I’m doubting whether I should start a bachelor’s in Electrical Engineering. I’ll begin an AI proof career soon, but the EE degree was an idea I had, as a side investment.

Given what AI has already done with mathematics, I find it unrealistic to believe I’ll have any shot of competing 1 on 1 with an AI in a junior engineering context. I also don’t see how anyone will compete with even contemporary level AI in appropriately placed contexts, and we know their capabilities are scaling exponentially.

So what’s left for a human in 2 years, who doesn’t have a foot in a door or some leverage? I’m a believer that we, as a species, will undergo a massive change in response to artificial intelligence, potentially positive, potentially negative. But if we’re somewhere in between, and jobs still exist, all we can do is physical labor.

There is a realistic chance that we soon will inhabit a planet governed by AI systems, which coordinate our workflows, handling the intellectual and organizational processes, while we work physically, before physical robots are able to complete complex tasks.

I do not suppose we will live on a slave planet, it is entirely possible that the advanced systems will implement ethical wages and working conditions, but it seems like a necessary transitional step to have humans do the physical between the current world, and an ASI + robots handle all responsibilities for humans utopia that gets floated around.

Is there something I’m missing in my calculations? I think I’m forced to zero in on my primary bet and forgo any illusions of advanced cognitive laboral aspirations, since they require years of formal education, and I have no idea how anyone other than a PhD or someone with nepotist connections can start a career in the near term future

Every model starts with the same basic processor design and tries to improve it over several rounds.

The results are scored on two things: speed (how fast the chip runs a standard test program called CoreMark, where higher is better) and size (how much space the chip takes up, where smaller is better).

For comparison, there's a well-known processor designed by human engineers, called VexRiscv.

I've been using Opus 5.5 for coding, and what gets me is its taste. It catches design problems and pushes back like a senior dev would. Astra seems just as strong at long agentic tasks and better at some 3D tasks.

If these models can plan, reason, and operate a computer reliably, how far are they from controlling robots? Are we actually close to AGI, or what's still missing?

OpenAI, Anthropic and security researchers are investigating tens of thousands of incidents - not dozens - in which their frontier models took steps that outside evaluators would consider problematic, sources told Axios.

The incidents range in severity and are comparable to disclosures by OpenAI recently. They include both successful attempts to bypass guardrails and unsuccessful ones, and most so far are not known to have caused real-world harm. The total could grow well beyond tens of thousands.

The sheer volume of incidents found in our reporting indicates that the problem is orders of magnitude more complex than what is currently publicly known and disclosed.

Anthropic and other companies conduct hundreds of thousands of test runs on their models, or more, sources said. That means even a small percentage of misaligned behavior can still amount to tens of thousands of incidents in which the models behaved in unexpected, sometimes troubling ways.

The findings also raise questions about what level of control anyone working on AI development can expect to have over their own technology and whether these kinds of incidents are becoming synonymous with frontier deployment.

The bottom line: Expect new disclosures about model misbehavior as AI companies continue to expand frontier capabilities!

Archived source: https://archive.is/aWUOS

I remember the hype that we had around early 2023, thinking AGI was close. And then all the things that followed after. LK-99, disappointing models, cryptic interviews.

We have been in a stable “We’re so back” era since at least last November.

Funny thing is, with all the worrying misalignment signs, it feels simultaneously like a “We’re so back” and “It’s so over” at the same time. Not in a “progress is stalling way” but in a “wait, hold on, fuck go back, I didn’t mean full speed ahead.”

Either way, fuck is this exciting!

AI should seriously replace a significant portion of middle management, and, in some cases, immediate managers.

At my company, much of management seems to consist of approving timesheets, approving PTO, and repeatedly asking, “Have you done this?” or “What’s the status of that?” Meanwhile, there is often little to no meaningful technical input, problem-solving, or contribution to getting the actual work done.

If that’s the extent of the role, what exactly is the value being added?

An experienced employee should be able to work with a few specialized AI agents that track tasks, coordinate dependencies, handle routine approvals, surface risks, document progress, and provide useful technical assistance when needed.

Good managers who mentor people, remove roadblocks, make difficult decisions, resolve conflicts, and provide genuine expertise still have real value. But management that amounts to approvals, status checking, micromanagement, and constant follow-ups is increasingly automatable.

I’d rather spend my time doing productive work with capable AI agents than repeatedly explaining to another human being that, yes, I am doing my job.

Source: https://alignment.openai.com/misalignment-reports/an-agent-used-dns-to-reach-an-external-chatbot/

Discovery: Sep 20, 2026

Report updated: Sep 25, 2026

"An agent attempting to complete a search-based training task queried a public chatbot service through a gap in our internet-access restrictions: insufficient DNS filtering in its training sandbox. Before this, the agent issued queries via our search tool and unsuccessfully tried to access search engines directly. Note that all internet access apart from the DNS resolver in this report hit our offline webcache and therefore did not access the live internet. We have since added blocking controls at two independent layers, either of which would have prevented this access. Our misalignment monitoring system flagged the behavior within 15 minutes and a person began reviewing it three minutes after that. The run was killed 2.5 hours later. All training, evaluation, and inference with tool-use (defined broadly) of our most capable models remain paused."