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How do you decide which new AI tools are actually worth adopting?

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Over the past year I've noticed something unexpected. Building products with AI keeps getting easier.

Choosing which AI tools to use keeps getting harder.

Every day there are new models, coding assistants, design tools, agents, MCP servers, and productivity apps. I love seeing innovation, but I also wonder whether we're reaching a point where too much choice is slowing us down.

As a founder, I've started becoming much more selective about what I try instead of chasing every launch.

I'm curious how others here approach it.

What's your rule for deciding whether a newly launched AI product deserves your attention?

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My rule ended up being pretty unromantic: I'll try almost anything once, but it only earns a permanent spot if it makes an obvious, almost annoying difference — not a nice-to-have, a "wait why was I doing this manually before" difference.

Tried Antigravity the day it launched, poked around, quietly went back to Claude by that evening — nothing wrong with it, just wasn't different enough to justify the switching cost. ChatGPT to Claude was the opposite story — the productivity jump was big enough that I'd basically already switched before I'd finished "evaluating" it.

Kind of feels like dating apps for tools at this point. Plenty of good-looking profiles, but I only commit if it visibly changes how my week goes.

 

I really like how you described it, "a wait, why was I doing this manually before?" That's such a high bar, but it's probably the right one.

And that last analogy made me laugh 😂. "Dating apps for tools" is surprisingly accurate.

Thanks for sharing your perspective!

For me, it has to solve a problem I already have.

I don't usually try a new AI tool just because it's using a new model or has some cool feature. If it can save me much time in something I already do regularly, then I'll give it a try.

 Hossein, I like that approach. I think that's becoming the healthiest way to evaluate AI tools. I'm trying to be much more intentional about that too. Thanks for sharing your perspective! 🙌

my rule is basically: can i replace something i already do manually every week with this, or is it just a nicer wrapper around something i already have. a lot of launches are the second thing wearing new branding. the ones that actually stick for me are the ones where i stop tab-switching to a different tool entirely, not the ones that do the same job with a prettier UI.

the other filter i use, especially for anything AI-agent related, is whether the maker can tell me in one sentence what happens when it's wrong. tools that are upfront about failure modes tend to be built by people who've actually shipped it to real users and hit the edge cases, versus a demo that only shows the happy path.

 I really like this framework. The "does it replace a tool instead of just looking better?" question is something I should probably ask more often. Great perspective! 👊

 appreciate that. curious to flip it back on you as a founder yourself - when you're pitching Lancepilot to someone skeptical, what's the one failure mode you're upfront about rather than let them discover it later?

 Honestly, I did something a little crazy once. 😄

Instead of trying to convince a skeptical prospect, I connected with them on another platform and then immediately used Lancepilot itself to reach out. Seeing the product in action caught them completely off guard, and it instantly got their attention.

That said, I still believe in being transparent. If someone's looking for a full CRM or a complex outbound suite, I'll tell them Lancepilot isn't trying to be that. I'd rather set the right expectations than overpromise.

 the reach-out-with-the-product-itself move is bold, but respect it, and the honesty about scope after is the part that actually earns trust long term. setting the wrong expectation is usually what kills adoption in month two, not month one.

I usually give a new AI tool one real workflow and one week. If it saves time only in the demo but creates extra checking, exporting, or context switching afterward, I drop it.

My main filters are pretty simple: does it replace a repeated task, fit into the tools I already use, and make mistakes easy to spot and recover from? I also check whether I can export my data and leave without rebuilding everything.

A tool earns a permanent place when I naturally stop reaching for the old workflow, not when it simply looks impressive on launch day.

 I really like your "one real workflow, one week" rule. I think that's a much better test than spending 15 minutes with a shiny demo.

And I completely agree with your last point, the best tools are the ones you naturally keep using without thinking about it. Thanks for sharing your framework! 🙌

my rule is a variant of 'who is using it.' not who is launching it. not who is tweeting about it. not benchmark numbers. which specific person i already trust has built it into their weekly workflow, and can name one concrete thing it changed for them. that filters out 90% of the noise instantly. tools with breathless launch coverage but zero warm intro usage almost always turn out to be interesting demos that dont survive contact with real work. tools that get quietly recommended in a DM by someone whose taste i've verified over years almost always earn the slot. trust transfers through people, not through content. thats also why the rest of the market trying to solve discovery with more content is backwards. its a trust problem, not an information problem.