My AI Policy

Generative AI tools have become impressive in their ability to synthesize code, text, and various media. One one hand, its training and use have me concerned in terms of ethics, integrity, and personal sovereignty over my digital tools. On the other, it is becoming pervasive in software development, the foundation of my career—at the very least, I must have some familiarity with it, and clear boundaries when interacting with other people who use these tools. Thinking through these concerns lets me frame clear, consistent, and explicit boundaries.

Disclaimers:

Summary

Rationale: I will not pay for access to LLMs

In my opinion, developers of frontier models are working on a moral framework that is incompatible with my own. I will not be complicit. Enumerated below are some aspects of this:

Personal sovereignty
Currently, a paid subscription costs around $20, not cheap, but not too expensive if it is a useful tool that saves a few hours a week. However, it is well-known that usage of frontier models are heavily subsidised but it is not publicly know by how much. I’d rather not become dependent on a tool that can suddenly start costing me $200 a month.
Techno-colonialism
The last two decades has seen our digital platforms have fall under the control of a few large American corporations, run by CEOs with questionable intentions. These platforms are our third-places, central to our communities, social lives and democracies. They have hollowed out the decentralized internet that existed before. With AI, their end-game seems to be having the means of (digital) creation in their control.
Disregard for intellectual property
LLMs have been trained on copyrighted art and literature, pirated books, GPL-licensed code, scientific papers, and so on. Whether or not this usage is legal, it is non-consenting. There also seems to be a two-tier justice system for enforcement copyright law.
(a non-consideration) climate change and the energy transition
In the face of a looming climate crisis, the massive carbon costs and stress to the grid because of AI training and usage cannot be ignored. This is a concern I personally have, yet hesitate to make it a core rationale for my policy, at least directly. At the end of the day, I consider climate change fundamentally a social justice concern—minimizing carbon usage is not an objective in and of itself, but a convenient measure of the harm we may bring upon the most vulnerable in our society. I cannot object to AI in direct terms of its carbon usage because that would be deeply hypocritical and come from a perspective of great privilege. I have used trans-continental flights to enrich myself in terms of my education and personal enjoyment. Though perhaps I also need a personal “climate policy”. My main objections to AI in aspects such as eminent domain being used to build data centers, local energy prices are, however, covered by other rationale, such as ‘Inequity’.
Inequity (unequal availablity)
One of my attractions to opensource and programming is its democratic nature. With (relatively) little capital expenditure (though with copious investment of time), one can teach yourself how to program and make contributions to projects. If LLMs take off, those without access may find themselves left out.
Inequity (unequal costs)
It is inexcusable the Indian goverment is using eminent domain to build data centers.
Incentive to addict
Similar to social media, getting users addicted seems to be in these companies interest. This seems far more worrying and dangerous than the case with social media, with cognitive offloading. If I use this tool regularly I think I can slowly slide into this mode of interacting with it. Not paying for the tool is one (perhaps crude) method of adding friction.

Rationale: AI in Software Development

Synthesising substantial (say 50 lines of non-throwaway code, to place a line in the sand) code comes with significant drawbacks.

LLMs can speed up development, but not by much
While LLMs have become pretty decent at synthesizing code, the time spent reviewing the code negates much of the advantages. Failure to review generated code (even in the absense of bugs) can lead to the LLM making subtle decisions with consequences that I do not understand. This may cause substantial backtracking later.
Reviewing AI generated code exhausts me
Because I am on the lookout for subtle bugs and decisions that the AI has made. I’m constantly aware that the AI has less context about what the code is doing. This is different from reviewing even a junior developers code. In fact, AIs seem to rarely make mistakes that junior developers would. The summary of the changes the AI spits out is rarely helpful, as that is quite verbose and must also be reviewed, adding to the cognitive load.
Less “institutional knowledge” of the codebase
Reviewing code does not give you the same in-depth understanding than if you wrote it yourself. This means that there is less likely to be someone on the team with an in-depth understanding of why particular decisions were made.

While LLMs can be useful for when working with unfamiliar languages or tools, I find the approach of:

  1. Ask the LLM research questions
  2. Write the code yourself
  3. Ask the LLM for a review

This approach lets me muddle along, learning the tool in the process, but puts me back on the idiomatic path through the review. I have a much more in-depth understanding of how and why things are the way they are. The approach of: 1. The LLM writes; 2. I review; feels like learning glass blowing purely through watching YouTube videos. While you will get some understanding, you’d be clumsy at best if you tried to do it yourself.

Postscript: Perhaps at some point AI will get so good that we can trust its code without review and let it write and evolve full applications on its own, but I do not think that day is today, and I do not think that that tech is this iteration of AI.

Rationale: AI in writing

While LLMs could significantly reduce the effort needed to write documents and combat my dyslexia, I feel that writing is too core a part of how I form my thoughts and voice them to trust it to a machine.

Rationale: AI in Art

Art is fundamentally about human expression, emotion, and communication. Sometimes these may come out of the process of creation itself rather than the finished product. Purely or largely AI art is meaningless.