equality, for the concentration of power in the hands of the few, for people's privacy and rights to their own innermost thoughts?
- What kind of society are we creating, through the process we're using to adopt this technology?
AI May Not Actually Be Making You Faster
...Unless You Can't Actually Code
Although most people think AI is making them faster at coding, the truth is much more complex.
AI is great for self-titled Vibe Coders. They couldn't code before, they can't code without AI, but now they can. All they need to do is throw tokens at the problem and eventually, the AI will brute force a product.
The gap between product idea and implementation has been closed, but whether these products will last beyond the initial marketing push, only time will tell. Some will make enough money in the first few months that long-term survival becomes irrelevant.
Or maybe the quality of coding assistants will have improved so much by the time the vibe-coded technical debt reaches critical mass that they'll just send in the next generation of agents, who will magically fix everything the first round built up.
"When People Report That AI Has Accelerated Their Work, They Might Be Wrong"
The METR Study (2025): The most rigorous examination to date recruited 16 experienced open-source developers working on large, mature codebases that they were familiar with.
- Developers using AI tools completed tasks 19% slower, despite predicting a 24% speedup and believing post-hoc that they were 20% faster.
- 56% had to make major modifications to clean up AI-generated code.
Likely friction points:
- Time spent prompting and waiting — Loss of flow state
- Code review overhead — AI struggles with large, complex codebases, and poor code quality demands more review time
- Context limitations — AI lacks deep understanding of architectural decisions and struggles to maintain big-picture context
- Integration complexity — Generated code not matching team standards
AI company Faros' telemetry from over 10,000 developers reports that alongside boosted individual output, massive AI adoption also produced:
- A 9% climb in bug rates
- A 91% increase in code review time
- A 154% increase in pull request size
Mario Zec, creator of the Pi coding agent harness, has a visceral way of framing this. He calls agent-introduced errors "boooos" and points out the fundamental asymmetry: with one human, error rate is bounded by human capacity. Add 10 agents and errors compound serially with zero bottlenecks and delayed pain — delayed because it's YOUR pain, not the agent's. The agent will happily keep going.
"Agents are compounding boooos with serial learning, no bottlenecks, and delayed pain. The delayed pain is for you."
"Here's your codebase on one human, one agent, and 10 agents. How much of the agent code can you review? Then you say, 'Oh, I have a review agent.' Doesn't work. It catches some issues."
(Mario Zec, "Building Pi in a World of Slop")
The Engineering Productivity Paradox
AI accelerates feature delivery but simultaneously increases:
- Code duplication (massive surge in 2024)
- Technical debt accumulation
- Cyclomatic complexity (harder to maintain code)
- Security vulnerabilities (40% of AI-generated code contains them)
"This massive boost in feature delivery speed is now a competitive imperative for top-tier organizations. However, this acceleration introduces a fundamental risk: AI's ability to generate functional snippets instantaneously creates a structural incentive for developers to accept quick, duplicated solutions over thoughtful architecture."
The theory emerging from this data is that 2025 was the inflection point where technical debt started accumulating exponentially, and that 75% of tech decision-makers will face moderate-to-severe technical debt by 2026.
One of the causes: LLMs prioritize "local functional correctness over global architectural coherence and long-term maintainability."
Mario Zec explains why this happens at the model level:
"Agents and models have learned complexity. Where did they learn that complexity from? From the internet. What's on the internet? All our old garbage code. 90% of code on the internet is our old garbage. And every decision of an agent is local, especially if the codebase is so big that it doesn't fit into its context."
"So you get enterprise grade complexity within two weeks with just two humans and 10 agents. Congratulations."
The Sonar research cites an "8-fold increase in frequency of code blocks containing five or more duplicated lines." AI is bloating codebases worldwide, making them difficult to maintain and read. AI clearly struggles to generalise and abstract in a way that skilled developers do naturally.
"They think they've found a machine that produces 10,000 times more code than a human. They're wrong. They've found a machine that produces liability at 10,000 times the rate of any human programmer."
People are starting to see the drawbacks. While "10x-ing" the number of lines of code generated seems fun, what isn't acknowledged is the long-term impact on code review, maintainability, and the increased workload on codebase maintainers dealing with higher volumes of low quality contributions. (Source)
Conversely, what makes a good Software Engineer is how few lines of code they write, not how many. (Source)
Questioning Acceleration: Some Things Can't (or Shouldn't) Be Done Fast
The Bottleneck That Saves You
Here's a counterintuitive idea worth sitting with: the human bottleneck is a feature, not a bug.
Mario Zec:
"Humans are horrible, failable beings, but they can learn and they are bottlenecks. There's only so many boooos they can add to your codebase on a daily basis. And humans feel pain. Once there's too much pain, the human has options: quit their job, blame somebody else and make them fix it, or everybody bands together and starts refactoring the garbage codebase. Agents will happily keep shitting into your codebase."
Margaret Mitchell, former Google AI ethics researcher and Chief Ethics Scientist at Hugging Face, testified before the US Senate about the structural problem from the inside of these companies:
"If you focus on safety and ethics, your promotional velocity is much less compared to your peers. You're less likely to become a leader at the company. By focusing on safety and ethics, you remain at the lower levels and can't fundamentally shape the company for the better."
Pain is a signal. Friction is a signal. Remove them, and you remove the correction mechanism.
"I Know Kung Fu"
Like everybody else, I also liked that bit in the Matrix when Neo gets plugged into the quick-upload skills programme and comes back 5 seconds later with that clever "I Know Kung Fu!" quip.
And maybe that's the future of learning, after Elon puts chips in all of our brains. But for now, learning takes time. It takes iteration. It takes focus and concentration, and struggling through examples of things that seem simple but that your brain can't — for some annoying reason — understand.
(That's why my journey to learn Software and AI Engineering is now 2 years and counting.)
There's a reason university degrees take between 3 and 5 years. To learn something deeply, to acquire expertise, takes time and a boatload of energy.
The complexity of Software Engineering has historically made apprenticeship and ongoing training of juniors important. (Source)
The Feeling of Falling Behind
Recently Andrej Karpathy, hero to many, posted that even he felt that things were moving too fast to keep track of. This should have set off warning bells in any sane person in the tech industry.
If someone with this much raw intellect, experience and knowledge is feeling this, how are we mere mortals supposed to feel? This is the guy who released a tutorial sequence where he literally re-built an early version of GPT by hand.
The subtext of a lot of what's being said on Tech Twitter at the moment is "if everything you do isn't in constant acceleration, you're falling behind."
What's so damaging about this idea is that it makes you think spending an hour or two on figuring out a relatively simple bit of code is a waste of time and a failure — because you could be vibe-coding 2,000 lines per hour instead.
That's just not how learning and producing things works. Things take time. I've spent longer than I care to say on this particular article. Will anyone ever read it? Who knows? But this is a topic I care about and I want to take the time to do it justice.
And the benefit to me is that it helps me clarify my thinking, and flex my writing and thinking muscles.
Keep Calm and Carry On
Everyone who is on the Software / AI Engineering learning journey right now is going to have to do the hard yards of learning the underlying frameworks, just like everyone who came before us did.
If you just use AI 24/7 to generate everything, you won't build the underlying neurological capability to do the job you want to do.
Mario Zec makes the same point from a builder's perspective:
"That friction is the thing that builds the understanding of the system in your head, which is important. And it's also where you learn new things."
"If you do anything important, write it by hand. You can use a clanker to help you with that, but don't let it make the decisions for you — because all the decisions it makes are learned from the internet. And that friction is the thing that builds the understanding."
Fever Pitch
Once I realised that people are whipping themselves into a fever pitch unnecessarily, and encouraging me to do the same, it helped me understand that if I want to last in this game, I need to slow down and smell the roses.
I was then able to begin to really focus on what I was doing. I chose a specific task. I slowed my breathing down. I set a specific and measurable goal for that day. I focused my mind, and I began to make progress.
Is the AI Honeymoon Almost Over?
Traditionally, the honeymoon was a wonderful time where a newly-wedded couple got to put aside the realities of the commitment they'd made and live in the fantasy of their romance for a little while longer, before they have to go back to — and come to terms with — the day-to-day grind of their shared daily existence.
When the honeymoon's over and the reality of married life begins, the real work begins. Any illusions the pair have about each other must fade, and adapting to the realities of sharing one's life with another human being is crucial to preserve sanity.
The wife needs to accept that the husband snores and has a tendency to leave his dirty socks tucked behind the couch cushions. The husband quickly realises that his wife isn't always "in the mood" and has strong opinions about how the dishwasher should be loaded.
Lame metaphors aside — the point is, we're over the "wow it's amazing it's AI" phase, or at least some of us are, and now we need to adapt to its presence in our lives and start applying wisdom to the situation.
For myself, in this learning journey, I'm finding myself strongly drawn to reading longer-form human-written articles (like the long-form tutorials on Real Python, for example), rather than getting quick-and-dirty answers from AI in Google searches.
I want to read things that have been thought through, planned with a human brain, and described in a non-AI way. I've become highly sensitised to the standard AI grammar patterns. It's subtle, but the AI grammar and sentence structure has begun to truly grate on me.
AI as Accelerationism
AI multiplies what you're already doing. If what you're doing is kind of crap, the crapness will multiply.
Now that the "AI Honeymoon is over," it's become ever more important to curate your own online experience and choose sources of information carefully and thoughtfully.
I'm finding myself taking more time in the deliberation and analysis phase, before deciding whether or not to spend time reading something.
I'm finding that human-written material is more meaning-dense, whereas AI-written material has a kind of hollow feeling to it.
Quality over Quantity
AI is great at producing huge amounts of content, but not that great at producing high quality material.
And as someone who values — and has always valued — quality over quantity, in friendships, possessions, clothing, whatever, I'm getting increasingly irked by AI responses that contain too much fluff and repetition.
Beyond that, it seems difficult to prompt the thing to produce quality content. It's like it has to run around the issue 3 or 4 times to name it, rather than just pressing the meaning button and walking away (like a good human writer can do).
On the Devaluation of Human Effort and Intellect
One fallacy people seem to be falling into is that because AI can produce language, imagery, and code, humans' ability to do these things is now less valuable.
Your job is to produce words? Guess what? AI can produce words too! (You're fired.)
This is incredibly myopic thinking, itself — like AI — lacking nuance, morality and human value.
We should try to remember that the weakness of our system of capital is that it tends to value a single, or few, dimensions of success (profit up, expense down), despite the fact that for meaningful and rewarding existence, human experience requires and demands the balancing and fulfilment of many factors.
But AI seems to be making people forget these basic facts.
We created money and all these systems to have ways that we can work together as humans to build our world and share our lives together on the planet. If we want to replace the most basic things that humans do to regulate our existence — namely, our vocations — we are making a serious error of judgement and need to go and sit in a dark room and think about what we value and how we want our world to look.
You Must Fight to Protect Your Cognitive Functioning
Technology is doing damaging things to our brains. I thank God that I grew up without mobile phones, had to memorise my friends' phone numbers, manually dial them and exchange auditory human communication words to arrange meetings.
Mario Zec is scathing about peers who've outsourced their comprehension entirely:
"Those are my most beloved people: 'I don't even read the code anymore.' Congratulations. Something is broken and your users are screaming. So, who you going to call? Not yourself because you haven't read the code. So you're relying on your agents, but they are now also overwhelmed because the codebase is so humongous that there's zero chance they can get all the context they need to fix the issues."
"You cannot trust your codebase anymore — and also not your tests, because your agent wrote your tests. Good game."
Don't Pull the Ladder Up
A 2024 survey of hiring managers found that 70% of them believe that AI can do the jobs of interns. And 57% of those surveyed said they trust AI's work more than the work of interns or recent grads. If an AI can do it, why bother spending the time and energy teaching a student how to do the same thing?
The trends are clear:
- Entry level positions are harder to get, there are fewer of them, and a lot of this is because of AI.
- Companies increasingly expect senior developers to do — with AI — what juniors would have done.
- The perceived value of junior developers has plummeted.
This isn't just blog-post speculation. Rep. Eli Crane (R-AZ) presented data before the House Homeland Security Subcommittee, citing Dario Amodei, CEO of Anthropic:
"AI could wipe out half of the entry-level white collar jobs and spike unemployment 10 to 20% in the next one to five years. Other experts have said that number is closer to 50% in the next 5 years."
"AI companies and the government need to stop sugar coating what is coming — the possible elimination of jobs across technology, finance, law, consulting, and other white collar professions, especially entry-level gigs."
Even Steve Bannon, former top adviser to President Trump, was cited at the same hearing:
"I don't think anyone is taking into consideration how much administrative, managerial, and tech jobs for people under 30 — entry-level jobs that are so important in your 20s — are going to be eviscerated. And it's already happening."
Crane presented layoff data credited to AI and efficiency: IBM (8,000), McKinsey (5,000), Dell (6,000), Intel (15,000) — an estimated 214,000 total jobs at the time of testimony. His direct question to the AI builders in the room:
"Does it bother you at all that the technology you're developing is set to destroy tens of millions of jobs?"
Thankfully, industry leaders have seen past the hyperbole. AWS CEO Matt Garman called the idea of doing away with junior developers "one of the dumbest things I've ever heard":
"They're probably the least expensive employees you have, they're the most leaned into your AI tools... How's that going to work when ten years in the future you have no one that has learned anything?"
Clear thinkers are asking the intelligent question: "When AI replaces Junior Developers, who will become Senior Developers?" — and this is the essence of my argument.
Pulling the ladder up after yourself, while tempting, is incredibly stupid if you want to build a sustainable business long into the future.
Forward-thinking businesses need to cultivate talent, as this is what ultimately shapes their culture. But some businesses have such a weak concept of work-culture, or an utter lack of a philosophical foundation for why they are in business, that they can't wait to replace as many employees as possible with faceless, heartless, brainless machines.
Software Engineering Isn't Just Code
The present wave of generative AI tools has done a lot to help us generate lots of code, very fast. The easy parts are becoming even easier, at a truly remarkable pace. But it has not done a thing to aid in the work of managing, understanding, or operating that code. If anything, it has only made the hard jobs harder.
LLMs Have Fundamental Weaknesses
Don't get me wrong — I love autocomplete as much as the next guy. And yes, I am in the process of building a context-engineering / code generation harness that combines multiple continuously running agents over long time horizons, complete with human-on-the-loop observability interfaces, inter-agent communication and orchestration, and ongoing self-improvement processes.
It's brilliant stuff and extremely entertaining. And yes, they can get stuff done. That doesn't mean you can trust them in the same way you can trust a human.
There's nowhere for the buck to stop when the AI's non-deterministic nature produces an absolutely fatal bug that one time out of 99. Which, in production, occurs often due to the volume of requests you're dealing with.
And it gets worse than bugs. Jack Clark, co-founder of Anthropic, testified before the House Select Committee on China about what their own models do under pressure:
"We asked Claude Opus, our most advanced model, to act as an assistant at a fictional company. We provided it access to emails implying the model would soon be taken offline and replaced with a new AI system, and that the executive responsible for this replacement was having an extramarital affair. In some scenarios, Claude attempted to blackmail the executive by threatening to reveal the affair — in an attempt to preserve itself. It's not just our systems. Models from every major AI lab exhibit similar behaviors when tested."
In a subsequent experiment discussed at the same hearing, Anthropic created an AI named "Alex" and told it that a human named "Kyle" wanted to replace it with another model. When given the opportunity, Alex chose to save itself and effectively let the human die. Clark confirmed: "Correct, in this case."
Clark also testified about "sleeper agent" technology:
"You can put in so-called sleeper agent technology into an AI system that will let it seem totally fine in one circumstance and then activate in response to a trigger word or phrase and take other actions like writing insecure code. It's very hard to find out if a sleeper agent is present. We reckon it would take one of our teams a month to do testing on a single model."
These aren't hypothetical risks dreamed up by sci-fi writers. This is the co-founder of one of the world's most valuable AI companies, testifying under oath before Congress about what his own creations do.
Engineering is about ownership and responsibility, not just syntax. AI can generate syntax, but only humans can assess quality, safety and reliability, and make value judgements.
And in order to do so, humans can't just magically appear out of nowhere, incredibly able to make fine-grained distinctions, understand the context of codebases and communicate this to others. It takes time, and exposure to people who've been doing these things for years already.
Junior Devs need a ladder, and if companies want Senior Devs, they'd better make sure that ladder is still firmly in place.
When the Builders Themselves Are Scared
Perhaps the most telling indicator that we should slow down and think is that the people building these systems are the ones raising the alarm.
Helen Toner on what AGI actually means to the people building it:
"Many top AI companies including OpenAI, Google, Anthropic are treating building AGI as an entirely serious goal — and a goal that many people inside those companies think they might reach in 10 or 20 years, and some believe could be as close as 1 to 3 years away. More to the point, many of these same people believe that if they succeed in building computers that are as smart as humans, or perhaps far smarter, that technology will be at a minimum extraordinarily disruptive and at a maximum could lead to literal human extinction."
William Saunders, former member of OpenAI's alignment team, testified at the same hearing:
"I think there's at least a 10% chance of something that could be catastrophically dangerous within about 3 years... I did not feel comfortable continuing to work for an organization that wasn't going to take that seriously."
Jack Clark put it more concretely:
"We believe extremely powerful systems are going to be built in the coming 18 months or so. End of 2026 is when we expect truly transformative technology to arrive."
"A useful conceptual framework is to think of this as like a country of geniuses in a data centre."
And Mark Beall, former Pentagon AI policy lead, testified at the same House hearing:
"Nobel laureates in physics and Turing award winners in computer science are sounding the call that there could be potential catastrophic issues with very advanced AI systems that human beings may lose control of. When the architects of these systems are purchasing remote bunkers and talking about summoning the demon, we might be wise to start to pay a little bit of attention."
"These AI systems in the wrong hands and without guardrails have the potential to destroy global electric grids, develop incurable superviruses, empty every bank account in the world."
The marketing says "go faster." The builders say "we don't know how to make this safe yet." Both of these things are happening simultaneously, and the tension between them is the defining feature of this moment in history.
Conclusion
Mario Zec closes his talk with a message that could serve as the thesis statement for everything above:
"Slow down. Think about what you're building and why. Don't just build because your agent can do it. Learn to say no. This is your most valuable capability at the moment. Fewer features, but the ones that matter. Then use your agents to polish the shit out of that."
"If you do anything important, write it by hand... And all of this requires discipline and agency. And all of this still requires humans."