Well, yeah. That's why we have "research & development" as a term.
What you're referring to is the "development" part of that. In some sense: the job you have _exists precisely because it's not part of the research phase_, and it's equally as valuable as the research part. Research is the proof of concept; development is scaling up and making production-ready and finding small efficiencies and so on.
From an industry perspective, it's tempting to conflate these, because that's what industry research labs are designed to do: integrated R&D. But that is not at all how academic research labs work.
As a Canadian / American who now lives in Europe: IMHO the two-party system and current constitutional structure in the US is an unfortunate local maximum.
It was very definitely better than the centuries of militaristic monarchic feudalism Europe waded through from medieval times until the mid-1900s. It is very definitely worse than modern pluralistic coalition-based democracies with proportional representation, which offer a wider range of choices to voters, and make it possible to launch competing parties / movements to counter institutional stagnation.
Until recently, the one counterargument I would hear to this second assertion is "but coalition governments have a hard time getting anything done". Now that we see a prime example of a government that alternates between a) not getting anything done and b) getting things done that belong somewhere in a timeframe from the 1890s to the 1940s, I no longer hear people making that counterargument.
Re: constitutional structure, one Irish friend I have made an interesting point: in his lifetime, there have been many changes and amendments to the Irish constitution. This is next to impossible in the US system, both because of the party loyalty dynamic mentioned above _and_ because of the incredibly high procedural bar to doing so. (And not least because of the current predominance of originalist thinking in the judicial branch, as though the constitution were an infallible document handed down from gods among men, eternally to be interpreted as the Founding Fathers intended back over 200 years ago in a completely different social, political, and technological context.)
"Force" is often an unrealistic expectation, though. Taking Claude Code as an example: you can add as many rules / guidelines as you want in instruction files, but they will not be followed 100% of the time, and more is not better [1].
You can of course use PreToolUse hooks to block particularly damaging actions of the "rm -rf" variety, but this is also not 100% guaranteed unless you're able to block _all_ ways of performing that damaging action (and you would be surprised: agents will happily write custom python / bash / etc. scripts to do actions you tried to block them from doing!)
Tools help instruct the agent to redo work e.g. to pass linter / formatter checks or relevant tests. But I've also seen them ignore those, often enough to be noticeable: e.g. "17 of 18 tests pass, the other 1 wasn't introduced by this feature" - regardless of whether that's actually true or not, regardless of whether I put "ALWAYS make sure ALL affected tests pass" in an instruction file somewhere.
This isn't to refute your main point: yes, you can improve your chances that AI will write good code. But there is no magic bullet that will force it, 100% of the time, to write good code; this is where vibe coders without requisite coding + engineering skills hit a wall. A multi-layered approach of guidelines + progressive disclosure + tools + hooks indeed reduces the probability of bad code enough to be useful for many engineering tasks.
As someone who now lives and works in Denmark: it's sad that so many of us have been conditioned to think 6 weeks severance is generous.
Here, labor unions are quite widespread, and very effective at negotiating reasonably but firmly. As a result, I can depend on 3 months severance _guaranteed under law_ after 6 months at a job. (After 3 years, it goes up to 4 months, and then from there up to a max of 6 months.)
It puts the responsibility for risk of instability, errors in planning hiring / capacity, etc. firmly where it belongs: with the employer.
(And no, the economic sky is not falling here as a result. Quite the opposite.)
> but people have hundreds and thousands on conversation on these apps that can't be easily moved elsewhere.
Except these aren't conversations in the traditional sense. Yes, there's the history of prompts and responses exchanged. But the threads don't build on each other - there's no cross-conversational memory, such as you'd have in a human relationship. Even within a conversation it's mostly stateless, sending the full context history each time as input.
So there's no real data or network effect moat - the moat is all in model quality (which is an extremely competitive race) and harness quality (same). I just don't think there's any real switching cost here.
For anyone who's a fan of the word game Wordle (where you try to guess a secret 5-letter word in 6 guesses): I've been working on a small side project I call Adversarial Wordle. Enjoy!
TBH there will likely be a _huge_ demand for "digital sovereignty consulting" over the next while, especially in the EU (and maybe also Canada).
Here in Denmark, the previously unthinkable is happening: because of Schleswig-Holstein's leadership in moving to OSS, the Danes are now seeking to learn from the Germans (or at least, that particular set of Germans) about digitalisation! That trend, plus the Danish government's all-in-on-vendors/consultants approach to digitalisation, will likely open a sizeable market - and the traditional vendors like Netcompany have taken a large beating in public opinion themselves, so it's a good time to start something in this direction.
And at the Digital Tech Summit in Copenhagen this year, digital sovereignty (and the lack thereof) was a very prominent theme across both public and private sector talks. As was the comparative advantage the EU has in _trust_, and how that helps e.g. businesses around cybersecurity, privacy-oriented SaaS, and data management expand even outside the EU - which makes it extra infuriating to see continued political interest in things like Chat Control and cracking down on GrapheneOS. This trust is IMHO pretty much the only advantage the EU has in the global tech marketplace, and we're busy throwing it away.
As a Canadian-American living in Denmark, I've seen both sides of this. In short: trust and mistrust are _both_ self-reinforcing concepts.
To take an example - would I want the current US government to be better at compiling information across all its agencies / departments? Absolutely not. What it does with its current level of consolidation is authoritarian enough that I'm not moving back there any time soon. I hear similar sentiments from my Hungarian colleagues, who are quite familiar with competitive authoritarianism in their own country.
Of course, this mistrust becomes self-reinforcing. I don't trust the US government, so I want it to be bad at its job - but then it's bad at its job, so I see it as ineffective and bloated and continue to mistrust it.
IMHO the only way out of this spiral is the hard way: a system must do the hard work to show itself trustworthy, and it must do so _before_ people will entrust it with the information that would make the job of being trustworthy easier. As with human relationships, it takes a _lot_ more work to repair trust than it does to break it. Unlike with human relationships, you also have systemic factors: the system needs an unbroken series of good, principled leaders; it needs to visibly and credibly punish corruption, not turn a blind eye; it needs to de-escalate divisions, not inflame them; it needs various institutional safeguards to work properly, not chop away at them; it needs to allow meaningful dissent and criticism, not crack down on it; it needs to learn from expertise, not undermine it.
Most importantly: the system needs to learn from its failures, and adjust the rules and incentives of the system itself to prevent those failures from recurring. This is generational work.
100% agree that spending on R&D is _very_ efficient in terms of just about anything you could conceivably care about - downstream economic outcomes, quality of life, geopolitical strength / prestige, etc.
For instance, in 2023 the US spent ~$190B in federal funding on R&D [1], compared to a budget of ~$6T [2] - i.e. about 3%. It's really really not a lot when you consider the aggregate impact over decades.
But it is still a lot in an absolute sense. This funding supports an entire ecosystem across both academia and industry that directly creates hundreds of thousands of jobs, many of which require highly specialized skills. I mention this not to create a sense of sticker shock, but to drive home the point that making this investment is a big and complex task - and one that takes a long time to rebuild. I firmly believe that the current chaos in the US will take at least a generation to repair.
Government investment in science is...the only way basic science happens, really. I'd recommend reading The Entrepreneurial State [1] here: in essence, basic science pays off too slowly to interest even the most deeply-pocketed capital interests, but it pays off, so wise societies invest in it; Silicon Valley owes its existence to massive formative public investments in underlying technologies.
Not to mention that smart people generally prefer to live in places that value and protect science, so it's _also_ an indirect form of geopolitical talent recruitment. (See brain drain + brain gain impacts of science policy, for instance. There's a strong argument to be made that US mid-20th-century dominance in science and engineering was largely driven by a lot of very smart people fleeing Nazi Germany.)
Basic science isn't so much a lottery ticket as a bond with unknown maturity measured in decades, a _very_ high rate of return, a high minimum investment, and dividend-like payouts created by adding skilled scientists, engineers, etc. to your tax base.
I'll choose instead to be amazed that Doom-capable computers are now inexpensive and ubiquitous enough that it makes total financial sense to use one in a light bulb!
More seriously, I see this argument all the time: that we are just squandering our advances in hardware by making comparably more inefficient software! Considering that efficiency used to be thought of on the level of minimizing drum rotations and such: the whole point is that we're now working at a much higher level of abstraction, and so we're able to build things that would not have been possible to build before. I for one am extremely grateful that I don't have to think about the speed of a drum rotating, or build web applications as spiders' nests of CGI scripts.
Are there modern websites and applications that are needlessly bloated, slow, and inefficient? Certainly - but even those would have been impossible to build a few decades ago, and I think we shouldn't lose sight of that.
Less flippantly: pedagogically, there's a big difference between "here's your addition tables, memorize them" and "if I have 1 of _anything_ and another 1 of that same thing, I now have 2 of that thing." The latter offers way more opportunities for further thought: by that logic, if I have 2 things and I take 1 away, I now have 1 thing! If I have 2 rocks and 2 sheep, I can count the sheep by laying out 1 rock per sheep! And since I can add more things, maybe there are more numbers for those amounts of things too? And what about differently-sized things? Or parts of things? Or...
That's the difference between getting "1 + 1 = 2" across as a literal by-the-book fact, and getting it across as an invitation to build / connect ideas and ask further questions.
What you're referring to is the "development" part of that. In some sense: the job you have _exists precisely because it's not part of the research phase_, and it's equally as valuable as the research part. Research is the proof of concept; development is scaling up and making production-ready and finding small efficiencies and so on.
From an industry perspective, it's tempting to conflate these, because that's what industry research labs are designed to do: integrated R&D. But that is not at all how academic research labs work.