Yeah - these are not moats though.

Moats are the logistic network that Amazon has.. ok spend $10bn over 5 years and then come at me - if I didn't sit still...

Moats are what Google has in advertising... ok, pull 3% of the market for more money than god and see if it works..

brand/ux is not a moat, it's table stakes.

What specifically is going on right now in AI that's not based on "hallucinogenic LLM prompts"?

> if you build a huge, general model to perform my task, I can quickly distill and fine tune it to be better, cheaper, and faster.

"better" = "smaller, more specialized, domain-specific"

Is everything that OpenAI or these other proprietary companies do with their models known?

That depends entirely on the design of the context around the content. There is no hard-and-fast rule that AI is bad.

Depends. I like when it's used artistically and you're intended to notice. I've been listening to a lot of AI covers and some of them lean into the artifacts to a high degree in different ways, akin to noise music. First track here is a great example:

https://youtu.be/HgfsKS-Ux_A

But this piece is talking about AI. It seems fitting.

It seems increasingly likely that LLM development will follow the path of self-driving cars. Early on in the self-driving car race, there were many competitors building similar solutions and leaders frequently hyped full self-driving as just around the corner.

However, it turned out to be a very difficult and time-consuming process to move from a mostly-working MVP to a system that was safe across the vast majority of edge cases in the real world. Many competitors gave up because the production system took much longer than expected to build. However, today, a decade or more in to the race, self-driving cars are here.

Yet even for the winners, we can see some major concessions from the original vision: Waymo/Tesla/etc have each strictly limited the contexts you can use self-driving, so it's not a 100% replacement for a human driver in all cases, and the service itself is still very expensive to run and maintain commercially, so it's not necessarily cheaper to get a self-driving car than a human driver. Both limitations seem like to be reduced in the years ahead: the restrictions on where you can use self-driving will gradually relax, and the costs will go down. So it's plausible that fleets of self-driving cars are an everyday part of life for many people in the next decade or two.

If AI development follows this path, we can expect that many vendors will run out of cash before they can actually return their massive capital investment, and a few dedicated players will eventually produce AIs that can handle useful subsets of human thoughtwork in a decade or so, for a substantial fee. Perhaps in two decades we will actually have cost-effective AI employees in the world at large.

There are plenty of real applications that Nvidia is fueling. Things that make money. There will be a reckoning for the hype men, but there is a good amount of value still there.

(As always, the task of the hype man isn’t to maintain the bubble indefinitely, but just long enough for him to get his money out.)

There are more fundamental issues at play, where I see stock price fairly divorced from actual, tangible value, but the line still goes up because people are going to keep buying tulips forever, right?

It sucks because I think investing in the stock market takes away from dynamic investment in innovative startups and real R&D, and shift capital towards shiny things.

I think what they're saying is that Cursor makes money because it's a good editor in general that integrates AI well, not just because of the fact that it uses AI.

If you just slap a ChatGPT backend onto your product, your competitors will do it too and you gain nothing without some additional innovation.

The article heavily emphasises the point that having the "smartest" AI isn't a moat, it's the experience and integrations that build the moat. That's exactly why Cursor is more popular than Aider.

TFA is saying that it's your product that matters, as always, and that using AI can't be your moat since everyone has access to AI.

It seems cursor did a bunch of things right, from choosing to base it on an already popular editor, to the vision and specific ways they have integrated AI, to the flexibility of which models to use. No doubt there was some "early mover" advantage too.

Certainly the AI isn't their moat since it's mostly using freely available models (although some of their own too I believe), and it remains to be seen how much of a moat of any kind or early-mover advantage they really have. The AI-assisted coding market is going to be huge, and presumably will attract a lot more competition.

I'm old enough to remember when the BRIEF (basic reconfigurable interactive editing facility) editor (by Underware) took the world by storm, but where is it now?

Any other former BRIEF users/fans out there ?!

> why does cursor have $100m ARR then?

First mover advantage.

They are not safe against Microsoft, who have the resources to copy every feature that Cursor has into VS code and can afford to offer it for "free" for a very long time and Microsoft also has access to the exact same models as Cursor.

So not only that tells you there is no moat, but offering the best tools and models for free is exactly what Microsoft's modern definition of "Extinguish" is from their EEE strategy.

This makes sense. Cursor makes money therefore they are the only AI code editor

An alternative phrasing is “you can’t build a moat with an AI model”. Which Cursor exemplifies by way of supporting 10 different models and adding more all the time.

What’s different now is that tech companies all have mutually assured destruction pacts.

Many key pieces of AI technology, like transformers, have patents. If you start trying to enforce your “…with AI” patent against Google, they’re just going to turn around and sue you for using their patented technology.

That's fine, but there are many tasks (for example, natural-language processing) where there is no classical way to precisely define the steps you want to be performed, and hand-tuning a list of keywords or heuristics is time-consuming and less accurate than reading through the texts yourself. In this case, providing high-level instructions to an LLM to read through the text and attempt to apply the spirit of your instructions is invaluable.

It's been going downhill for a while, even before AI. The VC-funded React SaaS craze took the "put something barely functional on the web, make some subscription cash" model and scaled it to what is essentially a scam/spam industry.

So if the UX feels increasingly framed in terms of what the "developers" see/want/believe to be profitable, and less from the actual user's perspective, that's because the UX was sketched by hustlers who see software development explicitly as a "React grind to increase MRR/ARR."

This is a good summary of the downturn of Google search. It constantly tries to offer you the kind of results it thinks you should want, not what you actually searched for. Thank god for Kagi and other up and coming alternatives.

Similar vein - I can't even type "2 + 2 = 5" on my iPhone anymore because the keyboard keeps suggesting "2 + 2 = 4" as soon as I finish the first part and autofills it when I hit the space. It's extremely frustrating, and I feel like a keyboard is a prime example of a system that should do what I say. If I make a mistake, I'll correct it.

I don't see integrating AI into UI/UX to be incompatible with enabling the user to do as she intends. In fact, I think the thoughtful use of AI could better help users do what they want. It is annoying when "smart" features completely miss the mark, but it's a worthy pursuit in my book.

> I want my UX in an app to do the damn thing I precisely intend it to do, not what it believes I intend to do

This is pretty weird epistemological phrasing. It's a bit like saying "I want to know the truth, not just what I believe to be the truth!"

I think the "the user doesn't really know what they want" idea has been taken a bit too far.

Basically all applications these days are like this. Rather than assume users are sentient, intelligent beings capable of controlling devices and applications in order to achieve some goal, modern app design seems to be driven by a philosophy which views the operators of applications as imbeciles that require constant hand-holding and who must be given as little control and autonomy as possible. The analog world becomes more appealing every day because of this.

Developers expect UI controls to afford fine-grained control over functions as if they’re a thin wrapper for the API. For example, if there’s some condition that prevents something from happening in a program and there are one or two things you can do to mitigate it, most developers would rather know the process failed and be given the choice about how/when to proceed. Our understanding of what controls ‘should’ do based on a sophisticated mental model of software architecture, networking, etc. We don’t have to deliberately reason about what the computer is doing— we just intuit that it’s writing a file, or making a web request, etc etc etc and automatically reason about the appropriate next steps based on what happens during that operation— sort of like writing code. Knowing that the process failed and how gives us valuable information that we can use to troubleshoot the base problem and possibly improve something.

Nontechnical users do not have that mental model: they base their estimation of what a control should do on what problem they believe that control solves. The discrepancy starts with misalignment between the user’s mental model of the problem, and how software solves the problems. In that hypothetical system where some condition is preventing something from happening and there are one or two things you can do to mitigate it, the nontechnical user doesn’t give a fuck if and how something failed if there’s a different possible approach won’t fail. They just want you to solve their problem with the least possible amount of resistance, and don’t have the requisite knowledge to know why the program is telling them, let alone how it relates to the larger problem. That’s why developers often find UIs built for nontechnical users to be frustrating and seem “dumbed down”. For users concerned only with the larger problem and have no understanding of the implementation, giving them a bunch of implementation-specific controls is far dumber than trying to pivot if there’s a stumbling block in the execution and still try to do what needs to be done without user intervention. Moreover, even having that big mess of controls on the screen for more technically-sophisticated users increasing cognitive load and makes it more difficult for nontechnical users to figure out what they need to do.

It’s a frustrating disconnect, but it’s not some big trend to make terrible UIs as developers often assume. Rather, it’s becoming more common because UI and UX designers are increasingly figuring out what the majority of users actually want the software to do, and how to make it easier for them to do it. When developers are left to their own devices with interfaces, the result is frequently something other developers approve of, while nontechnical users find it clunky and counterintuitive. In my experience, that’s why so few nontechnical users adopt independent user-facing FOSS applications instead of their commercial counterparts.

Network effect, as in TikTok's case, is a big moat!

So far, that is science fiction, and there is no reason to expect a change there.

Yup; AI can't, but AI plus laws made by purchased lawmakers sure can.

I feel like every enterprise ad or marketing site is like this. I have no idea what it does, but it says it'll make numbers go up, so execs buy. It must work to a degree, because it's so common.

The problem is there’s no consumer uses for it, it’s all enterprise.

Salesforce is the culprit here, not AI.

Alright alright alright...you've got some Earnst on you.