Not just not easier, but also a lot slower.

The UX experience for basically all LLMs that I've interacted with is having the AI sneak in where I don't want it and end up taking 10 extra seconds to do what used to be a 0.1ms operation.

The worst place for AI is in search bars, yet that seems to be the first place every product manager wants to put it. I hate waiting forever to get results because AI is poorly searching in the foreground.

I had a conversation the other day with some who told me that one very useful thing with AI now is that you can use it to find hotels in a city. My obvious reaction was that this has been a one or two button press thing on google maps for a very long time. They replied well yeah but now with AI you can tell it to only find rooms that don't go over a certain price.

I think a good chunk of people just do not ever try to understand what buttons do, ever, and it's maddening.

Going to the grocery store is a similar example. For a while I was big on getting my groceries delivered. But then I started to realize that the amount of time it took to fill my cart was basically the same amount of time it took in the actual store, so I wasn’t really saving much time or convenience at all… plus I was missing out on some intrinsic joy of discovering and picking things myself.

> click on the search bar/payee filter and type “McDonald’s.” Sure but you still have to open a dedicated app and that the search is just a string search.

I recently setup Muse (just seems to have the easiest out of the box connectors) to connect to all my accounts over Plaid. I can ask questions like: * Review all recurring transactions. * Show overview of last 12 months of spending by category. * Review last months CC transactions. * Estimate next months expenses.

And then in the same app, I automatically get personal financial and budget information that's actual useful. I can adjust the frequency and type of content just by updating the agent's goal.

I tried using YNAB. It felt so cumbersome compared to just asking AI for the info. I had to make a $300 fake expense to zero out an account minimium balance, I can't treat an investment account as a spending account (without some weird hacks).

Meanwhile, the same AI can cleanup my email inbox, watch my calendar, etc.

And I spent like ~2 days having Claude (over remote-control) create a Monte-Carlo simulation for retirement (including Tax calculations). Works great, I can read the code and make whatever changes I need.

The most friction I still encounter is the connector itself being too opinionated. Plaid's API tries to predict future expenses based on what's detected as a recurring charge; this of course was incorrect if similar Amazon.com charge amounts occur on similar days.

IMO purpose built apps are dead. A sandbox of tools that AI can use is far more flexible. Spending $20/month only needs to replace 3-4 other paid single purpose apps (or identify a single $20 recurring service to cancel) to be net-positive. Plus you don't have to configure it, just ask questions.

I sat through an egregious demo of an AI add on for documentation. The flow was load doc (e.g. pdf) -> data filled into a specialized form view. To really wow me, they showed how I could type in the AI chat interface "change the title to 'abc'" and then it would make the change in the form view.

Why can I not just type the new name into the title field itself? Well, this is supposedly easier and in time the AI will learn and get the title right 100% of the time, so there won't be a need for me to edit it.

This is a major player in the industry, not some random kid.

> Something that is perhaps “underreported” is that a lot of AI solutions aren’t easier to do than just doing the thing.

Even apps can be strictly worse than lower tech solutions.

A local car service I can text with “I need a car to airport XYZ tomorrow at 7am” beats every app flow.

Yahoo Mail tried to add an "AI summary" to the top of all emails. The two line summary was worse than merely reading the two lines in the original email.

its a classical effect of having a hammer and recognising everything as a nail to whack. its pretty tricky to find a problem well suited to a tool like LLMs.

i programming stuff using LLM of which the resultant programs are deterministic and dont use LLMs or tokens is a better or easier approach to having gains. because the resulting program can be easier to use and doesnt cost a lot to run. Automations are a good target, or integratig libraries into other programs. tedious things u know well how to do but take time to do manually. then once done fix a problem or workflow that u need fixed by the solution yielded.

ive not seen 1 app that does calls to an LLM that i find useful, apart from harnesses that produce code (which is only recently becoming more appealing and might depend on how good and fast u can write code, at which im not the best nor the fastest.)

There's a symmetry to things that happened in the mid-aughts as at that time there were some shifts in computing habits as well. For instance I knew a bunch of folks at the time who, instead of typing "www.website.com" into the URL bar, would open up google and type "www.website.com" and then click the top link.

I'd ask why and they'd just shrug, like *obviously* google is the front page for everything on the web.

Your anecdote is spot on and it feels like the people who are doing things that way are channeling similar energy.

Or they just flat out don't work. I was on an insurance website recently, and getting big annoying popups to use their AI agent for help with the site or my plan, even encouraging me to ask questions about my plan. I did have one, very simple, "what's my plan's copay?"

"Sorry, I do not have that information."

So then, "Ok, where do I find it?"

"Sorry, I do not have that information."

What the hell? What is this for? Of course, someone will point out "it's because of regulations" or some technical limitation, sure, I can understand that, but then I must ask the question then why did you think it was worth building?

I think to some extent it's possible to reduce cognitive load even on these simpler actions just because you have to see less unrelated data on what you asked about. I say this as someone who doesn't really use LLM integrations.

And these type of requests consume very little tokens in comparison to almost any programming work.

An AI bot can jump straight to the thing you want, like "How much did I spend at McDonalds over the last year?"

It's worth considering whether you really want the full list of MCD transactions or is that just a step you're used to as a means to getting the answer you want.

I'm not sure about what you use but my primary credit card (a major American bank) has an incredibly annoying interface for searching previous transactions.

That's the whole point. Convert the most basic and mundane thing into a revenue-generating subscription.

Cognitive load. To get something out of a computer pre-ai you needed to enter precise command. Sometimes too precise - looking at you tar.

Here you can phrase it anyway you like and even typo at writing mcdonalds won't stop the machine from giving you the right answer.

That's how I feel about LLMs for programming, too. I've tried using them to write programs for me, but every time, when I finish up it winds up being a wash in terms of time+effort, or it took more time+effort than just writing it myself.

Okay, now do "all my transactions this year for retail stores"? Or "All luxury / deductible purchases"?

Of course when you arbitrarily limit the problem domain to some specific example, you can make any tool seem pointless and stupid. It's when you expand your goals to things that aren't trivial that you get benefits.

Do we need not Mathematica or NumPy because calculators can add just fine?

> a lot of AI solutions aren’t easier to do than just doing the thing

Eh, I could see a case where an AI makes ordering for a group easier.