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721 points ralusek | 2 comments | | HN request time: 0.001s | source
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ryandrake ◴[] No.41870217[source]
I'm making some big assumptions about Adobe's product ideation process, but: This seems like the "right" way to approach developing AI products: Find a user need that can't easily be solved with traditional methods and algorithms, decide that AI is appropriate for that thing, and then build an AI system to solve it.

Rather than what many BigTech companies are currently doing: "Wall Street says we need to 'Use AI Somehow'. Let's invest in AI and Find Things To Do with AI. Later, we'll worry about somehow matching these things with user needs."

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crazygringo ◴[] No.41870929[source]
This feels extremely ungenerous to the Big Tech companies.

What's wrong with trying out 100 different AI features across your product suite, and then seeing which ones "stick"? You figure out the 10 that users find really valuable, another 10 that will be super-valuable with improvement, and eventually drop the other 80.

Especially when if Microsoft tries something and Google doesn't, that suddenly gives Microsoft a huge lead in a particular product, and Google is left behind because they didn't experiment enough. Because you're right -- Google investors wouldn't like that, and would be totally justified.

The fact is, it's often hard to tell which features users will find valuable in advance. And when being 6 or 12 months late to the party can be the difference between your product maintaining its competitive lead vs. going the way of WordPerfect or Lotus 123 -- then the smart, rational, strategic thing to do is to build as many features as possible around the technology, and then see what works.

I would suggest that if Adobe is being slower with rolling out AI features, it might be more because of their extreme monopoly position in a lot of their products, thanks to the stickiness of their file formats. That they simply don't need to compete as much, which is bad.

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1. ryandrake ◴[] No.41871104[source]
> What's wrong with trying out 100 different AI features across your product suite, and then seeing which ones "stick"?

Even the biggest tech companies have limited engineering bandwidth to allocate to projects. What's wrong with those 100 experiments is the opportunity cost: they suck all the oxygen out of the room and could be shifting the company's focus away from fixing real user problems. There are many other problems that don't require AI to solve, and companies are starving these problems in favor of AI experiments.

It would be better to sort each potential project by ROI, or customer need, or profit, or some other meaningful metric, and do the highest ranked ones. Instead, we're sorting first by "does it use AI" and focusing on those.

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2. crazygringo ◴[] No.41871171[source]
What you describe, I don't see happening.

If you look at all the recent Google Docs features rolled out, only a small minority are AI-related:

https://workspaceupdates.googleblog.com/search/label/Google%...

There are a few relating to Gemini in additional languages and supporting additional document types, but the vast majority is non-AI.

Seems like the companies are presumably sorting on ROI just fine. But, of course, AI is expected to have a large return, so it's in there too.