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Something is changing in the unit economics of software

nicolo.xyz

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Re: Something is changing in the unit economics of software

#11

Post by chr15m »

"Inference" is just software running. It has always cost money to run software, it's just that it is generally too cheap to matter. If a client makes a regular API call to your server, you pay for that compute, probably in the form of a flat hosting fee. If too many calls come in and workload goes up, you pay for a more expensive hosting tier to handle it (or do dynamic scaling which is per-unit of compute).

Right now the "hosting" cost for inference is per-unit because it's new and expensive, but that won't last.

There is a lot of inefficiency right now keeping prices elevated. That will change very fast and soon paying for inference will likely resemble paying for hosting your app.

The bigger problem for SaaS is that the floor has risen - people can build their own solutions for things that they used to buy SaaS for. So the industry needs to level up and solve harder problems.

Re: Something is changing in the unit economics of software

#12

Post by Spooky23 »

It depends on the solution. If AI is generating value, you can charge for the value.

Most SaaS already works this way. M365 or Adobe Creative Cloud are great examples. They value it like a life insurance policy and find ways to make you sticky. It’s easier to just buy it.

The first round of AI products suck because they are not well defined. Copilot only makes sense if you do shit in office and SharePoint isn’t a dumpster fire. In my large O365 environment the bottom 50% of users use less storage than the top 2%. So why would i buy copilot for my janitor?

When M365 E9 reconciles invoices automatically with Excel, I’ll pay $150/mo and fire a bunch of people.

Re: Something is changing in the unit economics of software

#13

Post by skinfaxi »

SwellJoe wrote: post #9

This is one reason I've been trying to figure out tasks (and products based on those tasks) that can be pushed to the edge, either via small specialized models or small general purpose open models. I suspect the same desire to keep unit costs low is part of why Google is falling behind on the "frontier", but seemingly at the lead, or near it, on models that run on-device. I think they're just focused on making models…

> And, even with specialization and fine-tuning, often a high-end general purpose model is going to do a better job and people don't need a bunch of local tools installed to do their various tasks.

I think this is the critical point that would be interesting to see if it holds. Technology seemingly tends towards increased specialization.

Re: Something is changing in the unit economics of software

#14

Post by carlosjobim »

"Users began expecting something fundamentally different from software: not just tools that store and retrieve, but products that reason, generate, and respond."

Absolutely not. Customers want systems for sales, reservations, accounting, and taking stock. That's where almost all the SaaS money is and none of it benefits from AI - and never will.

Re: Something is changing in the unit economics of software

#16

Post by euazOn »

chr15m wrote: post #11

"Inference" is just software running. It has always cost money to run software, it's just that it is generally too cheap to matter. If a client makes a regular API call to your server, you pay for that compute, probably in the form of a flat hosting fee. If too many calls come in and workload goes up, you pay for a more expensive hosting tier to handle it (or do dynamic scaling which is per-unit of compute). Right no…

Exactly my point in another comment. Just to illustrate this further: a rough ballpark of how the cost of intelligence fell since 2022 could be about 1000x, and continues to fall. Unfortunately, it’s really hard to measure.

It’s so cheap that companies choose to spend more on AI inference (more reasoning, more capabilities, longer context), not less - see Jevons paradox.

Re: Something is changing in the unit economics of software

#18

Post by roncesvalles »

>Users began expecting something fundamentally different from software: not just tools that store and retrieve, but products that reason, generate, and respond.

Not really.

>Every inference call costs money.

Not really, either. If you buy your own GPU, rack it, and run an open model, there is no unit cost. This is just expensive hosting infra. You also pay unit costs for SaaS that your software uses (things like SMS etc).

Re: Something is changing in the unit economics of software

#19

Post by Ozzie-D »

The inference cost discussion is interesting but I think it misses the more consequential shift. The real change in unit economics isn't what it costs to run the model — it's that the marginal cost of building a new feature dropped by an order of magnitude.

Previously the bottleneck was engineering time. Now a competent person with a frontier model can prototype in hours what used to take a team weeks. That compresses the cost side but it also compresses the moat. If your product can be rebuilt by a motivated person in a weekend, your pricing power evaporates regardless of your inference costs.

The SaaS companies that survive this will be the ones whose value comes from network effects, proprietary data, or integration depth — not from code complexity that used to be expensive to replicate.

Re: Something is changing in the unit economics of software

#20

Post by euazOn »

roncesvalles wrote: post #18

>Users began expecting something fundamentally different from software: not just tools that store and retrieve, but products that reason, generate, and respond. Not really. >Every inference call costs money. Not really, either. If you buy your own GPU, rack it, and run an open model, there is no unit cost. This is just expensive hosting infra. You also pay unit costs for SaaS that your software uses (things like SMS…

> If you buy your own GPU, rack it, and run an open model, there is no unit cost.

No. There is economic opportunity cost (borrowing), energy cost, infra cost, depreciation / risk of failure with each unit of work, bandwidth, maintenance, and lots more. Small, but not zero, and often overlooked - especially the opportunity cost.

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