GMoromisato

I think you're arguing that using AI is unethical and therefore no contest would ever allow games made with AI.

But that's a claim and not a universal truth. I don't think there's anything ethically wrong with using AI to create a game (even artwork), and clearly House House didn't think there was anything wrong with using AI for debugging.

The fact that AI-use is controversial--meaning different people disagree--is what makes me think that we'll end up with a bifurcation. There will be communities that accept it and those that reject it.

Does IGF want to reward human achievement or recognize the best indie games? Both are viable paths. Chess tournaments reward human achievements, for example. But I agree with the OP that it's unfair to not be clear on their criteria.

No matter which IGF chooses, I believe awards will ultimately bifurcate: some other contest will emerge for the other side.

I don't think that's bad, nor do I think allowing AI means "not giving a shit."

Anecdotal, but I have the model write a podcast script of two reviewers talking about a book, one chapter at a time.

Fable (5.1 and 5.2) consistently write a much shorter script (2.5 pages) compared to Opus 5.5 (4.5 pages).

Fable 5.2: https://claude.ai/share/274a96a8-c8a5-4564-9ca1-e914edffc722

Opus 5.5: https://claude.ai/share/da8ab6bd-0987-47dc-9c26-68ebbfb25781

I've been reinventing the wheel since I started programming. Back then, we had no choice--there weren't any open-source libraries. But I also enjoy reinventing the wheel because (a) the wheel can be exactly how I want it, and (b) I get to learn and understand new things.

Coding agents have actually cut both ways for me. I can reinvent the wheel more easily and with higher quality, but I can also integrate an external library much more easily than before.

The other day I needed my system to download SFTP, so I had the agent build an SFTP client. It turned out that I actually needed FTPS, so I had the agent build that too.

Another time I wanted to integrate with Git repos, but instead of trying to reinvent that, I asked the agent to integrate libgit2. Of course, I asked it to add an API that matched the semantics of my system, so from my perspective, the wheel was exactly how I wanted it.

I think what's going to happen is that there will be more vertically integrated platforms that do it all. If you think about it, it's really annoying to cobble together a system from different libraries, each of which might have different semantics.

What we want, as developers, is to have a single, well-designed, integrated platform that exposes all the functionality we need in a consistent way. That's a lot easier now that LLMs can mold code to your exact specifications.

I'm biased, of course, because that's exactly what I'm building with GridWhale.

Fair point.

But I've seldom been in a situation where multiple jobs fit the three circles equally and I need to use a fourth circle ("What the world needs") to decide.

I also think a core tenet of Western culture is that the power to choose your own path (individualism) comes with the responsibility to benefit others (service). E.g., selling cigarettes to kids doesn't fit in the center of the three circles because one shouldn't enjoy that, even if one is good at it and can get paid for it.

Instead of asking, "What does society want me to do," we ask, "How can I make the world better."

The lens to use is the Venn diagram of:

   1. What you like doing
   2. What you are good at doing
   3. What people will pay you to do

For math, if your goal is to prove something that no one else has proven, then all three are under attack.

I think the "egoless" path is taking a 50,000-foot view of mathematics that ignores the above lens. Sure, mathematics will survive and even prosper with AI, but what about the mathematicians?

In time, the profession of mathematics will evolve so a new task is at the center of the diagram--maybe it will be explaining math results to humans or teaching math to future mathematicians. I don't know. But it's easy to see that not everyone is going to be happy with the transition, and many will be left behind.

For software engineering, it depends on what your center is. If you like "writing code", then #3 is under attack. But for those whose goal is, "make something work", all three are still viable and even better than before (or at least faster).

That's why we see so much disagreement on HN about whether AI is good or bad for software engineering.

I'm sure lots of other people have made this observation before.

I don't quite get it. Can Pixar copyright its GPU-rendered films or can it only copyright the human-created models?

How will the USPTO draw a legal distinction between AI-generated and GPU-generated?

My sense, from working in the biotech industry, is that this is going to take much longer than people think. I have two clients using ML/AI for drug discovery and their results have been mixed. There are cases where a drug was identified by ML but then failed during animal testing.

Unfortunately, the feedback cycle in drug development is measured in years (and millions of dollars). I've seen cases where a drug works better than competitors but has a worse safety profile. The solution was to alter the drug to remove the side-effects, but that required a new trial (meaning, two more years plus ten million dollars more).

That said, I do think there is some low-hanging fruit. There are lots of datasets from past trials and it's likely that there are some patterns there that AI could mine. I don't think it will be like software engineering ("we build a Photoshop-clone in 2 hours") or math, but we might see announcements about a new drug candidate being found. Verifying it, however, will take a long time.

I was born in Peru and I absolutely abhor the word "LatinX". I assume people meant well, but ironically, it exhibits the "we Americans known better than you" attitude that liberals usually avoid.

The fact that ETH is false when many people expected it to be true is the amazing thing. This isn't AI finishing the last 10% of a proof and stealing the credit--this is AI exceeding human thinking. If nothing else, this buries the "stochastic parrot" position.

That said, AI is spiky, and I fully expect there to be a class of problems that AI is really good at solving and a (maybe larger) class where AI is worse than human mathematicians.

I don't think the next generation of Democrats is as corrupt as Trump. Jeffries seems like a pretty standard politician, and even progressives like AOC, Mamdani, Sanders, etc. do not seem venally corrupt.

And even Trump's corruption is not the problem. If that were his only sin the US would be fine.

Our political disfunction is caused not by personal failings (although those abound) but by genuine disagreement on which direction the US should take.

I believe countries and civilizations die when they stop believing this.

It is also possible, maybe even likely, that we are the ones who are odd.

Agreed! I only wish I understood anything after page 3.

Microsoft has (and had) some amazing designers. They came up with Metro, which was the forerunner of the flat design that everyone loves/hates now.

Recreational programming is great! I'm always reinventing the wheel because it's more fun for me to (e.g.,) write my own XML parser than to learn some library.

But, IMHO, the difference between writing programs for fun and writing programs that other people find valuable is like the difference between masturbating and making babies. The former is fun, the latter is fulfilling.

There is nothing wrong with having fun. But if you ever find yourself unsatisfied with all your half-built projects, maybe consider writing software that other people value or even love.

Yes, it can be frustrating to try (and often fail) to satisfy others. But seeing someone enjoying your software is an amazing feeling. It makes you feel proud, useful, and talented all at once.

I agree with this take. The complexity is that math professionals are currently being paid based on the theories they prove. Until they come up with a different goal for being paid, this will be disruptive.

My trick for X is to use Lists. I create a separate list for each topic I'm interested in and populate it with the relevant accounts. Keeping the list up to date is relatively easy because I can grow it as people re-post relevant content.

You're right--my level knowledge is 15 years out of date.

For Google I meant L8, which I guess is Principal Engineer now?

You're right about that! Remember that in 2011, Microsoft was considered a zombie-company. No one expected it to grow in value.

Yup! Depends on the company and the person, of course.

Another thing: People at FAANGs usually ride the stock up and get way more than 7% per year. I'm embarrassed to say that I sold a lot of MSFT stock at $30 in 2011. Today (~15 years later) it is trading at $530.

Fair enough--I didn't do the math. Looks like you'd hit $25M after 15 years or so. And I grant that 7% is aggressive.

Still, if you reach staff-level at 40 years old, you could have $25M at 55. That's pretty amazing. But that requires lots of luck. Most people never make it to staff-level. I never did.

Which game would you rather play:

   A. Guaranteed $1,000 each month
   B. Every month, flip a coin. Heads gains $5,000, tails you lose $1,000.

Obviously B has greater expected return, but losing $1,000 hurts. What if you can't pay the rent that month?

To me, that's SPY vs. QQQ. Tech in general has higher expected return, but more volatility. If you can handle the volatility, then QQQ is better. If not, then not.

Ultimately, I'm taking a bet on tech because I believe tech has the greatest chance of improving the economy/world.

[I'm also not qualified, so do your own research]

If you have $5 million USD wealth you're in the top 5% of the US (and top 0.5% of the world).

If you cannot have a happy life on that, well...

But I get it--if your goal is to accumulate more wealth (no judgement--that's a legit goal) then it gets harder. When I was at Microsoft, partner-level engineers were making $1 million per year; now it's probably $2 million. Staff-engineer at FAANG is probably similar, I bet, to say nothing of the AI labs. I bet you could reach $25 million after 10 years with the right investment strategy. And $25 million throws off at least $1.25 million per year if you invest right.

Beyond that, you probably do need a startup or some other significant ownership stake.

I put half in a basket of tech stock (QQQ, etc.) the other half in buying a house. I am not an expert, so don't take my investment advice--do your own research.

I've more or less followed this advice, and it's worked out for me. For the most part, I used my salary to pay for expenses and stock compensation to build up wealth.

The tricky part is that a lot of this advice is situation-dependent. Should you switch to a riskier company that offers a higher compensation? Sometimes yes, sometimes no. Should you work late to accomplish goals hoping to get a promotion? It depends.

So how are we supposed to know what to do?

The answer, in my opinion, is people. You have to trust people who are trustworthy and avoid people who are not. As an employee, your success depends on the people who hired you--they are the ones who can promote you or cheat you. The best leaders are the ones who realize that helping you advance is going to help them advance. Help your boss accomplish their corporate goals and they will promote you--not as a reward, but as a bet that you will help them on the next goal.

Bad leaders are either too selfish (fail to promote you) or too incompetent (unable to rise, even with your help). You need to learn to avoid them.

Therefore, my advice is this: Look at the leaders in your company--people who could plausibly hire you. Which ones are the best bets? Which ones have both the talent and the integrity to succeed? If you're lucky there's more than one and you can work to get on their team. If you're unlucky, there are none and you need to find a different company.

> The AI labs are clearly in control of their bots. It's a computer. Networks are not mysterious.

Yes, exactly. The issue isn't, "we don't know how to sandbox an agent", the issue is that they need to test the agent with full internet access because that's the product they are selling. They are selling a product that does things for you on the web. You have to be able to test it with a real web connection.

Maybe the answer is to simulate the web. What if you ask an agent to reproduce the key parts of the web (travel sites, government sites, etc.); effectively, clone a real, working version of the web, but inside a simulator. Now you could test the agents inside the simulator. Since "coding is a solved problem" maybe that wouldn't be as hard as it sounds. Right?

Agreed!

The argument from the frontier labs isn't, "We're worried that AI is going out of control, please stop us" it is, "We're taking a lot of risks to compete, so please limit competition."

I don't think this is a moral/ethical issue--everyone agrees that the frontier labs are responsible for hacking attempts. No one is trying to deflect blame.

Instead, this is a legal question: What were the damages? Was the target company negligent in setting up security? Were criminal laws broken? Etc. [IANAL, so forgive the lack of precision.]

If hacked companies had losses, they have an incentive (even a fiduciary duty) to recoup their losses. They do that by suing the company (as, e.g., authors sued Anthropic for copyright infringement). If they haven't sued the company, it means either (a) they didn't have losses, or (b) they're still working on the lawsuit and haven't filed it yet.

The other dynamic is that frontier labs feel competitive pressure to take risks to develop their models. Which risk is higher:

A. A new model being tested hacks into a sensitive site (bank, government, etc.) and the company has to pay a massive fine (or even face jail time).

B. A competitor comes out with a significantly better model and they lose significant market share.

That is a hard choice to make. The frontier labs are asking for government restrictions--that apply to all companies--so that they can minimize A without having to worry about B.

They are trying to minimize their risk (and thus maximize their profits).

Agreed! Specifically, compensation (monetary and reputational) for professional mathematicians was bundled into theorem proving--essentially, climbing the mountain. Now that a teleporter exists, we need to unbundle compensation.

I don't know what that means in practical terms, but I agree that's the issue.