Some 2024 LLM takeaways: multimodal vision became common, LLM prices crashed, overall environmental impact worsened despite efficiency gains, and slop arrived
A lot has happened in the world of Large Language Models over the course of 2024. Here's a review of things we figured …
Simon Willison's WeblogSimon Willison
Context & Ripple Effects
This review marks a transition from LLM capability novelty to deployment economics: vision became routine while model pricing fell sharply, even as aggregate environmental impact moved in the opposite direction. Later coverage shifts attention to reasoning features, coding agents, and higher-priced subscriptions, suggesting that cheaper base-model access did not end demand for differentiated capabilities.
Model providers face lower pricing power for broadly available LLM access, while buyers can more readily treat multimodal vision as a standard capability rather than a premium differentiator.
Efficiency improvements have not reduced the sector's overall environmental burden; the growth in LLM development and use outweighs those gains in this account.
Second-order effects
Falling model prices raise pressure on providers to differentiate through task performance, tooling, and product packaging rather than raw access to multimodal models.
As lower-cost models enable more output, including “slop,” buyers must place more value on evaluation and workflow controls instead of assuming higher volume creates higher-quality results.
Third-order effects
If capability commoditization and usage growth continue together, LLM competition will increasingly hinge on the economics of serving useful tasks—not simply the cost of a model call.
The mismatch between per-model efficiency and rising aggregate impact points to environmental performance becoming a system-level constraint as adoption expands, though this review alone does not establish how providers or policymakers will respond.
The trend: LLMs are moving toward commoditized baseline capabilities and lower unit prices, while value, operational control, and environmental costs shift to the scale of deployment.
Really enjoyed “Things we learned about LLMs in 2024” by — @simonwillison.net, especially this analogy between today's datacenter buildout and the 19th century railway boom. The parallels are striking. simonwillison.net/2024/Dec/31/ ... [image]
The good, the bad and the ugly: “Things we learned about LLMs in 2024” A nice round-up of development in the field and lots of food for thought about the future. simonwillison.net/2024/Dec/31/ ...
Kudos @simonwillison.net, this is such a good article: simonwillison.net/2024/Dec/31/ ... . Best points to me: — DeepSeek v3 $6m training suggests HUGE infra spends maybe unnecessary but tech execs prolly continue to invest — Synthetic training data works — LLMs look easy to …
I think everyone who has an opnion, good or bad, about LLMs, should read how @simonwillison.net has summer up what's happened in the space this year. He's the most credible, most independent, most honest, and most technically fluent person watching the space. simonwillison.net/2…
Great summary right here on the state of AI in 2024. I particularly like the part on “LLMs need better criticism”. As Simon says, arguing there are no good use cases and that it's all unreliable “plagiarism” slop is a pretty good tell on how well you understand the tech. [embe…
simonwillison.net/2024/Dec/31/ ... is a great roundup of what's happened in the world of LLMs this year. — I say this as someone who's barely knowledgeable enough about this stuff to grasp the intricacies, making the accessibility of the post all the more impressive! [image]
Here's my end-of-year review of things we learned out about LLMs in 2024 - we learned a LOT of things https://simonwillison.net/2024/Dec/31/ llms-in-2024/ — Table of contents: [image]
I had fun with this comparison of today's datacenter arms race to the 1800s rollout of the railways, with its sequence of bubbles and resulting financial crashes https://simonwillison.net/... [image]
One of the most notable trends from 2024 was the total collapse in terms of LLM pricing - the API models are absurdly inexpensive now Generating captions for 68,000 photos using Gemini 1.5 Flash 8B costs $1.68! https://simonwillison.net/... [image]
LLM assistants are going to be a good forcing function to make sure all app features are accessible from a textual interface as well as a gui. Yes, a strong enough AI can drive a gui, but it makes so much more sense to just make the gui a wrapper around a command line interface …