Ferrari makes hypercars, they know a thing or two about making aerodynamics look good. It's a primary concern of all their designs and yet all their other designs look a lot better than this.
I think they are just falling into the same trap all other manufacturers do at first. They think the customer buying the EV is a different customer, who didn't like their other cars. So they make the techno-future mobile for a customer that doesn't exist.
Just make the same cars with an EV drivetrain, that's what the person who loves your brand but is in the market for an EV wants.
Legacy car manufacturers have done just that (forcing an EV into an ICE chassis). The results generally suck and the pure EV manufacturers like Tesla and BYD have kicked their ass in the market.
You can use a similar design to your existing fleet without a literal retrofit of an existing chassis to shoehorn a battery and electric drive train in there.
The retrofits usually are less preferable not only because of pointless inconveniences like transmission tunnels, but because they'll be the manufacturer's first toe dipped into the EV waters. The retrofit chassis speaks to either a rush to market, or a cautious approach not wanting to commit too many resources. The former says it'll have issues, the latter says they might bail on it and leave you stranded for service and repairs. Or both at once.
That was kinda different thing. It was legacy manufacturers scrambling to push out any EV they could get together so they are not left behind too much. But in meantime they started working on genuinely new designs (like Hyundai Ioniq, Mercedes EQS, BMW Neueu Klasse) or they adjusted their platforms to better accommodate electric drive trains (like Audi e-tron).
I wonder this in general, what's the impetus for writing new frameworks and such? Are we already seeing a slow down in that space? HN front page certainly paints that picture.
You're better off plonking down an existing framework and getting all the structural boilerplate benefits the LLM can leverage.
LLMs are far better at frameworks they have a lot of training data for, if have been around for a while. They write more idiomatic, ecosystem friendly code. Does that still matter?
They are also not getting the same quantity or quality of data as was possible in the first years of "ingest". Compared to the beginning, from here on it is more like a drip feed of new training data. Still immense volumes of data, but we are talking 1 year of data production from society versus centuries of text and data ingested in a short time frame.
For pre-training, yes. But for post-training you need high-quality labelled datasets for reinforcement learning. So far AI has been most successful in coding, because you can translate the usage into such datasets, and thus produce a virtuous cycle: More usage produces more data, which produces better models, which drives more usage.
The question is whether this same model can successfully be applied in disciplines like medicine, law, engineering, etc.
I think it's a mix across the different kinds of people.
For me my car isn't loud right now, but I do just genuinely enjoy the thrill and sound of the car in "track" setup. It's too loud to drive on the street but it's a thrill on track. The loudness isn't the point and I wish it were quieter, but the different exhaust components give it the raw visceral sound that I love.
I guess you can think of it like the difference between music on the TV or music at a concert, the sound is literally different not just louder, and the context makes everything more visceral.
Happened for my car community too, anecdotal of course.
Two aspects I think, the clout chasers move on, and the remaining cohort are older with a bit more empathy for community and also better things to do than provoke the cops. No speaking for everyone with that last bit, there are still those that thrive on the chaos.
There's also tech to solve for having your cake and eating it too. Get a valve, loud for track days, quiet for the commute.
Outsourcing seems to come in cycles, where it's tried, fails due to communication issues (resulting in quality issues), then things get inhoused again.
I do think there is some opportunity for AI to smooth out the communication aspect, but I think what we will actually see is larger volumes of poorly guided work coming through for each feature. The AI does not fix the lack of deep systems understanding which is why inhousing is always the antidote to bad outsourcing.
I need to make this clear, there are great devs on either side of the various oceans, the issue is usually communication between two parties with nuturally mis-aligned incentives.
I’ve had a lot of success in past with the Apple approach. I design and architect locally but build it overseas. I think AI and the post-WFH office work culture really helped executives get over the hump / learn to make decisions and lead without being in the same physical space daily. Also, feel like the communication gap is largely a solved problem at this point. It is incredibly common to find English speakers in this profession from any country. The trick is learning to project management. At times, you simply just give the person objective instructions of what to build and the exact rendering and color palette. Or the exact packages you they can use as dependencies. But largely the world communicates together much better than the previous wave of outsourcing.
I seem to be totally outside the hype bubble, but I have to suspect there is a lot of imagineering and wild extrapolations in the elss technical hype bubbles. I am curious but no enough to go looking.
I'm surprised you say that because it is all over Hacker News. Every single post is co-opted into promoting AI. Try finding a submission with fifty points or more than doesn't have AI or LLM's mentioned somewhere in the comments.
That's a good point, I guess I see the Hacker News hype a bit more realistically then maybe I should. HN has definitely changed in the sense that I rarely see interesting technology or achievements hit the front page, that aren't AI related. It feels like AI has taken all the oxygen from the room.
My little related insight, is that maps are extremely lossy. Even satellite maps. Life on the ground, is full of detail.
When I travelled Japan specifically, maps didn't tell you much at all. It might look like a residential deadzone from high up, but be bustling with cool stuff to do when you walk through.
That is one possibility (that is playing out). Another one worth contrasting is the idea of AI as leverage for the worker. If you can take a regular developer and augment their output by 25%, then they have become more valuable to you and you should pay them more. Why should you pay them more? Because the market rate will price in that they provide more value now and you'll lose those workers to competitors if you don't.
That's a pretty old economic idea, and it will be interesting to see if it holds up in this instance. I have no idea how this all plays out. I do think it won't be one size fits all though.
Given that the user between your comment and mine is a 1 day old account that did not address my comment at all and instead hallucinated a response, I assume they are a bot.
I answered your question. AI-assited programmers will be paid less since more can do the same job through the use of AI assistance. In some cases, they will even can some developers if productivity goes up enough, and the team can reach business objectives with fewer people than pre-AI times. At the same time, these coders will become more and more dependent on AI, and as I'm sure you know, the API is priced so that they make more money than they lose per request. More and more usage = more and more revenue.
This pattern is only going to become more extreme year after year. I used to reject the idea that LLMs could produce useful code or debug things, but these days, we have Claude Opus and chatGPT Codex. And just around the corner, there's Claude Mythos. I believe it's ready to go out, but they are scanning OSS to give the code underneath it all a head start to fix the types of security issues Mythos can find before releasing the product. Otherwise, we could be talking an LLM jailbreak into a "scan this popular Java logging library" or "this popular OS operating system, Linux, for security flaws." If they didn't do it this way, there could have been a lot of damage to PCs, companies, government, bureaucracies, and institutions in general.
If I were to steelman your position, for now, people need to be a good dev to make the most out of the LLM ecosystem, and the skill of prompt engineering varies person to person as well. I could see an exceptional dev outputting not just more than they used to but with their improvement relative to themselves being much higher than the average improvement other devs gained. In that scenario, yeah, salaries could still increase despite the role being AI-assisted and despite the LLM tools costing these devs' company money every query. Skill varies anywhere between "vibe coder" all the way up to the highest position that still codes at your company, and familiarity with how to leverage LLMs the best can vary that widely as well.
Right now, the name of the game is making sure your LLM has a good action plan before letting it attempt to fix a bug or refactor or add a feature. Devs with more experience know what to ask Claude to do whereas a greener dev doesn't know the questions to ask, leaving Claude to guess right sometimes and wrong sometimes. Simply put, if a dev doesn't know to ask for something, there's a bigger chance the LLM won't care to do it. And if there's some nuanced, tricky aspect to the code not described to the LLM, the LLM might burn a lot of tokens to reach a bad solution. A good dev might give more clues and hunches and more context to fine-tune the prompt so that it almost definitely succeeds whereas a greener dev doesn't have intimacy with the system yet, needing tips and descriptions of subsystems themselves before they could pass it along to Claude. By this stage, people also differ in their skills with the various tools in the ecosystem. Power tools do a lot more in the hands of a seasoned handyman than in the hands of an eight-year-old after all.
However, the better LLMs get, the less differences like this will exist, and ideally, every dev will approach a similar amount of productivity. Salaries aren't reflective of how much profit a worker produces in the company (unless you are the CEO or a little below them or maybe have some stock). Supply and demand drive salary. If something nearby AGI arrives tomorrow, by definition, almost any two devs will provide similar value at which point teams will downsize, yet productivity will hold steady or increase. They will downsize to save some money since we live in a brutal world where workers have no loyalty to a company, and a company has no loyalty to its workers. Pensions are a relic from the past. After all that will happen, a large group of qualified devs will be searching for jobs, so they can remain in a home with food in it. Companies will see tons of resumes flowing in all by AI-assisted devs that can do the job.
The companies will then do two things: Offer the hired devs a transfer to AI-assisted dev for less money or else while also interviewing all the ones that all the companies fired, giving them that same AI-assisted dev salary to everyone in the picture. And they will have calculated the proper discount off the old full salary using some kind of economic equations. Then the wildcard happens: some of the ones needing work urgently start to offer their services for even less than the company is. It's a spiral downward until the salary becomes so low to the point where a dev would rather be an ex-dev doing something else that is more relaxing and also still paying them enough money to survive. No reason to do tough coding work, it'll still be tedious with stronger LLMs. Comfort and relaxation will prevail for many as they no longer feel the salary justifies doing the work.
And at the top, assuming AI costs do not exponentiate, they will be making more money than ever before since they downsized teams, slashed salaries, and got hired at even lower salaries than the slashed salaries. (There will still be a premium for knowing the systems like the back of your hand without need to ramp up before adding value, so you'll get paid more than a new hire.)
I think they are just falling into the same trap all other manufacturers do at first. They think the customer buying the EV is a different customer, who didn't like their other cars. So they make the techno-future mobile for a customer that doesn't exist.
Just make the same cars with an EV drivetrain, that's what the person who loves your brand but is in the market for an EV wants.
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