I disagree in the case of LLMs.
AI already has a massive problem in reproducibility and reliability, and AI firms gleefully kick this problem down to the users. "Never trust it's output".
It's already enough of a pain in the ass to constrain these systems without the companies silently changing things around.
And this also pretty much ruins any attempt to research Claude Code's long term effectiveness in an organisation. Any negative result can now be thrown straight into the trash because of the chance Anthropic put you on the wrong side of an A/B test.
> That being said, vastly reducing an LLMs effectiveness as part of an A/B test isn't acceptable which appears to be the case here.
The open question here is whether or not they were doing similar things to their other products. Claude Code shitting out a bad function is annoying but should be caught in review.
People use LLMs for things like hiring. An undeclared A-B test there would be ethically horrendous and a legal nightmare for the client.
LLMs are non-deterministic anyway, as you note above with your comment on the 'reproducibility' issue. So; any sort of research into CC's long-term effectiveness would already have taken into account that you can run it 15x in a row and get a different response every time.
You can do A/B testing splitting up your audience in groups, having some audience use A, and others use B - all the time.
I think the article’s author is frustrated over sometimes getting A and at other times B, and not knowing when he is on either.
Or, you could, you know, try to understand your users without experimenting on them, like countless of others have managed to do before, and still shipped "great products".
Using A/B tests to safely deploy and test bug fixes and change requests? Totally different story.
Focus groups have their place, but cannot collect nearly the same scale of information.
I don't care, at all, about the "scale of information" for the company's sake.
They are clearly good for both user satisfaction and the companies bottom line.
Edit: how to disable auto updates of the client app https://code.claude.com/docs/en/setup#disable-auto-updates
Where can I sign up?
Yes. I'd like some guarantee that my results are reproducible for some reasonable amount of time. New versions can also introduce regressions. A prompt that works well with today's model might not work with tomorrow's, even if the latter is "better".
These are two very different things. I suspect that in some cases pointing finger at a black box instead of actually explaining your decisions can actually shield you from legal liability...
Obviously it would be unreasonable to accept such terms without further context. The further context in this case being that Anthropic will maintain Claude as an AI agent and seek to improve it's performance. What is at the heart of this issue is whether or not Anthropics recent A/B testing violated that context. Not whether or not they violated the TOS (they didn't, obviously)
If you wanted something more deterministic write it yourself or get it verified, all hosted llms as far as know does neither.
That is a dark pattern to inflict on users expecting consistent output.
People keep complaining about LLMs taking jobs, meanwhile others complain that they can't take their jobs and here I am just using them as a useful tool more powerful than a simple search engine and it's great. No chance it'll replace me, but it sure helps me do ny job better and faster.
Every client is free and encouraged to feed back its financial health: profit for that hour/day/month/...
The AB(-X) test run by the LLM provider uses the correlation of a client's profit with its AB(-X) test, so that participating with the testing improves your profit statistically speaking (sometimes up sometimes down, but on average up).
You may say, what about that hiring decision? One thing is certain: when companies make more profit they are more likely to seek and accept more employees.
Say a particular finetune prioritizes profits right now and makes recommendations like "cut down on maintenance, you can make up for it later with your increased profits and their interest". It produces more profits, and wins the AB test. Later the chickens come home to roost.
You can reduce the problem by using long-term indicators, but then each AB test is very slow.
Evil might be a stretch, but I really hate A/B testing. Some feature or UI component you relied on is now different, with no warning, and you ask a coworker about it, and they have no idea what you're talking about.
Usually, the change is for the worse, but gets implemented anyway. I'm sure the teams responsible have "objective" "data" which "proves" it's the right direction, but the reality of it is often the opposite.
In my experience all manner of analytics data frequently gets misused to support whatever narrative the product manager wants it to support.
With enough massaging you can make “objective” numbers say anything, especially if you do underhanded things like bury a previously popular feature three modals deep or put it behind a flag. “Oh would you look at that, nobody uses this feature any more! Must be safe to remove it.”
No. Users aren't free test guinea pigs. A/B testing cannot be done ethically unless you actively point out to users that they are being A/B tested and offering the users a way to opt out, but that in turn ruins a large part of the promise behind A/B tests.
Yes, I wish software developers were more like actual engineers in this regard.
It's not "unexpected" but it is still unethical. In ye olde days, you had something like "release notes" with software, and you could inform yourself what changed instead of having to question your memory "didn't there exist a button just yesterday?" all the time. Or you could simply refuse to install the update, or you could run acceptance tests and raise flags with the vendor if your acceptance tests caused issues with your workflow.
Now with everything and their dog turning SaaS for that sweet sweet recurring revenue and people jerking themselves off over "rapid deployment", with the one doing the most deployments a day winning the contest? Dozens if not hundreds of "releases" a day, and in the worst case, you learn the new workflow only for it to be reverted without notice again. Or half your users get the A bucket, the other half gets the B bucket, and a few users get the C bucket, so no one can answer issues that users in the other bucket have. Gaslighting on a million people scale.
It sucks and I wish everyone doing this only debilitating pain in their life. Just a bit of revenge for all the pain you caused to your users in the endless pursuit for 0.0001% more growth.
You don't have an answer on "how much should A/B testing negatively affect a user"? So "a lot" would be on the table?
Sorry, but how is A/B testing not exactly that? The experiments may be on non-disruptive things like button color, but they're experiments no less.
The users are also rarely informed about the experiment taking place, let alone on the motivation or evaluation criteria.
Enshittification is coming for AI.