“the AI guy” at MGX. I try a new software every day. ex. EQT Motherbrain, Spotify, KPMG, Accenture
Stockholm, Sweden
Joined April 2009
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With the emergence of AI coders doing most of the work, I predict a future where it writes correct code with much less guardrails. In fact languages designed for addressing human flaws will be displaced by AI first programming languages, closer to the metal.
Very important development. Formal verification is the holy grail for high risk systems like flight control, medical devices, power plant controllers and more.
Prediction: after all easy software is refactored, cracked engineers will mainly find joy building very complex systems that were not possible before.
… so best time ever to be or become one.
New on our Frontier Red Team blog: using Claude to formally verify software at large scale.
Formal verification can prove that code is free of certain bugs, but historically it has only been feasible for small codebases (or required a lot of expert time).
With AI models, however, this is beginning to change. In collaboration with @theoremlabs, we used Claude to prove security properties in over one hundred real-world open-source libraries over the span of a few weeks.
Read our research in full here: anthropic.com/research/forma…
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Predicting possible consequences of good interpretability research:
- LLM API will have concept suppression and boosting controls instead of fine tuning.
- LLM will ship and be priced by subdomains/ cognitive subsets.
- inference acceleration and iq focus by lobotomy instead of quantization/distillation.
有点震撼。。。我们可能真的快要搞清楚大模型内部到底是怎么运作的了。
OpenAI早期机械可解释性研究的参与者，昨天突然提出了一个很大胆的结论：
我们可能一直误解了神经网络。
昨天， @nickcammarata 发了一篇很有意思的文章，叫《Neural Networks Are Already Clean》。
这哥们早年就在OpenAI做机械可解释性研究，2023 年还牵头启动了@OpenAI 第一个大规模自动化神经元解释项目。
当时他们发现，现有工具只能解释大约 2% 的神经元。
Nick因此得出了一个很悲观的判断：神经网络内部存在大量纠缠，想搞清楚它们到底在干什么，可能非常困难。
结果三年后，他带着更先进的工具和Agent重新研究，发现情况完全不同。
在他们测试的现代视觉模型中，最后一层 99%～100% 的神经元都能找到相当清晰的解释。
甚至在Qwen3-32B这样的语言模型中，初步研究也发现，约90%的神经元都有一个可以识别的主要功能。
最有意思的是，他还重新研究了过去的一些经典案例。
比如曾经有一个神经元，既会对猫脸产生反应，也会对汽车前脸产生反应。
以前大家觉得，这是典型的Superposition，多个不相关的概念纠缠在了同一个神经元里。
结果重新研究后发现，这个神经元可能只是在识别一种共同的V形视觉结构。
猫脸和汽车前脸恰好都有这种特征。
全世界的家人们折腾了这么多年，可能连最基础的观察方式都有问题。
而且我觉得这篇文章最值得关注的，其实是他们使用 Agent的方式。
他们让Agent自动观察神经元、提出功能假设、设计实验、分析电路，再通过新的输入验证假设，甚至自动审查了10 万张神经元归因图。
这其实也是我最近一直关注的一个方向：让 AI 自己去做科学研究，把大量观察、假设、验证的工作自动化。
过去需要研究员花大量时间逐个分析的神经元，现在已经可以让Agent成规模地开展研究了。
当然，目前这些结果还处于早期阶段，距离完整解释大模型的内部机制，还有很多工作要做。
但我觉得Nick提出的这个观点非常有意思：
神经网络内部可能一直都很有规律，只是过去我们的工具太原始，根本看不清楚。
如果这个方向最终被验证，机械可解释性领域过去很多基础假设，恐怕都需要重新审视了。
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I think of models as a mildly in the spectrum, engineer polymaths. Half my prompt input is nuance. Maybe that’s the human part of the equation. But i have great hopes for a knob to modulate that.
Prediction: next generation of models will be a renaissance or Affective Computing and Symbolic AI (inference)
one of the worst things about the newer frontier models
is that they are so RL-fried into always writing code that any question is assumed to be an instruction
and they immediately start making changes
"why does X work like Y"
> "it shouldn't, let me do Z instead"
???
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@thorstenball Great list. Some of my views are slightly different, here are my counter predictions, line by line.
“Code review will die. “
Code will evolve. New domain specific abstractions will be formed to allow some degree of control of what is architected. Humans will interact with abstractions not code directly. Maybe non readable code will rule. Maybe more semantic sugar will be added to manage increasingly large code bases.
“Unit tests might die too”
Testing will evolve to be more extensive and faster, more vertical (many layers). Testing will be behavioral. Cyber testing will be 90% of the testing surface.
“The craft of writing code will disappear. “
Like musicianship post software, coding will prevail as a form of human activity not a necessity. More Humans become virtuoso level than ever because they can now touch more parts of the stack and do more complex things.
“The craft of building software will be more important than ever. “
💯
“Most bugs won't be "coding" bugs. “
💯
“Open source in its current form doesn't make sense anymore.”
💯
“Performance critical contributions by humans will stay what they are: an edge case.”
Except when new much more complex software becomes the norm. Software allways expanded to occupy all available compute. This time it will be the same, expectations will rise, and with that new performance problems too.
“The terminal is dead.”
I think chat and terminal will fuse. CLI as a distribution mechanism will continue to be the norm to enforce APIs. Terminal will become UI. Terminal willl become conversational. All because developing it becomes free.
“Tokens are the new computing paradigm. “
Yes, but this is temporary language. Software will shortly evolve to uncover new more interesting paradigms due to exponential innovation. Tokens will then be just another type of assembly no one cares about.
“The triad of PM/Design/Eng will disappear. “
I think this already is the case. It survives this generation, then the jobs will merge for sure.
“The difference between software engineering at large corporations and small ones will increase. “
Large teams will disappear. Only small ones will survive. Eng size will be equivalent to legal team size but more dominant.
“There's no proof that "good code" will matter in the future.”
I think we will find that good software architecture will be the main requisite for achieving complexity that scales.
“Some people will be priced out of producing software. “
💯 also, total cost of developer will be come the key metric (salary + tokens)
“It's questionable whether cheaper models will be used. “
Price will just remain tiny dimension of a broader decision spectrum: Models will differentiate by many things: benchmark, speed, domain, provenance, TTFT, features, and so much more…
“Models will become so fast that UI will be generated on the fly.”
💯 but designers will define the archetypes and pin them so the concept of affordance still applies and is leveraged.
Such a cool project!
Forget trying to design everything in Figma. You can build your own design tools with AI.
Introducing Toolcraft - a starter kit and UI library for building beautiful creative apps. It's free and available for you today.
npx @pixel-point/toolcraft create
Use it to build apps to stylize images, create WebGL shaders, Three.js scenes, animations, photo editing tools, or whatever else needs a canvas and controls.
Toolcraft gives you the full architecture in a single solution. It comes with:
- plenty of UI components: sliders, pickers, timelines, curves, and more
- built-in canvas, export, and toolbar setup
- AI instructions to help the agent deliver a great result to you
With Toolcraft, you don’t need an extensive prompt. Just explain what type of visuals you want to build, attach references, and wait.
It’s been a game changer for our design process.
Watch the full video on our YouTube channel with all the details and examples.
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I’ve been saying this a lot. Think about the Agent(‘s) Experience above all - how the AI perceives the system and the user. AND the AI experience (how the user perceives the AI). Most AI agents are blind to most stuff, let’s change that in the future or software.
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This is a very cool idea. Great to see something new coming out. Paradigms shifting.
Introducing Fractal — the recursive language model CLI agent.
Fractal is a terminal agent that can answer questions, plan tasks, read and write files, run commands, inspect codebases, and work through problems turn by turn.
But at its core, Fractal is not just another CLI harness around an LLM.
It is powered by predict-rlm, our self-harnessed Recursive Language Model runtime. In practice, that means Fractal can recursively reason through context, interact with its own REPL environment, and take action through code as it works.
We built it to make RLMs easy to try and actually useful on real tasks.
If you've been hearing about RLMs but could never come around to using one, this is the best way to do so.
And if you already know what RLMs are capable of, Fractal is the easiest way to use one on your own tasks.
Install it today:
→ curl -LsSf fractal.trampoline.ai/instal… | sh
Drop us a ⭐️:
→ github.com/Trampoline-AI/fra…
Or check out the website:
→ fractal.trampoline.ai/
Prediction: UX will disappear for a while (for chat UX)… but will come back big time for post AI specialized experiences.
Similar to the client to server circular trend.
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What happens when the moat disappears in software — when everyone can copy and everyone can ship fast?
Companies won’t converge. They’ll contrast.
Paradoxically, enterprise software may have a renaissance. There are now so many building blocks that the combinatorial space is exploding. Categories won’t collapse into “everything is a chatbot.”
Instead we’ll see sharper opinions, more differentiated products, and fewer copycats.
Speed and Complexity will be table stakes. Conviction and Taste will be the “moat”. People will choose by betting on future innovation advantage.
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A new paradigm is emerging: Skill-Oriented Programming.
Skills become the primitive for deterministic, quasi-deterministic, and non-deterministic behavior in business systems.
Example: quasi-determinism → a skill with a static function but llm as the driver. Instead of config tables, you simply describe the behavior. Allow the AI to change the file, expands the expressivity of the system, retaining some degree of control. Block that and you establish rules of physics for the environment.
This enables simpler, more powerful architectures where you design the persona first, then build the system around it to achieve the desired outcome.
Working on this, super exciting to see it taking shape.
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Fixing bugs on a live system by asking its assistant to retry a task using a tool… while I’m actively AI coding that same tool.
Feels like performing brain surgery on a patient who’s awake and talking.
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Thinking like an agent is the most important mindset for the modern architect, designer, engineer, org designer. I call it the “AI Experience” - how the AI sees and interacts with you and your business - and has been a major unlock for how we build apps and rethink processes.
As more Next.js apps are built with AI, we've been focusing on improving the agent experience.
Here's what we've learned, and how we're thinking about agents as first-class users ↓
nextjs.org/blog/agentic-futu…
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Jobs that do the work become jobs designing how the work is done.
No one does the work. The work gets done.
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Idea: enterprise as code. One repo with all (agents) you need to run the company: product, ops, sales, everything... later, enterprise in a box….
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This is exactly what life at Spotify felt like 🤣
The company hired me to lead their "Agile Transformation."
I don't know what Agile means.
Nobody does.
That's why it works.
I make $425,000 a year.
To move sticky notes.
From left to right.
On a board.
The board is digital now.
The sticky notes cost $80,000 in Jira licenses.
Progress.
Day one, I said "we need to break down silos."
Everyone nodded.
Silos are bad.
I don't know why.
But destroying them is a career.
My career.
I introduced "squads."
Squads are teams.
But disrupted.
We disrupted the teams into teams.
Different names.
Same people.
Same problems.
But Agile problems now.
Agile problems are strategic.
A senior engineer asked what we're actually changing.
I said, "The mindset."
He asked what that means.
I said, "It's a journey."
He asked where we're going.
I said, "Toward agility."
He asked what agility means.
I pointed at the sticky notes.
They were moving left to right.
That's velocity.
We have velocity now.
The VP of Engineering said two-week sprints don't fit their work.
I said, "That's waterfall thinking."
Waterfall is bad.
Like silos.
I don't know what waterfall is.
But I know it's bad.
She stopped talking.
Waterfall accusations end conversations.
We had a retrospective.
In the retro, we discussed what went wrong.
Everything went wrong.
We put it on sticky notes.
Then we moved the sticky notes.
Into a column called "Parking Lot."
The Parking Lot is where problems go to die.
It's full.
We don't look at it.
That's agile.
Velocity is up 40%.
I defined velocity.
I also defined the points.
I also defined the stories.
We're crushing it.
At the things I made up.
To measure.
Ourselves.
The CEO asked for ROI.
I showed a chart.
The chart went up.
Charts should go up.
This one did.
I didn't label the Y-axis.
Nobody asked.
Leadership is confidence.
We do standups now.
Every day.
We stand.
For 45 minutes.
Standing is agile.
Sitting is waterfall.
My legs hurt.
But we're transforming.
The transformation is now "Phase 3."
Phase 1 was assessment.
Phase 2 was implementation.
Phase 3 is "continuous improvement."
Continuous means forever.
Forever means job security.
I'm very secure.
My contract was extended.
Three more years.
For "cultural impact."
The culture is confused.
But impacted.
Agile transformation isn't about being agile.
It's about transforming.
Continuously.
Toward more transformation.
The destination is the journey.
The journey is billable.
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@_hex_tech I love threads! but please make your tables look nice? they are not honoring your system... check evidence they have the best tables in the business.
Also, system msg for threads to e.g. "allways show my numbers in Millions"
Longshot: custom react components 🙏
Websockets will be the spinal medula of AI applications, when the brain is on the server and perception on the browser. adding ws to vercel is fundamental to smarter, livelier apps.
Server 🧠needs to have fast access to perception (from browser). (1/n)
We’re not actually using socket.io, it was tongue in cheek. We use Pusher to notify the client there’s something to pull.
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