A note on your "IBM has been shrinking" take: IBM spun out Kyndryl in 2021, including 90,000 employees [0]. That leaves the remaining company with 255,000 employees after the spin-off in 2021, which means it has actually grown by 15,000 people to reach the 270,000 in 2024.

The spin-off also explains the drop in revenue. In 2021, the first results reported excluding Kyndryl, IBM had revenues of $57.4B [1]. Since 2021, revenue grew by ~9% to reach $62.75B in 2024.

[0] https://www.kyndryl.com/us/en/about-us/news/2021/11/2021-11-... [1] https://newsroom.ibm.com/2022-1-24-IBM-RELEASES-FOURTH-QUART...

Fair. A good callout. And maybe the right move. However, a healthy IBM would not have needed to calve off its entire Global Technology Services business.

I'm confused - the post says "o3 does not have access to a coding tool".

However, OpenAI mentiones a Python tool multiple times in the system card [1], e.g.: "OpenAI o3 and OpenAI o4-mini combine state-of-the-art reasoning with full tool capabilities—web browsing, Python, [...]"

"The models use tools in their chains of thought to augment their capabilities; for example, cropping or transforming images, searching the web, or using Python to analyze data during their thought process."

I interpreted this to mean o3 does have access to a tool that enables it to run code. Is my understanding wrong?

[1] https://openai.com/index/o3-o4-mini-system-card/

One of the blog post authors here! We evaluated o3 through the API, where the model does not have access to any specific built-in tools (although it does have the capability to use tools, and allows you to provide your own tools). This is different than when using o3 through the ChatGPT UI, where it does have a built-in tool to run code.

(Interestingly, even in the ChatGPT UI the o3 model will sometimes state that it ran code on its personal MacBook Pro M2! https://x.com/TransluceAI/status/1912617941725847841)

I see, thanks for the clarification!

Just throwing this out there. Is it possible that in some way, it does have a MacBook Pro M2? For example, that the tools the ChatGPT UI have are exposed to it through access to one, which is can run whatever tools it wants through? That might actually be quite a sensible way to expose a “tools” UI to an LLM. If what it’s saying is technically accurate on the “flagship product” (ChatGPT), it could be the API version is simply confused about its differences (no access to tools).

I've been getting the anti-adblock pop-ups despite not being logged in. So that last statement seems to not be universally true.

Since the general rule of thumb is that only 10% of startups succeed in the long term, this statement is probably true.

Wait, this means AI startups actually will fare better than other startups then? Seems like the inverse of what the title is trying to convey which is pretty funny if true.

I'd say the 5% could be the startups that fail later (after three years).

Or like 86% of statistics, they are pulled from the are and only "roughly" correct.

Do you have a source on that?

Isn't this a lean startup principle, mechanical turk? There's nothing inherently wrong with doing this at points in your dev cycle.

I don't actually see it as a negative on IBM's part. A client could simply need them to do this for the longer term. Might be easier than dealing with technical debt or updating old systems.

I am a source on that, I know people that were looking at that Jira 24x7x365.

Tried this a few weeks ago with two MBPs: one of them started charging, depending on which end of the cable I plugged in first.

Interesting that it calls itself Eliza, like the NLP software from the 60s (https://en.wikipedia.org/wiki/ELIZA)

So, there are 235 "Eliza chatbot" and over 76K "chatbot" repositories on GitHub. A lot of these have example conversations and answer lists in formats similar to the conversions in the article. I suspect if you go looking somewhere there will be one where the answer to the question "what's your name" is "Eliza".

https://github.com/search?q=eliza+chatbot

It being trained on eliza transcripts also perhaps explains why it's so "good" at having a conversation... sounding much like eliza.

It's actually pretty amazing how "good" eliza was at having conversations, not using anything like contemporary machine learning technology at all. That first conversation snippet in OP that OP says is "kind of scary" is totally one Eliza (or similar chatbots) could have. Weird to remember that some of what we're impressed by is actually old tech -- or how easy it is to impress us with simulated conversation that's really pretty simple? (Circa 1992 I had an hour-long conversation with a chatbot on dialup BBS thinking it was the human sysop who was maybe high and playing with words)

But I doubt you could have used eliza technology to do code completion like copilot... probably?

You're right, when you search `"what's your name" Eliza` on GitHub, you get about 8k code results, some of which include the response "My name is Eliza". But at the same time there are even more results if you try other names (e.g. 61k code results for `"what's your name" Tom`). So I still think its interesting that it happened to pick Eliza here. Possibly because repos that use the Eliza name tend to contain more Q&A-style code than others (as you mention in your comment).

It is not really calling "itself" Eliza.

It is predicting how a piece of text is likely to continue, and it probably had examples of the original ELIZA conversations, and other similar documents, in its training data.

If the user took charge of writing the ELIZA responses, then it would likely do just as well at predicting the next question of the "human" side of the conversation.

True! There is an Eliza implementation on GitHub (https://github.com/codeanticode/eliza/blob/master/data/eliza...) so I guess this how it knows about it.

Eliza (Cassan) is also the name of AI from deus ex.