Ask HN: What is an optimal game theoretic response to AI adoption?
4 comments
I hope my comment is on topic. If not, apologies. But I think I have some skin in the game here.
I could be the perfect example of someone overreaching. My coding skills are limited, but I’m also not stupid, and I don’t release anything I build without having it reviewed by an expert.
That said, IMHO, humans were already very good at producing garbage before AI.
I think the real distinction is not expert vs non-expert. It is whether the person using the tool has enough judgment and feedback loops to catch bad output.
In my field, AI lets me do much more than I could before. It also lets me explore areas I would normally not touch alone. But I still have my business partner, who is the stronger engineer, check everything I do before releasing anything.
That review step is very important. Without it, I would probably sometimes ship polished garbage.
I’m also not sure quality is as objective as people like to pretend it is. Some things can be tested. But even there, testing only reduces risk. It does not make software perfect. Vulnerabilities are often found years later, in code written and reviewed by experts, and running in production.
So maybe the danger is not AI. The danger is removing the review systems and pretending the output is expert-level just because it seems to be working correctly.
I could be the perfect example of someone overreaching. My coding skills are limited, but I’m also not stupid, and I don’t release anything I build without having it reviewed by an expert.
That said, IMHO, humans were already very good at producing garbage before AI.
I think the real distinction is not expert vs non-expert. It is whether the person using the tool has enough judgment and feedback loops to catch bad output.
In my field, AI lets me do much more than I could before. It also lets me explore areas I would normally not touch alone. But I still have my business partner, who is the stronger engineer, check everything I do before releasing anything.
That review step is very important. Without it, I would probably sometimes ship polished garbage.
I’m also not sure quality is as objective as people like to pretend it is. Some things can be tested. But even there, testing only reduces risk. It does not make software perfect. Vulnerabilities are often found years later, in code written and reviewed by experts, and running in production.
So maybe the danger is not AI. The danger is removing the review systems and pretending the output is expert-level just because it seems to be working correctly.
>> The danger is removing the review systems and pretending the output is expert-level just because it seems to be working correctly.
This is what is happening. In the rush to adopt AI many are abandoning sane engineering principles/processes lest one gets left behind. Only to rediscover them on failure, hoping it is not catastrophic.
This is what is happening. In the rush to adopt AI many are abandoning sane engineering principles/processes lest one gets left behind. Only to rediscover them on failure, hoping it is not catastrophic.
In financial services AI isn't theoretical. We recently build NL2SQL agentic systems for wealth management, systems that work in production are ones where domain experts (compliance, risk, portfolio) collaborated to define the semantic layer first. Solo + AI builds skip that step and it shows immediately under edge cases. SR 11-7 model risk governance exists partly because this failure mode already happened before LLMs existed.
I had a realization that everybody using AI makes things worse off for everybody because a) more of inferior things and b) they are inferior since we would be using it in areas we are not good at i.e. experts in their fields collaborating to build something versus them independently building it with an AI. Even if one assumes the best AI, it is me who when is far out of his competence that my creation is of a lesser quality. These people can still use AI and collaborate to surpass previous frontiers, but the current zeitgeist is that one doesn't need other experts now.
This led to gaming out AI adoption scenario with claude from a game theoretic approach for both an organization and an individual perspective.
The response in part 1 was expected that AI adoption is a dominant strategy.
In part 2, I asked it to reconsider the implicit assumption that adoption is beneficial. It may be good to wait if the cost of adoption is greater than the probability of benefit of adoption. The interesting part is the portfolio approach for the individual, akin to investing.
In part 3, I asked it to break down the cost model for different domains and while it pulled out the numbers out of the hat, the reasoning for software/eda seems like a correct approach but we see the opposite happening today in the industry. What I wanted to ask is what others think these would be for their respective domains.