360 karma · joined December 2, 2022
it is trivial now to create synthetic multi-platform personas that generate real looking post histories at unlimited scale. the baseline assumption should be that everything on the internet is fake until proven otherwise.
this is delusional hubris from people who are not real engineers. anyone who actually writes high assurance software or does low level performance optimization knows that the only thing that matters is what can be demonstrated in reproducible tests.
the full stack requires about a 1000 different specialties that each require about 10 years of experience to master. and that is just one computer. we are building distributed systems of millions of these computers, operating at global scale, across dozens or hundreds of legal jurisdictions, which takes the complexity of a single computer, and multiplies it many times over, across several other dimensions.
anyone who thinks that they can understand the systemic effects of changing 0.00001% of this system by reading the code is a dangerously naive fool.
It is just a function. A next token prediction function. Garbage-in/Garbage-out is as true as ever.
Coding is solved. Specification and translating requirements into specifications is an unbounded domain and unsolvable by definition.
What LLMs change is that now you develop the specification through iterative implementation and testing.
You start with a weak specification which produces slop, then you progressively build the specification with different approaches to automated testing. The tests are the specification. Ultimately, what you ship is the tests. They are the only thing that proves the functionality of the program. An LLM can produce code that passes any test suite you give it. If the result is bad, the problem is not the code, the problem is the specification.
This is one of the most senior engineers at Amazon saying that human code review is dead: https://x.com/MarcJBrooker/status/2101005954708021604
Nobody at this level is "vibe coding." They are using LLMs as a tool in a whole suite of tools that they have spent decades mastering, and LLMs happen to be the most powerful tool ever created. When you understand the existing tool suite, and can integrate this new super tool, the results are order-of-magnitude improvements in velocity, with much higher levels of quality and assurance.
Anybody talking about "code," like it is actually important, simply lacks the perspective to understand this.
We went from punch cards, to assembly, to C, to interpreted languages, to frameworks, to AI, and every cycle had the exact same debates.
A lot of it is Ego. Everyone thinks they are smarter than they actually are and that their work is uniquely valuable.
Computer programmers are monkeys who get paid to press buttons. We get paid because we know which buttons to push and in what order. It's a great gig. It's made me more money than I ever imagined possible, and I have fun doing it, but the flip side is that it is the most competitive industry on earth.
If you slow down, fall behind, and refuse to adapt, you will get eaten alive.
I have some sympathy for people, but an the end of the day, if you want to get paid better that 99% of people on earth, you are going to have to work for it. That is not an entitlement, and if you think it is, you will not make it.
But to anyone even vaguely thinking of taking this seriously, go look at what antirez, dhh, jared sumner, mark brooker, and many other real engineers who have ship real things are doing and saying.
Most of these people have spent their entire lives contributing to open source, and they have proved their skill shipping working software and scale for decades. They are really trying to help people by showing and telling them exactly how AI works and how to use it to make better software.
yes. it was solved before but when writing code by hand the cost of building exhaustive test suites was far to high to do it in practice, except in very narrow cases where high assurance was required. now that AI can implement all of the testing frameworks for you it can be done for everything.
> we shouldn't need anymore software engineers
no. the job changes, but the skills that software engineers have are more valuable than ever because they now gate a much higher level of productive output.
corporations aren't really ruthless profit optimizers. micro incentives don't actually favor efficiency. hiring decisions don't actually have much to do with output and productivity. for example: it has been known forever that adding more people to a project usually decreases velocity, but that has never stopped anyone.
technology changes but people don't. AI makes higher quality software faster and at greater scale, and velocity is what is really valuable, so companies that master AI development will be making more money, and they will hire more people, because that is what they do.
Engineers played along with this farce because code review served valuable team collaboration, coordination and management functions, about which the author of the article is correct.
Understanding a system by reading code is harder than understanding a system by writing code.
If AI can generate code at 100X, 1000X, or 10000X human capacity (no ceiling here), and you are gated on code review as your mechanism for system understanding, then a team's productive output will barely increase.
If companies want to compete in the world of AI generated code, human code review has to go. The only question is, what replaces it?
Continuing to apply human code review to AI generated code is negligent, if you are shipping at AI generation speed, with that as your only gate, and no other systems and processes to validate correctness and limit risk.
On the engineering side we can adapt easily.
Code review was never about finding bugs. When we do code review the first thing we check is: "do the tests pass?" Then we look at the change and the test coverage added for it and ask: "does the test coverage adequately demonstrate the functionality of the code?" The we ask: "What is the scope and potential impact of this change?" "What is the deployment and rollback plan and how will we monitor and detect defects after deployment?"
Code review was never about the code. It made the lawyers happy and provided a vehicle for doing the things that actually make systems work.
If we imagine Super Intelligence, where NO human is capable of understanding, then how would it ever be possible for any human to identify what is actually beneficial or not?
This resolves in a paradox, common to all magical thinking. You can certainly wish that some all powerful benevolent entity will solve all of your problems for you, but it is not likely to work out well.
None of this is new. It is the same delusions as alchemy and the same thing that tales about genies warn of.
nobody who has ever achieved notoriety in any intellectual field ever did it to "meet demand." if your goal is to be an interchangeable widget that produces value as part of a corporate machine that exists for the enrichment of your shareholders, you are certainly free to choose that path, but don't imagine that is the limit of human existence. also, don't be surprise when you are replaced by AI, because it is a superior widget.
Not at all, because open source contributors aren't megalomaniacs trying to dictate how people use their products. In fact, the exact opposite. That is the entire point of Open Source. Complete freedom for the user.
Their argument, in effect, is that we should throw out the entire Constitutional order and make Dario Amodei dictator of the world. The psychopathic hubris is laughable.
* How much money did Anthropic and its associates donate to the Biden campaign and associated entities? * How much money was spent on lobbying by Anthropic and associated entities? * How many Biden officials were hired by Anthropic after they left office? * How exactly did Anthropic get exclusive no-bid contracts to be the Federal Government's sole AI provider for classified systems?
And with this corrupt influence, and the money that it earned them, Anthropic has attempted to:
* Stoke public fear and panic about AI * Stoke conflict with China * Eliminate domestic competition * Suppress international competition * Degrade their models in order to limit their ability to create competitive products * Train their models on their customer's proprietary data in order to steal their intellectual property
Anthropic played a game and they lost. They can wrap that up in whatever sanctimonious moralizing clap-trap they want, but nobody believes a word they say, or will ever trust them, so they might as well be talking to the wind.
OpenAI may be playing the same game but at least they have the intelligence and humility to read the room, recognize a failed strategy, and adapt.
Who wants to buy intelligence from people who are stupid enough to publicly try to dictate terms to the US Military? Complete and utter brand destruction for everyone involved.
The other outcome would be clearly inequitable: forcing the counter party to eat the loss for your irresponsible use of an AI agent.
If you don't like AI slop. Don't read it. But wasting your time generating human slop to complain about AI slop is so obviously futile that it immediately identifies the writer as lacking the capacity for reason or emotional clarity, or merely seeking attention for their self-promotion with clickbait.
Programming with AI enforces some good disciplines, which were always true, but could be avoided doing it by hand. Most importantly: you are shipping your tests. If you don't have reproducible automated tests then it probably doesn't work.
AI is not fake and it does work, but what I am saying is that from a pure systemic analysis perspective, you can do the numbers, and even if AI was complete fugazi, the benefits you get from the electrical generation capacity, and the ability to fund it through private markets, which bypasses Congress, and locks in commercial contracts (often with foreign governments) which will be almost impossible politically to reverse, would still make it optimal from a strategic perspective. That is my calculation, and to the extent that it is correct, I would assume that the US Military's strategic planning apparatus would arrive at the same conclusion.
AI compute has some unique characteristics that make it especially useful for grid management. Moving consumer compute to the cloud means that the electrical use of that compute can be centrally managed. In an emergency, you can cut electrical use for consumer AI by 50% or more, because chips run more efficiently at lower power, and you can shift workloads onto quantized models, reduce resolution for video output, etc, to reduce compute, which leads to minor service degradation but not interruption. AI datacenters are also adding massive amounts of battery storage capacity, which is an additional grid buffer. For every GW in capacity added by hyperscalers that is creating a dispatchable reserve capacity of 50% under completely normal circumstances (hyperscalers do this internally to optimize their own costs) and then that number goes up depending on the scale and duration of the emergency.
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This is my observation from using it without an specific context engineering to optimize for Deepseek's cache compression and sparse attention mechanisms. I am pretty sure that if you specifically structure your context to align to the cache compression boundaries you can significantly improve performance in the full 1M context, but there is not much reason to do this, because if you design your outer loop to work with shorter contexts that solution is portable and more efficient, so I haven't bothered with a optimizing for DS at this point.
For an example of my real token usage for a day with DS: Input (Cache hit) 530,949,760, Input (Cache miss) 7,875,004, Output 1,389,685 - it is still 1/5th the price of Baidu (the cheapest) and 1/7th-1/10th the price of US hosts.
Also, Deepseek platform is not the same thing as Deepseek open weights. There is a major misconception that the existence of an open weights model means that it is the same thing as the proprietary platform offering, but that is definitely not the case.
AI is really all about electricity. AI could be completely fake and yield zero value whatsoever and the US would do exactly what it is doing now because the AI bubble is what creates the market for building new electrical generation capacity, which is needed for re-industrialization. Also why our friends in UK/Europe/China are so busy pushing anti-AI propaganda to try to undermine this.