finolex1

The issue is what happens if/when the models grow capable enough that the providers can't stop them even if they want to. You could have strict penalties but that's not going to solve an open research question.

Not really relevant to this article, which is about graduate students in research programs, who get a stipend and don't pay anything. Of course, low stipends are also a big detractor for potential students

Forget AI, Google/Microsoft/Amazon could all in theory have built a clone of Jira/Figma/<x> tool by now. But large companies lack the focus and commitment needed to build true competitors to these products, especially if it's not a big enough market to make a real difference to their bottom line.

Perhaps this will change soon if AI models reach the "army of geniuses in a datacenter" level, but current models are a far cry from just being able to clone Jira or Asana.

It's not so simple because:

1. Companies can hire overseas. There's some cost to it in terms of added friction, but if wages rise enough in C1, then it's worth the friction to hire in C2 instead.

2. Workers also consume and invest, raising demand for other jobs. Employment is not a zero sum game, especially at the macro scale.

How is that different from the US? Immigrants also get booted here if they lose their job. They also pay social security, Medicare, and other taxes but usually don't get the benefits unless they stay here for long enough and get a green card.

You're attributing way too much intent to what is the viewpoint of some random analyst at Goldman Sachs (who doesn't even control any purse strings). A year ago there was another big hullabaloo when a GS team wrote a long post about how AI companies would never make enough revenue.

This is either insanely ambitious genius or pure shithousery. I guess we'll find out which one it is in 10 years

Singapore.

But anyways, the order of causation is probably reversed. Cities with high density are forced to invest in good public transport by sheer public demand and pressure.

In this case, there might be some truth to the statement.

Not in the sense that AI is replacing current jobs, but that they would rather invest that money in Anthropic or on Data Center buildouts

Mercedes' feature has been sunset. It only ever worked in good weather on a limited set of motorways, below a certain speed, and with a guide vehicle in front of it l.

https://www.electrive.com/2026/01/12/mercedes-pauses-level-3...

Is there anything substantial in his list ("agents, subagents, their prompts, contexts, memory, modes, permissions, tools, plugins, skills, hooks, MCP, LSP, slash commands, workflows, IDE integrations") that Claude Code or Cursor don't already incorporate?

I empathize with his sense that if we could just provide the right context and development harness to an AI model, we could be *that* much more productive, but it might just be misplaced hope. Claude Code and Cursor are probably not that far from the current frontier for LLM development environments.

Residents of every city claim that they have the craziest drivers or toughest streets to navigate in. London isn't really that materially harder to drive in than San Francisco.

This specific bet is very targeted, but we do absolutely have commerciallly available self-driving cars in 2025 in several cities, and the list of cities is rapidly expanding.

An 8-10 year delay from expectations is not too bad all things considered.

Tourists get this impression by staying in the city centre. However, if you live in an outer neighborhood, what would be a 15 min car ride can easily become a 40 minute journey on public transport if you have to walk to the train station, take the train, and perhaps take a bus for the last mile. Now imagine you have a kid in tow or an elderly parent.

Yes, public transit is not a necessity here like in the States, but it's a nice convenience to have, and plenty of people are wealthy enough to pay for it.

I'd love to see someone build Duolingo with a single system prompt. By HN standards, no one would be paying for Character.ai, Cursor, Windsurf, or dozens of others tools because they could just call the ChatGPT API themselves.

He literally says in his post "It might look antiquated but it makes Cursor, Windsurf, Augment and the rest of the lot (yeah, ours too, and Copilot, let's be honest) FEEL antiquated"

It's the trend that matters, not the absolute numbers

1. It's not a defense, just an observation.

2. Transaction costs for liquidating your wealth is materially different from selling enough to significantly affect the market for an asset. As an extreme example, large holders of a meme cryptocurrency cannot sell the majority of their holdings without crashing the value of their coin.

3. Borrowing works for smaller amounts if you can spread out the sales of your assets over a long period of time (or if you don't need to sell at all, e.g. if investing in something that gives you returns).

Most billionaires cannot extract most of their wealth without crashing the price of their remaining assets. No doubt the gulf is still great, but it's a little smaller than the raw numbers imply.

There is still publicly available code and documentation to draw from. As models get smarter and bootstrapped on top of older models, they should need less and less training data. In theory, just providing the grammar for a new programming language should be enough for a sufficiently smart LLM to answer problems in that language.

Unlike freeform writing tasks, coding also has a strong feedback loop (i.e. does the code compile, run successfully, and output a result?), which means it is probably easier to generate synthetic training data for models.

Why would it benefit society to get less targeted ads as opposed to more targeted ones?

Replace "Open Source AI" in "is there an argument against xxx" with bioweapons or nuclear missiles. We are obviously not at that stage yet, but it could be a real, non-trivial concern in the near future.

Perhaps improvements might fizzle out, but a lot of smart people seem to think they won't. The AI models we are using today are the worst that models will ever be in the future. We can't base our valuations on our current experiences with AI today.

5 years ago, just getting a computer to form vaguely relevant grammatically correct sentences felt magical.

If you are immuno-compromised or living with someone who is, shouldn't you not be travelling thousands of miles to a large, packed conference anyway? Since masks are not 100% effective.

To each their own, but I personally will not be attending PyCon next year because it was exhausting to wear a mask for 5 days straight.

It's obviously made in jest

From my understanding, buybacks return money to investors, who are free to plow that cash into something else that "creates value". It's not like Google is the only place that knows how to use that money effectively.

You don't have to go all the way to France. It's the same anywhere outside big cities in the US.

I think you're getting the order of causation reversed. The alternative to Infosys in the 90s would be to move abroad or work in a government job. You can't build a Porsche on day 1 - you have to start with the 10k car.

The consulting companies kick started the engineering base that eventually led to MNCs/big tech setting up offices in India, and the broader startup ecosystem today.

Yeah, but the average quality of students would go down.

They might apply, but certainly won't attend if they are admitted to Stanford, Yale, etc. The higher the price differential, the more likely people would be willing to take a 'prestige' hit and go to the lower cost school.

Anyone applying from a family making 400k a year (top 2%) would still be sensitive to an 80k vs 240k/year sticker price change.