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Erik D. Kennedy reflecting on why has AI not impacted designer, yet.
My hunch: vibe coding is a lot like stock-picking – everyone’s always blabbing about their big wins. Ask what their annual rate of return is above the S&P, and it’s a quieter conversation 🤫
Ha!
An insightful post by Bruce Schneier on the security issues plaguing AI. He also suggests that prompt injections might be unsolvable in today’s LLMs.
The fundamental problem is that AI must compress reality into model-legible forms. In this setting, adversaries can exploit the compression. They don’t have to attack the territory; they can attack the map. Models lack local contextual knowledge. They process symbols, not meaning. A human sees a suspicious URL; an AI sees valid syntax. And that semantic gap becomes a security gap.
Prompt injection might be unsolvable in today’s LLMs. LLMs process token sequences, but no mechanism exists to mark token privileges. Every solution proposed introduces new injection vectors: Delimiter? Attackers include delimiters. Instruction hierarchy? Attackers claim priority. Separate models? Double the attack surface. Security requires boundaries, but LLMs dissolve boundaries. More generally, existing mechanisms to improve models won’t help protect against attack. Fine-tuning preserves backdoors. Reinforcement learning with human feedback adds human preferences without removing model biases. Each training phase compounds prior compromises.
This is Ken Thompson’s “trusting trust” attack all over again. Poisoned states generate poisoned outputs, which poison future states. Try to summarize the conversation history? The summary includes the injection. Clear the cache to remove the poison? Lose all context. Keep the cache for continuity? Keep the contamination. Stateful systems can’t forget attacks, and so memory becomes a liability. Adversaries can craft inputs that corrupt future outputs.
This is the agentic AI security trilemma. Fast, smart, secure; pick any two. Fast and smart—you can’t verify your inputs. Smart and secure—you check everything, slowly, because AI itself can’t be used for this. Secure and fast—you’re stuck with models with intentionally limited capabilities.
Ben Thompson talking about origins of the US-East-1, the AWS region that went down a couple of days back and along with it a significant portion of the internet.
Northern Virginia was a place that, in the 1990s, had relatively cheap and reliable power, land, and a fairly benign natural-disaster profile; it also had one of the first major Internet exchange points, thanks to its proximity to Washington D.C., and was centrally located between the west coast and Europe. That drew AOL, the largest Internet Service Provider of the 1990s, which established the region as data center central, leading to an even larger buildout of critical infrastructure, and making it the obvious location to place AWS’s first data center in 2006.
That data center became what is known as US-East-1, and from the beginning it has been the location with the most capacity, the widest variety of instance types, and the first region to get AWS’s newest features. It’s so critical that AWS itself has repeatedly been shown to have dependencies on US-East-1; it’s also the default location in tutorials and templates used by developers around the world.
The Oatmeal talking about AI art.
As a kid, I had one of those little Casio keyboards where you could hit a button and it’d automatically play a song.
I remember hitting the button.
I remember standing there, pretending to make music.
That’s how I see Al art.
Standing there.
Pretending.
Pretending. This resonated with me. At times I have had this feeling whenever I used AI. While generating proposals. While generating code. While generating ideas. It seems I am pretending to work.
So is the future of work that we all pretend to work?
Stephanie Palazzolo writing for The Information.
Not only has the one-year-old Thinking Machines not yet released a product, it hasn’t talked publicly about what that product will be. Even some of the company’s investors don’t have a very good idea of what it is working on. While raising capital for Thinking Machines earlier this year and late last year, Murati shared few details about what it would be building, prospective investors said.
“It was the most absurd pitch meeting,” one investor who met with Murati said. “She was like, ‘So we’re doing an AI company with the best AI people, but we can’t answer any questions.’”
Despite that vagueness, Murati raised $2 billion in funding—the largest seed round ever—at a $10 billion pre-investment valuation from top Silicon Valley VC firms including Andreessen Horowitz, Accel and GV. The investors also made the highly unusual decision to give her total veto power over the board of directors. (Thinking Machines is using Nvidia-powered servers it rents from Google Cloud, whose ultimate parent Alphabet also oversees GV.)
When 2025 started all I could read was AI is going to replace humans. Now its all about—AI is in a bubble. These valuations don’t make sense.
I don’t know what to believe anymore. But I am bookmarking this in case the bubble explodes in the future.
Jason Gorman explaing the challenge of comprehension debt with AI generated code.
When teams produce code faster than they can understand it, it creates what I’ve been calling “comprehension debt”. If the software gets used, then the odds are high that at some point that generated code will need to change. The “A.I.” boosters will say “We can just get the tool to do that”. And that might work maybe 70% of the time.
But those of us who’ve experimented a lot with using LLMs for code generation and modification know that there will be times when the tool just won’t be able to do it.
“Doom loops”, when we go round and round in circles trying to get an LLM, or a bunch of different LLMs, to fix a problem that it just doesn’t seem to be able to, are an everyday experience using this technology. Anyone claiming it doesn’t happen to them has either been extremely lucky, or is fibbing.
It’s pretty much guaranteed that there will be many times when we have to edit the code ourselves. The “comprehension debt” is the extra time it’s going to take us to understand it first.
And we’re sitting on a rapidly growing mountain of it.
On a very similar note, Steve Krouse explains how vibe code is legacy code because nobody understands it.
Ethan Mollick reflecting on the recent report by OpenAI which evaluates AI model performance on real-world economically valuable tasks
Does that mean AI is ready to replace human jobs?
No (at least not soon), because what was being measured was not jobs but tasks. Our jobs consist of many tasks. My job as a professor is not just one thing, it involves teaching, researching, writing, filling out annual reports, supporting my students, reading, administrative work and more. AI doing one or more of these tasks does not replace my entire job, it shifts what I do. And as long as AI is jagged in its abilities, and cannot substitute for all the complex work of human interaction, it cannot easily replace jobs as a whole…
…and yet some of the tasks that AI can do right now have incredible value.
Deena Mousa explaining how radiology combines digital images, clear benchmarks, and repeatable tasks, but replacing humans with AI is harder than it seems.
First, while models beat humans on benchmarks, the standardized tests designed to measure AI performance, they struggle to replicate this performance in hospital conditions. Most tools can only diagnose abnormalities that are common in training data, and models often don’t work as well outside of their test conditions. Second, attempts to give models more tasks have run into legal hurdles: regulators and medical insurers so far are reluctant to approve or cover fully autonomous radiology models. Third, even when they do diagnose accurately, models replace only a small share of a radiologist’s job. Human radiologists spend a minority of their time on diagnostics and the majority on other activities, like talking to patients and fellow clinicians.
Now where have I heard this before? Oh yes, here.
Coding can be a challenge, but I’ve never had spent more than two weeks trying to figure out what is wrong with the code. Once you get the hang of the syntax, logic, and techniques, it’s a pretty straightforward process—most of the time. The real problems are usually centered around what the software is supposed to do. The hardest part about creating software is not writing code—it’s creating the requirements, and those software requirements are still defined by humans.
I read this post by Can Elma on how AI is helping senior developers but not junior developers. While the post has some interesting takes, there’s an even more interesting discussion on it on Hacker News. Two of my favourite comments.
Comment by kaydub.
Because juniors don’t know when they’re being taken down a rabbit hole. So they’ll let the LLM go too deep in its hallucinations.
I have a Jr that was supposed to deploy a terraform module I built. This task has been hanging out for a while so I went to check in on them. They told me the problem they’re having and asked me to take a look.
Their repo is a disaster, it’s very obvious claude took them down a rabbit hole just from looking. When I asked, “Hey, why is all this python in here? The module has it self contained” and they respond with “I don’t know, claude did that” affirming my assumptions.
They lack the experience and they’re overly reliant on the LLM tools. Not just in the design and implementation phases but also for troubleshooting. And if you’re troubleshooting with something that’s hallucinating and you don’t know enough to know it’s hallucinating you’re in for a long ride.
Meanwhile the LLM tools have taken away a lot of the type of work I hated doing. I can quickly tell when the LLM is going down a rabbit hole (in most cases at least) and prevent it from continuing. It’s kinda re-lit my passion for coding and building software. So that’s ended up in me producing more and giving better results.
Comment by bentt.
The best code I’ve written with an LLM has been where I architect it, I guide the LLM through the scaffolding and initial proofs of different components, and then I guide it through adding features. Along the way it makes mistakes and I guide it through fixing them. Then when it is slow, I profile and guide it through optimizations.
So in the end, it’s code that I know very, very well. I could have written it but it would have taken me about 3x longer when all is said and done. Maybe longer. There are usually parts that have difficult functions but the inputs and outputs of those functions are testable so it doesn’t matter so much that you know every detail of the implementation, as long as it is validated.
This is just not junior stuff.
Faisal Hoque arguing that there are three bubbles in AI. He concludes his post by explaining the benefits of bubbles.
Far from being a threat, the AI bubble might be the best thing that could happen to pragmatic adopters. Consider what speculative excess delivers: billions in venture capital funding R&D you’d never justify to your board; the world’s brightest minds abandoning stable careers to join AI startups, working on tools that you’ll eventually be able to use; infrastructure being built at a scale no rational actor would attempt, driving down future costs through overcapacity.
While investors bet on which companies will dominate AI, you can cherry-pick proven tools at competitive prices. While speculators debate valuations, you will be implementing solutions with clear ROI. When the correction comes, you’ll also be able to benefit from fire-sale prices on enterprise tools, seasoned talent seeking stability, and battle-tested technologies that survived the shakeout.
The dotcom bubble gave us broadband infrastructure and trained web developers. The AI bubble will leave behind GPU clusters and ML engineers. The smartest response isn’t to avoid the bubble or try to time investments in it perfectly. It is to let others take the capital risk while you harvest the operational benefits. The bubble isn’t your enemy. If you play your cards strategically, it can be a major benefactor.
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