The typical AI economic discussion always focuses on job loss, but that's only half the story. We won't just have corporations firing everyone while AI does all the work - who would buy their products then?
The disruption goes both ways. When AI slashes production costs by 10-100x, what's the value proposition of traditional capital? If you don't need to organize large teams or manage complex operations, the advantage of "being a capitalist" diminishes rapidly.
I'm betting on the rise of independents and small teams. The idea that your local doctor or carpenter needs VC funding or an IPO was always ridiculous. Large corps primarily exist to organize labor and reduce transaction costs.
The interesting question: when both executives and frontline workers have access to the same AI tools, who wins? The manager with an MBA or the person with practical skills and domain expertise? My money's on the latter.
Idk where you live, but in my world "being a capitalist" requires you to own capital. And you know what, AI makes it even better to own capital. Now you have these fancey machines doing stuff for you and you dont even need any annoying workers.
By "capitalist," I'm referring to investors whose primary contribution is capital, not making a political statement about capitalism itself.
Capital is crucial when tools and infrastructure are expensive. Consider publishing: pre-internet, starting a newspaper required massive investment in printing presses, materials, staff, and distribution networks. The web reduced these costs dramatically, allowing established media to cut expenses and focus on content creation. However, this also opened the door for bloggers and digital news startups to compete effectively without the traditional capital requirements. Many legacy media companies are losing this battle.
Unless AI systems remain prohibitively expensive (which seems unlikely given current trends), large corporations will face a similar disruption. When the tools of production become accessible to individuals and small teams, the traditional advantage of having deep pockets diminishes significantly.
AI is an existential threat to tech companies not software engineers.
In many domains, the scope and complexity of software systems goes beyond the ability of a single software engineer to manage. A coordination layer becomes necessary when the number of engineers required goes beyond a threshold (say 5 or so). When the development effort must be coordinated over extended periods (say several months or years), mechanisms to raise capital and manage risk become necessary. These functions are why companies exist.
Consider that a massive increase in software engineer productivity will make coordination unnecessary for many kinds of software. In the market that opens up, companies with expensive executives, middle management and coordination inefficiencies will not be competitive. Smaller shops with a solo engineer or a team of less than 5 will outcompete larger players because their costs will be significantly lower. Massive one-size-fits-all products will be harder to justify when a small dev shop can quickly build or customise software for the unique requirements of a business or niche.
Before the CEOs stop needing engineers, engineers will stop needing CEOs and managers to coordinate their efforts and raise capital.
It's a threat to many workers imo, just like autonomous machine were to the workers during the industry revolution, and later with the factories moving to China. Many people suffered from unemployment and the solution so far have been solved by creating new needs and new jobs, as well as policies such as social security.
But with the externalization of intelectual work (which happen without IA, for ex. India tech) I wonder if such solution is possible.
If AI increases the productivity of a single engineer between 10-100x over the next decade, there will be a seismic shift in the industry and the tech giants will not walk away unscathed.
There are coordination costs to organising large amounts of labour. Costs that scale non-linearly as massive inefficiencies are introduced. This ability to scale, provide capital and defer profitability is a moat for big tech and the silicon valley model.
If a team of 10 engineers become as productive as a team of 100-1000 today, they will get serious leverage to build products and start companies in domains and niches that are not currently profitable because the middle managers, C-Suite, offices and lawyers are expensive coordination overhead. It is also easier to assemble a team of 10 exceptional and motivated partners than 1000 employees and managers.
Another way to think about it is what happens when every engineer can marshal the AI equivalent of $10-100m dollars of labour?
My optimistic take is that the profession will reach maturity when we become aware of the shift in the balance of power. There will be more solo engineers and we will see the emergence of software practices like the ones doctors, lawyers and accountants operate.
This is a really interesting take that I don't see often in the wild. Actually, it's the first time I read someone saying this. But I think you are definitely onto something, especially if costs of AI are going to lower faster than expected even a few weeks ago.
I'm tempted by this vision, though that in itself makes me suspicious that I'm indulging in wishful thinking. Also lutusp wrote a popular article promoting it about 45 years ago, predicting that no companies like today's Microsoft would come to exist.
A thing to point out is that management is itself a skill, and a difficult one, one where some organizations are more institutionally competent than others. It's reasonable to think of large-organization management as the core competency of surviving large organizations. Possibly the hypothetical atomizing force you describe will create an environment where they are poorly adapted for continuing survival.
To play devils advocate, the main obstacle in launching a product doesn't involve the actual development/coding. Unless you're building something in hard-tech, it's relatively easy to build the run of the mill software.
The obstacles are in marketing, selling it, building a brand/reputation, integrating it with lots of 3rd party vendors, and supporting it.
So yes, you can build your own Salesforce, or your own Adobe Photoshop with a one-man crew much faster and easier. But that doesn't mean you, as an engineer can now build your own business selling it to companies who don't know anything about you.
a (tile-placing) guy who was rebuilding my bathrooms, told this story:
when he was greener, he happened to work with some old fart... who managed to work 10x faster than others, with this trick: put all the tiles on the wall with a diluted cement-glue very quick, then moving one tile forces most other tiles around to move as well.. so he managed to order all the tiles in very short time.
As i never had the luxury of decent budget, since long time ago i was doing various meta-programming things, then meta-meta-programming.. up to extent of say, 2 people building and managing and enjoying a codebase of 100KLOC (python) + 100KLOC js... ~~30% generated static and unknown %% generated-at-runtime - without too much fuss or overwork.
But it seems that this road has been a dead end... for decades. Less and less people use meta-programming, it needs too deep understanding ; everyone just adds yet-another (2y "senior") junior/wanna-be to copy-paste yet another crud.
So maybe the number of wanna-bees will go down.
Or "senior" would start meaning something.. again.
Or idiotically-numbing-stoopid requirements will stop appearing..
As long as the output of AI is not copyrightable, there will be demand for human engineers.
After all, if your codebase is largely written by AI, it becomes entirely legal to copy it and publish it online, and sell competing clones. That's fine for open source, but not so fine for a whole lot of closed source.
The government is getting rid of tax exemptions for non-domiciled individuals who up to this point did not need to pay tax on foreign source income that was not brought into the UK. The economy overall is in a dire state, Labour are projected to win the election this week in a landslide. The rich are worried about further tax raids from a left wing government.
I had to look this up [1] as it doesn't make any sense. Turns out that is true. No wonder why they are all moving out of UK. The plan was that they could tax them and generate their projected £3B tax revenue. When they are all gone, which means not only do you not get those £3B you also lose all their potential spendings in the city.
Ah.. The UK is complying with OECD guidelines and leaving the race to the bottom.. What percentage of non domiciled have to actually become domiciled to make up for the rest, who paid flat tax and couldn't risk investing in the UK?
I think all countries that are similar are discussing tax reforms or expecting eventual consequences. That doesn't mean all of them are doing the reforms this year.
In the UK, the loss of subsidised childcare and tax free allowances creates an effective marginal tax rate of over 100% for a parent earning £100,000-£125,000.
This isn't quite fair. The problem is that JavaScript expertise is still volatile. It is a use it or lose it skill. It quickly becomes alien unless you stay on the threadmill and keep up with the developments, patterns and tooling. I admit syntax is not actually difficult to relearn but patterns take time to acquire.
I've been programming for over 20 years and the languages and frameworks I favour have a sticky quality. These are the ones I can put away for years, return, get back into it and pick up new features in a few hours at most. The cognitive load of getting back into them are low.
I picked up django around 2011 and moved on to other things including JavaScript at the time. When I needed to build a Django project in 2016, there were some improvements and new features but the syntax, tooling and patterns were the same. It was possible to just jump right back in after a quick skim of the docs. It is possible to develop mastery of this stack, set it down for a few years and return to it with familiarity and continue on that journey.
The JavaScript ecosystem in that period however has pulled off a massive amount of transformation. I learned the basics of jQuery, yui, knockout, emberjs, backbone, coffeescript, ecmascript X, knockout, baconjs, rxjs, react with classes, react with hooks, redux, typescript, nextjs, react server components, react native, graphql, assemblyscript and on and on. It has been impossible for me to develop any deep mastery of anything in the JavaScript space. This does not even include the insane tooling with vague overlapping responsibilities. The closest I have found to reasonable stability in this area has been clojurescript with reagent.
I am constantly stuck in tutorial mode when I need to dive back into the frontend. I recently upgraded a 4 year old Django codebase in 2 hours. I am ripping out all of the react code because it is just not worth the effort any more. The tooling, dependencies and patterns are now outdated. HTMX has that simplicity and elegance that will keep the cognitive load manageable while I focus on the real problems.
> This isn't quite fair. The problem is that JavaScript expertise is still volatile. It is a use it or lose it skill.
This is true of most of almost all living technologies.
> When I needed to build a Django project in 2016, there were some improvements and new features but the syntax, tooling and patterns were the same.
Python and Django both underwent major transformations between 2011 and 2016. That was the 2->3 transition, class based views, channels, packaging was heating up, async came, and there were several syntax and libraries improvements. Were you more comfortable with Python at the time, is that why it was easier to get back into it?
Most Angular 2+ apps written in 2014 have essentially the same architecture as an Angular app written in 2024 (Directives, Components, Services, Pipes, etc, etc).
What you're describing reads like putting down any living technology and picking it back up again a few years later.
> This is true of most of almost all living technogies.
It's not really true for any other programming language. If I'm an expert in Java 8, I can build and ship a product using it. I don't need to be up-to-date in my knowledge of Java 17. Same holds for almost every language.
Javascript is an exception because you don't get to decide which version to deploy, so you have to constantly be keeping up with it.
It’s the opposite problem with JavaScript. JavaScript almost always going to be backwards compatible. But, you’re right, because you can’t control the environment you’re going to be perpetually using “new” features that are 2-5 years old.
For example, I can still run my projects from 2004 but I'll have to wait until 2028 to widely deploy my bleeding edge projects from 2024.
This is exactly right. The lakehouse is a custom data warehouse you can build out of these cloud primitives to suit the specific data needs of an organisation. Think of it as a database scaled up by several orders of magnitude. Everything from storage costs to latency can be optimised as design choices. The common core in this architecture is data held in standard file formats such as parquet, delta tables, avro etc.
One of the most powerful ways to integrate LLMs with existing systems is constrained generation. Libraries such as outlines[1] and instructor[2] allow structural specification of the expected outputs as regex patterns, simple types, jsonschema or pydantic models. Llama.cpp supports bnf grammars.
These outputs often consume significantly fewer tokens than chat or text completions.
The disruption goes both ways. When AI slashes production costs by 10-100x, what's the value proposition of traditional capital? If you don't need to organize large teams or manage complex operations, the advantage of "being a capitalist" diminishes rapidly.
I'm betting on the rise of independents and small teams. The idea that your local doctor or carpenter needs VC funding or an IPO was always ridiculous. Large corps primarily exist to organize labor and reduce transaction costs.
The interesting question: when both executives and frontline workers have access to the same AI tools, who wins? The manager with an MBA or the person with practical skills and domain expertise? My money's on the latter.