On one hand, industrial research is different from academic research. There’s no tenure and not the same level or presumption of academic freedom. Fair enough.

The problem is they specifically wanted to bathe in the glory of an ethical research team and all the benefits that come with that.

You can’t have it both ways.

- Reductionist analogies like how Microsoft Word is not conscious therefore AI is not.

- Dismissive in saying LLMs are not capable of moral reasoning. Maybe he meant agency or responsibility?

- Builds a case based on a Julius Caesar example without realizing its natural extension leads to a philosophical zombie which is not easy to disprove as consciousness.

- Seems to casually disregard non human sadness as a concern.

- Rolls out the stochastic parrot argument with a new coat of paint. This argument never made sense. Having a predictive element a part of the mechanism doesn’t rule anything out. Proper functionalist arguments tend to say what is required not what excludes.

Overall the article seems like a bit of a red herring. The premise is LLMs are not conscious, but most people don’t think they currently are so what’s the point of the claim?

The more interesting questions are whether artificial consciousness is fundamentally possible, and if so how far away are we and what pieces are still missing.

Iiuc water might compress ~50% at the right place in the Earth’s mantle, maybe just not looking much like liquid.

The part I hate though is that I’d bet none of the performance claims are based on int8.

Why do we care about bf16 benchmarks when no one will be using that with this model.

It sounds like marketing spin where the performance claims are based on BF16 and the “runs in 16GB” claim is on a totally different quantized version.

That result would say less about performance of languages than it would about competency of developers with a language.

I just don’t buy that a task could be assigned to two teams with comparable expertise and domain knowledge in Rust and Java, and have the Rust result be at a “huge” performance deficit.

No, don’t believe that was an apples to apples comparison.

To a lot of us it’s not clear that’s what’s happening. It’s speculation and one possibility.

It may also be a secondary consideration and not the primary gating factor.

Anthropic has had their missteps but it’s still plausible to take what they say at face value.

There was some improvement in cognitive scores, but no placebo group. Without a placebo group, there are a lot of explanations for the data.

It might be saturated for smaller scopes of work, but it’s not hard to see the cracks when you scale up what you ask of SOTA models/agents.

One example, to try and single shot prompt coding a ChatGPT equivalent chatbot.

Sure it will spit something out, but the feature depth, UX subtitles, backend integration, and lots of pragmatic engineering decisions along the way will just not be baked.

Another example is building a C compiler from scratch which Anthropic showed is still a struggle to do.

Not that these these specific examples are important but just to point out scaling up expectations shows the cracks.

It’s not just a model problem of course, better agents, orchestration features (like Dynamic Workflows mentioned in the post), all need to continue to evolve.

Ar what point does my CS degree become totally useless is an open question.

The big difference is that meditation and journaling do not require a belief that you are communicating with supernatural beings.

“I don't think intelligence and spiritual practice are mutually exclusive.”

That’s a low bar. At the least we know supernatural/religious beliefs are negatively correlated with scientific training and scientific eminence.

This is the kind of design wisdom that’s both true and difficult to win an argument over.

It reminds me of arguments related to over-engineering and complexity. The principles are super important to having a codebase that scales and continues to be efficient to work in as the team grows, but they are hard to objectively measure.

Locally or in isolation something may sound like a great idea. Being able to step back and see the greater ripple effects require some experience and intuition that can’t always be used to convince people otherwise.

The problem is you can’t defend it right? Someone could say your evidence came from a prompt: “Take this article and reverse engineer a hypothetical unpolished first draft written in a mix of Russian and English”

I’m not sure what the right answer is here. Fwiw I have no doubt you wrote it unassisted.

The other day I was criticized for posting a comment people thought was AI but was actually not.

I’m starting to notice that more often with others as well. Happens sometimes to those who were always using emdash, sometimes to those who happen to have traits that these machines themselves learned from how to write, and now they sound suspicious.

I don’t think this means we should never call out slop or lazy writing, but it does seem our ability to detect this stuff is on a spectrum. Some of it is obvious. But beyond a certain point, for example with this article, the signals can become too weak to make any strong claims.

It’s disconcerting to admit that we’ve come to a point where it’s possible to be completely fooled one way or the other by what’s human or AI. Lots of stuff we can still detect, and sometimes it’s obvious, but at the margins we can no longer reliably discriminate.

One could argue coding is only a use case and that their models are still killing it overall. However agents are strategic across the board and coding agents are at the forefront. They’ve already lead to new products like CoWork and it’s easy to understand why Google should be doing everything possible to catch up.

Surprised they’re not trying to entice developers away with more heavily subsidized subscription plans. Maybe it’s because as some say those days are ending and soon we’ll all be paying per token. Or maybe it would just put too much of a strain on available compute.

The point is you made two brigade style comments about my posts sounding suspiciously like an LLM and having hallucinations.

Neither turned out to be true and I think a better response would concede the point.

It may be more helpful for us to stick together as humans since we can’t always recognize each other so easily anymore.

1. Evidently you’re no longer able to distinguish AI from people as the whole comment was written by a human off the cuff.

2. The numbers are not hallucinations. It’s word on the street reporting, so yes it’s speculative, but a model did not make up it up unless that’s where TechCrunch got it which is not on me.

https://techcrunch.com/2026/04/17/sources-cursor-in-talks-to...

Ffs.

edit: removed cursing you out. Sorry but this is frustrating. I don’t leave AI generated comments here (or anywhere else).

Fwiw it’s beating Claude Sonnet in most benchmarking (benchmaxxing?), yet they’ve priced it almost half off on a per token basis.

Question is are you going to persuade anyone with this argument?

Are there many devs at Google who legit prefer Gemini over Claude and Codex? Would love to hear about that.