At the end of the day, Microsoft won't care how bad any of this will make them look. Their reputation has been abysmal for decades, but none of it actually seems to have any kind of negative effect on their bottom line.
I would love to know how much of their internal workflows are being handled by AI workflows. Because this seems like the kind of thing your agent might do.
The point of this was always to explore what is possible with AI as quickly as possible. Obviously, there is going to be a lot of waste, but the 5-10% of employees who are truly thinking about it and discovering novel applications are what you are truly after. Because right now, you effectively have a giant, as of yet poorly explored space of potential uses.
Anyone who can find the actually valuable portions of the space early has a potentially huge competitive advantage. Even if the result of the experiment is the negative that AI is actually mostly not that useful, that is still extremely useful information in a time of great uncertainty regarding outcomes.
The bottom line is that this approach may be expensive, but if you have the money to burn, it's far from the worst strategy if you are trying to position yourself correctly for the future.
The thing I don't get though, is that most people just don't have that much work they need to do. I can use AI to pretty easily get my work done just via the regular chat interfaces. But because of the tokenmaxxing metrics that leadership tracks, I end up just having the AI deliberate for hours on random things just so that I can boost my token numbers. I think tokenmaxxing for the end goal you described is only realistic when the engineers are truly buried under a backlog of work.
Not being buried under a backlog of work is one aspect, and the other is that the sheer _urgency_ of these efforts makes it look like companies like Uber could be displaced in a year or two by someone who gets lucky with AI use.
Which absolutely isn’t the case. Even if someone would manage to overtake a market leader on tech merit alone, within 1-2 years, thanks to AI, markets don’t swing on such short notices. The fake urgency is absolutely psychotic.
What’s the huge advantage though? Adopting workflows that give big productivity gains is relatively easy even for big corporations. It’s only an advantage if you can keep it secret.
OTOH maybe we’re in for a future of patenting prompts.
Building a model for predicting the ultimate winner of a US presidential election is particularly difficult, because you are dealing with noisy input data and nonlinear effects, i.e. just a few thousand votes in a few key states can completely flip the outcome. If you then have poorly calibrated polls with a large margin of error, there is really nothing much you can do.
On the other hand, it does raise the question how valuable the 538 models for something like this really are if the outcome is a coin flip anyway.
Exactly, and correlated errors, where a polling error in one state predicts similar errors across the board.
I disagree that it's all pointless though. Most basically it's smart for campaigns to have a good model and let that inform strategy where appropriate. Since the president is a big deal other people's decisions are also impacted, and in the long run it pays to have good predictions of those chances. Also, the outcome sometimes is fairly certain and that isn't always easy to see.
I agree, it's far from pointless. The 538 model is arguably close to the best you can do considering how difficult the task is, but it's important to understand it as purely a reflection of the polling data (and 538's reliability scores for polls), and that polling data is inherently flawed. After all, there are only 2 ways to perform a perfectly accurate poll: either know the outcome a priori, or run the election. We shouldn't be too surprised when models like 538 fail to correctly predict the outcome, because that's not what they represent. It's an analytical tool for understanding the current state of polling.
Anthropic have clearly been working towards this since last year when they started focussing more on building products around their models that could be monetised, instead of competing on the most advanced chatbot. IMO this is part of a broader strategy on their part to tap into the enterprise market, because that is where the money is, not in selling subsidised subscriptions to consumers. This is also what the market will want to see: a credible path towards profitability.
This is not about money for him, this was always about control. When they wouldn't give him complete control over the project, he pulled out and probably expected OAI to fold without his support. But they survived, and he eventually realised that he had made a huge mistake by giving up all of his influence over SOTA AI research.
Trying to trade with generalist LLMs is just an exercise in futility, because none of these models have ever seen the inside of a real trading firm. None of that knowledge is in their training sets.
It's worse. The special lingo doesn't make a good trader.
You can be certain the firms looked ahead and had specialized ML tools built and ready to go. And yet, none of them stand out for success, over the past decades and into this LLM bloom.
The way to riches there is just like during the gold rush: The people making money are the ones selling shovels and canteens and wheat, not the ones running sluice boxes.
They are quants with a deep experience in trading that started to develop general LLMs as a side business, that does not mean their experience is baked into their models.
That is exactly the point. It may be wasteful, but it's the fastest way to explore how AI may actually be useful to your business. Even if 80% of employees are just wasting tokens, you still have 20% who are figuring it out.
It is difficult to believe that you can cobra effect yourself into greatness. I'd rather say the most useful perk for companies doing this is the AI-washing adoption metrics they can report, which will hopefully (for them) increase valuations.
Even if that were true it'd mean that current AI usage is overshooting actual, productive use by 5x. This is a problem when all the AI projections are that the current state is the minimum and future usage will be 10+x.
It does mean that, but in a situation where people don't know what the productive use is you have no other option.
It's like that famous quote about advertising that says "Half my ad spend is wasted, but I don't know which half". 20% of token use is useful, but as you don't know which 20% it is you have to spend 5x more to get that knowledge.
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