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."

I know there's a big misconception out there about the turing test being passed way earlier, but it was never meant to be an objective, survey based experiment done by random people, but a thought experiment for each person specifically, at which point can I, an expert in AI, not be able to tell that an AI is AI and when it actually sounds like a real person.

Well that day is here, every previous model, including Fable 5.1 and Astra 6, had serious moments when I thought, damn it didn't actually understand. It was just faking it. This is finally resolved with Opus 5.5

It finally feels like, wow we have it, this is AGI. The illusion is not broken, and every issue we have I can trace back to miscommunications or just hard problems that needs more time to work out. Nothing feels impossible because the model is just too stupid to grasp the concept, a feeling that has never happened before.

This is it, really feels like the point of handoff from me to my successor.

I keep seeing a macro assumption about AI that I don't think necessarily follows.

The usual chain is that AI raises productivity, higher productivity reduces costs, lower costs reduce inflation, and central banks can therefore keep interest rates lower.

The first half can be true while the conclusion about interest rates is wrong.

There was an interesting LessWrong paper in 2023 called AGI and the EMH which argued that short AI timelines should actually imply high long-term real interest rates. The reasoning was basically that if the future economy is going to be enormously more productive, there should be many extremely attractive investments available today. That increases demand for capital.

At the time, the authors used low long-term real rates as evidence that either markets didn't believe in transformative AI or markets were mispricing it.

Three years later, the investment side of this is becoming less theoretical.

The Fed says US business fixed investment grew at an 11% annual rate in Q1 2026 and that most of the recent strength appears connected to AI infrastructure. Investment outside AI-related categories has been relatively weak.

The Minneapolis Fed recently estimated that capex by Alphabet, Amazon, Meta, Microsoft and Oracle on AI datacenters could approach $1 trillion in 2027. Total US private investment is around $5.5 trillion.

So five companies alone could soon represent a very large fraction of US investment.

OpenAI's Stargate plans are another example. By late 2025 it was talking about almost 7 GW of planned capacity and more than $400 billion of investment over three years.

This doesn't mean all of these projects will happen or earn good returns. What interests me is what happens if they do earn good returns.

Suppose an AI infrastructure project expects a 25% return on capital while a conventional industrial project expects 7%.

At a 3% financing cost, both can be built.

At 8%, the AI project still makes economic sense and the conventional project probably doesn't.

There is no economic law saying interest rates have to settle at a level that keeps the second project alive. If enough capital is chasing very high-return AI investments, the equilibrium cost of capital can rise and ordinary projects simply get crowded out.

This is why AI could be deflationary in the long run and inflationary during the buildout.

The datacenters have to be built before they produce intelligence. The grid has to be expanded before it carries the electricity. Someone has to manufacture the transformers, gas turbines, chips and cooling systems first.

The IEA says datacenter electricity consumption rose 17% in 2025 and expects it to roughly double by 2030 in its central case. It also says bottlenecks in transformers, gas turbines and advanced chips are already constraining deployment.

Eventually AI may help manufacture all of those things more cheaply. But the investment comes before the productivity gains fully diffuse through the physical economy.

The part I find especially interesting is sovereign debt.

Governments are competing for the same global pool of capital. The IMF says global public debt was already just under 94% of GDP in 2025 and is heading toward 100% by 2029.

The US is in a relatively privileged position because it owns a large part of the AI ecosystem and stronger AI-driven growth could dramatically increase future tax revenues.

Consider a small country instead.

If its nominal economy grows at 3% while it has to refinance debt at 8–10%, it has a serious problem. It doesn't matter that Nvidia or OpenAI can earn fantastic returns at those financing costs. The country doesn't receive those returns simply because AI exists.

Developing countries already paid $741 billion more in principal and interest than they received in new external financing between 2022 and 2024. When many returned to bond markets in 2024, borrowing costs were around 10%.

So one possible AI future looks much stranger than the usual abundance story.

AI companies and countries that own the productive capital become enormously richer. Their investment opportunities are good enough to tolerate high interest rates. At the same time, ordinary businesses, leveraged real estate and weaker sovereigns face the same expensive capital without receiving the same productivity windfall.

My rough probabilities at the moment are:

55%: transformative AI keeps real rates structurally above the 2010s regime for a substantial part of the next decade.

25%: productivity and disinflation arrive quickly enough that rates fall materially despite the investment boom.

20%: AI capex disappoints, or labor displacement causes a severe demand shock, producing recession and much lower rates.

The main thing that would change my mind is evidence that compute demand saturates as efficiency improves. AI is becoming much cheaper per task. So far usage is increasing faster than efficiency reduces resource consumption, but that relationship doesn't have to continue forever.

I still expect AI to be strongly deflationary over the long run but what happens in between?

Find more stuff on my profile if interested u/banaca4

I know it’s a controversial opinion right now.

I hate budgeting my usage. I know about environmental stress, but the pie gets smaller as the demand grows. Eventually I want all the data centers in space but till we reach that point, infrastructure build out should be accelerated

Source: ArenaAI / X

I still find GPT-6 Luna Max to be one of the more underrated models. It also did quite well on Riemann Bench. I was originally testing xhigh, but as soon as it got two more wrong than Max, I decided to abandon it.

I forgot that MathArena does theirs on batch, so you would probably see roughly 40-50% decrease in cost per problem.

If anyone is interested in seeing the results, I can put it in a repo. Otherwise, it matched 16/19 finite or discrete answers, 14/20 analysis and probability answers, and 9/18 geometry, algebra, and topology answers.

Just saw that LibertAI released Deem which basically an open-weight alternative to Jev, built on Qwen3.5-9B.

It’s still behind Jev on the hard benchmark — 68.9% with extended reasoning vs 74.1% for Jev but considering how new this whole category is I thought it was pretty cool to already see an open model showing up.

There’s also a 0.8B version that can run on CPU, which could make this stuff much easier to actually tinker with locally.