OpenAI looked at buying Cursor creator before turning to Windsurf
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What saved them? Heineken. They didn't care if the pubs made much of a profit - they made positive margins on having people drink their beer at market prices. They just wanted to increase volume. So they bought up several major players. In 2008 they acquired Scottish & Newcastle's operations, later thought bought Star Pubs & Bars, which had 1,049 leased and tenanted pubs, and finally half of Punch Taverns.
The same strategy can work for OpenAI - buy up the wrapper companies, and make sure YOUR models are served to the user base.
One of those benefits brings to mind another analogy: Apple. The ai model and the tooling are kind of like the hardware and software. By co-developing them you can make a better product and certainly something hard to compete with.
"The company must change its mindset and become proactive in its approach to compliance. I have decided this can best be achieved by the imposition of a sanction that will serve as a deterrent to future non-compliant conduct by Star and other pub-owning businesses."
https://www.gov.uk/government/news/heineken-pub-company-fine...
"We have something called A-SWE, which is the Agentic Software Engineer. And this is where it starts to get really interesting because it’s not just about augmenting a developer, like a Copilot might do, but actually saying, ‘Hey, this thing can go off and build an app.’ It can do the pull request, it can do the QA, it can do the bug testing, it can write the documentation."
https://www.youtube.com/watch?v=2kzQM_BUe7E The relevant discussion about A-SWE begins around the 11:26 mark (686 seconds).
Surely then they have no swe reqs right?
Until we play with it, it doesn't exist.
edit: typo.
It's pretty obvious that these tools are not replacements for developers as of yet. I've tried them, and they are very nifty, and can even do some boring tasks really well, but you can't actually substitute actual developer skill (yet). But everybody is holding their breath because it looks like they might eventually reach that level, and the time-frame for that eventually is unknown.
If this truly exists they'd have no need to hire since it'd force multiply their existing developers.
What better marketing than being able to proudly claim that "OpenAI no longer hires those pesky expensive developers and you can too" because they can improve/multiply the productivity of their existing developers with their innovations.
As some one who has been both in engineering and management roles, I feel the manager role (not all but a lot of managers are just information pass through and tool would be more consistent for it) should be relatively easier for the automation. Bit surprised how no one talks about that as a possibility?
How do any of these companies create “an AI Software Engineer”? Scraping knowledge posted by actual engineers on StackOverflow? Scraping public (& arguably private) GitHub repos created by actual engineers? What happens when all of them are out of a job? AI gets trained on knowledge generated by AI? Where will the incremental gain come from?
It’s like saying I will teach myself to cook better food by only learning from recipe books I created based on the knowledge I already have.
This sounds like the ouroboros snake eating its own tail, which it is, but because of tool use letting it compile and run code, it can generate code for, say, rust that does a thing, iterate until it's gotten the borrow checker to not be angry, then run the code to assert it does what it claims to, and then feed that working code into the training set as good code (and the non-working code as bad). Even using only the recipe books you already had, doing a lot of cooking practice would make you a better cook, and once you learn the recepies in the book well, mixing and matching recepies; egg preparation from one, flour ratios from another, is simply just something a good cook would just get a feel for what works and what doesn't, even if they only ever used that one book.
What's missing is the judgement call of a human to say if some newly created information makes sense to us, is useful to us, etc.
The question above is not about whether new information can be created or the navigation of it. It's about the applicability of what is created to human ends.
When I gave an example of a recipe book, that’s what I meant. There’s the element of not knowing whether something worked without the explicit feedback of “what worked”. But there is also an element of “no matter how much I experiment with new things, I wouldn’t know sous vide exists as a technique unless I already know and have it listed in the recipe book.” What I don’t know, I will never know.
I like Cursor and use it daily, but none of its models are even close to being able to take nontrivial work. Besides, it quickly gets expensive if you’re using the smarter models.
IMO these AI tools will become to software engineers what CAD is to mechanical and civil engineers. Can they work without it? Sure, but why would they?
I started getting worse results from Cursor too. Then, Gemini 2.5 Pro dropped with 1M context, I repomixed my project, popped it into AIStudio, and asked it make me prompts I can feed into Cursor to fix the issues I have.
Gemini has the whole picture and the prompts it creates tell Cursor which items to change how.
Next advances in coding AI depend on real-world coding data, esp how professional developers use agentic AI for coding + other tasks.
RL works well on sufficiently large base models as shown by rapid progress on verifiable problems with good training data, e.g. competition math, competitive coding problems, scientific question answering.
Training LLMs on detailed interaction data from AI-powered IDEs could become a powerful flywheel leading to the automation of practical coding.
Once sufficient data is gathered, the next generation models will be among the very best at agentic coding, which leads to stronger stickiness, and so on.
Honestly, too many. Software engineers can be really, really dumb. I think it has something to do with assuming they're really smart.
But even unwilling developers may be forced to participate (see the recent Shopify CEO email), despite knowing full well what's going on. I mean, tons of people have already had to go through the humiliation of training their offshore replacements before getting laid off, and that's a much more in-your-face situation.
So it seems the most rational position is to embrace the tools and try to ride the wave before the gravy-train is over.
Maybe I am one of the stupid ones but I don't get you people.
This is going to happen whether you want it or not. The data is already out there. Our choice is either learn to use the tool so that we could have that in our arsenal for the future; or grumble in the corner that devs are digging their own graves and cry ourselves to sleep. I'd consider the latter to be stupid.
If you had issues with machines replacing your hands in the industrial age, you had a choice of learning how to operate the machines, I consider this to be a parallel.
You realize that it's what I am saying? Having the tool in our arsenal means being able to do another job (prompt engineering, knowing how to evaluate the AI etc...) in case we are made obsolete in the next couple of years. What happens after that is a mystery...
I've found the enthusiasm towards software that ostensibly aims to replace their skillset utterly bizarre.
I agree. But this is a more general flywheel effect. OpenAI has 500M users generating trillions of interactive tokens per month. Those chat sessions are sequences of interaction, where downstream context can be used to judge prior responses. Basically, in hindsight, you check "has this LLM response been good or bad?", and generate a score. You can expand the window to multiple related chats. So you can leverage extended context and hindsight for judging response quality. Using that data you can finetune a RLHF model, and with it finetune the base model.
But it's not just hindsight analysis. Sometimes users test or implement projects in the real world, and the LLM gets to see idea validation. Other times they elicit tacit experience from humans. That is what I think forms an experience flywheel. LLM being together with humans during problem solving, internalizing approaches, learning from outcomes.
Besides problem solving assistance LLMs are used for counselling/keeping company/therapeutic role. People chat with LLMs to understand and clarify their goals. These are generative teleological models. They are also used by 90% of students if I am to believe a random article.
So the triad of uses for LLMs are: professional problem solving, goal setting/therapy, and learning. All three benefit from the flywheel effect of interacting with millions of people.
It’s the same reason you should never choose an oncologist using yelp reviews.
We should all start building the products that we think will terrify OpenAI most.
And MSFT has many end game options to dump free IDEs on the market with integrated AI.
The question is do they want to go in that direction? (And also if they do, do they only allow Gemini model or do they open it up to a choice of various models (to also include models not related to Google/Gemini) and/or BYOK). I don't see why not because I believe they will slaughter Cursor, Windsurf, et al if so ...
Then again there's also Android Studio, so what do I know. (not a lot)
2. Even if it's thinkable in last 4-5 years due to raising Popularity of web IDE, Android studio was built before that and they chose the IntelliJ platform.
3. Google also has flutter and go.
One of the oddities of Instagram and WhatsApp is both of them were twists on what the expected formula for user value was at the time. (Retro photos and international SMS replacement respectively).
Zed is a standalone editor written from scratch and it hasn't had the same success as Cursor (yet).
JetBrains IDEs are my absolute favorite. JetBrains controls the entire stack but they haven't had the same results yet. Cursor and Claude Code have some sort of product differentiation here that is hard to argue against.
When you have just raised $40 billion and you spend $3 billion on a company that has a product that you also build that is dumb as rocks.
YouTube had the advantage of being able to post pirated videos something that I'm not sure Google would have been able to do. YouTube gained traction in ways Google couldn't duplicate.
I guess not mostly stock, but still half a billion cash, although not $1 billion. I guess my original point still stands, though it isn't quite as impactful an example. :)
[0] https://www.cnet.com/tech/services-and-software/facebooks-fi...
Additionally, there’s speculation that OpenAI might be venturing into social media (https://news.ycombinator.com/item?id=43694877). If true, this could open up numerous competitive fronts that the company may struggle to handle effectively. They’re already dealing with fierce competition in their core business, so expanding too quickly into unrelated areas could spread their focus and resources too thin, potentially weakening their competitiveness in unfamiliar markets.
Related to a potential M&A from OpenAI? I’m less likely to follow their suggestion if they turn around and bake this into OpenAI’s product suite.
related to locking in revenue, any dollar counts when your multiple is 100
It allows me to do documentation driven design in my hobby work and as a result has restored a great deal of joy in that process. Genuinely a surprise for me.
Tried all the front runners and only Windsurf gave me this feeling. Happy for their success. Less happy that my current $10/mo sub might get locked behind a $$$ bundle because someone in the OpenAI c-suite said “Finally, some steak to help sell our sizzle!”
You are 25 yo and made $2.5 Bn in 4 years
How wise it is to buy Cursor is another question. Current valuation has them at 100x revenue. And I suspect agentic products will be a lot less cash flow positive than traditional SaaS because of the massive cost of all that constant codebase context and stream of code.
They might not even get the full $40 billion
They had raised a Series C investment recently. Historically that puts ownership of founders and employees at around 40%. Could be a lot higher for a hot AI company though.
Given two founders, AI company, Series C, and a $3bn purchase price they could each have netted around $750 million in a good scenario. Less if they cashed out in secondaries in previous rounds (which would have been smart). Fantastic outcome for them, obviously. This is the 0.001% scenario that founders dream about.
I've only seen codebase indexing or generating embeddings with Turbopuffer. There has to be more magic to that, right?
Or... do they need more coding data from programmers to train their models?