Oh god, the whole world become a boring mess of sameness, won't it. What a world we have created as the tech industry. Absolutely dumb as rocks we are.
I think it is from the business side, rather than the software side.
The business side's goal is to obtain a monopoly and extract rent. You can see it in google search getting worse so they can show more ads, you can see it in Apple's app store behavior, pretty much all the examples.
The objective is not to provide a good product that people want to buy, except insofar as that drives adoption towards a monopoly
I have sort of come to that believe anti-trust may be the solution to finding more successes and enabling better products
Business education has taught everyone that spending $100 to earn $120 that you can live confortably on, is for suckers. The real goal is to then spend $80 to earn $200 the next year, and then spend $60 to earn $1000 the following year, and then to spend $40 to earn $10,000 the following year, and then to spend $20 to earn $100,000, and so on. Growth for no other purpose than growth.
None of those reports are any good though. Maybe for shallow research, but I haven't found them deep. Can you share what kind of research you have been trying there where it has done a great job of actual deep research.
Deep Research hasn't really been that good for me. Maybe I'm just using it wrong?
Example: I want the precipitation in mm and monthly high and low temperature in C for the top 250 most populous cities in North America.
To me, this prompt seems like a pretty anodyne and obvious task for Deep Research. It's long, tedious, but mostly coming from well structured data sources (wikipedia) across two languages at most.
But when I put this in to any of the various models, I mostly get back ways to go and find that data myself. Like, I know how to look at Wikipedia, it's that I don't want to comb through 250 pages manually or try to write a script to handle all the HTML boxes. I want the LLM/model to do this days long tedious task for me.
That's actually not what deep research is for, although you can obviously use it however you like. Your query is just raw data collection—not research. Deep research is about exploring a topic primarily with academic and other high-quality sources. It's a starting point for your own research. Deep research creates a summary report in ~10 min from more sources than you could probably read in a month, and then you can steer the conversation from there. Alternatively, you can just use deep research's sources as a reading list for yourself so you can do your own analysis.
I think we have very different definitions of the word 'research' then.
I'd say that what you're saying is 'synthesis'. The 'Intro/Discussion' sections of a journal article.
For me, 'research' means the work of going through and getting all the data in the first place. Like, going out and collecting dino bones in the hot sun, measuring all the soil samples, etc. - that is research. For me, asking these models to go collate some webpages, I mean, you spend the first weeks of a summer undergrad's time to go do this kid of thing to get them used to the file systems and spruce up their organization skills, see where they are at. Writing the paper up, that's part of research sure, but not the hard part that really matters.
Agreed—we're working with different definitions of "research". The deep research products from OpenAI, Google Gemini, and Perplexity seem to be more aligned with my definition of research if that helps you gain more utility from them.
It's excellent at producing short literature reviews on open access papers and data. It has no sense of judgment, trusting most sources unless instructed otherwise.
Gemini's Deep Research is very good at discriminating between sources though, in my experience (haven't tried Claude or Perplexity). It finds really obscure but very relevant documents that don't even show up in Google Search for the same queries. It also discounts results that are otherwise irrelevant or very low-value from the final report. But again, it is just a starting point as the generated report is too short, and I make sure to check all the references it gives once again. But that's where I find its value.
The funny thing is that if your request only needed the top 100's temperature or the top 33's precipitation, it could just read "List of cities by average temperature" or "List of cities by average precipitation" and that would be it, but the top 250 requires reading 184x more pages.
My perspective on this is that if Deep Research can't do something, you should do it yourself and put the results on the internet. It'll help other humans and AIs trying to do the same task.
The project requires the full list of every known city in the western hemisphere and also Japan, Korea, and Taiwan. But that dataset is just maddeningly large, if it is possible at all. Like, I expect it to take me years, as I have to do a lot of translations. So, I figured that I'd be nice and just as for the top 250 for the various models.
There's a lot more data that we're trying to get too and I'm hoping that I can get approval to post it as its a work thing.
Sounds like the you're having it conduct research and then solve the Knapsack problem for you on the collected data. We should do the same for the traveling salesman one.
How do you validate its results in that scenario? Just take its word for it?
Ahh, no. We'll be doing more research on the data once we have it. Things like ranking and averages and distributions on the data will come later, but first we just need it to begin with.
If you have the data, but need to parse all of it, couldn’t you upload it to your LLM of choice (with a large enough context window) and have it finish your project?
Well remember listing/ranking things are structurally hard for these models because you have to keep track of what it has listed and what it hasn't, etc.
The failure mode is that people unfamiliar with a subject aren't able to distinguish careful analysis from bullshit. However the second failure mode where someone pointing that out is assumed to be calling people stupid is a longstanding wetware bug.
If you are at Faang and feeling imposter syndrome, maybe you just need to get better at things. I actually rejected multiple offers from Faang back in the day because they were so mediocre.
That would be nice but up to now there's been no real consequences for Apple, the operators of the biggest walled garden. MS has also been a pretty bad actor in many ways, although their platform is slightly open, for now.
Yeah, unfortunately, they have to jump on the AI bandwagon because they are forced to by other editors, providing free AI, but they simply do not have the skills to integrate the AI properly. It's a shame, and unfortunately removing negative reviews will not help as people will simply migrate to a different product. You can have three 5 star reviews, but that doesn't help if nobody else is using it.
I think this is a common gameplay from companies which are basically reaching their peak of what they will be able to do, but they have to keep growing somehow. Duolingo is basically a dying company if you think about it, like the growth is done, the other company which recently made news, Shopify is another one of these. You do not see them 10xing their revenue in any way again, so they have to play all of these games to squeeze more from the company by basically cutting people, and these are all steps to that goal.
I would expect again other companies like Dropbox to also follow a similar path.
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