Inside Pinterest's efforts to replace expensive AI with open-source models

Pinterest uses a multimodal generative AI strategy to lower computing costs. Their approach includes OpenAI's and Alibaba's large language models.

TL;DR

Pinterest uses a "model-agnostic" approach, blending proprietary, closed-source (OpenAI, Anthropic), and open-source (Alibaba) AI models.
This strategy aims to reduce AI computing costs, with Pinterest reporting an estimated 90% cost reduction compared to relying solely on proprietary models.
Investments in cloud infrastructure and hiring AI/ML expertise, including engineers like Matthias Zenger and Mirjam Wattenhofer, support this approach.
New AI-enabled features include auto-collages for advertisers and a voice-enabled AI feature for user queries, both utilizing a mix of AI models.
Pinterest plans to continue experimenting with AI models, prioritizing personalization with its own, cost-effectiveness with open-source, and performance with closed-source models.

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Elon Musk reposted

X Freeze

𝕏 just open sourced the latest recommendation algorithm on GitHub

𝕏 is the only major social platform in the world to publicly open source its core recommendation algorithm

It is by far the most transparent social platform in the world right now

https://t.co/ik1hDHgbj7 https://t.co/Obfm4deBS2

Elon Musk reposted

Dogan Ural

𝕏 just open-sourced its For You algorithm.

This is one of the biggest transparency moves from any social platform.

Here’s the Grok breakdown:

clem 🤗

Very cool to see Cursor doubling down on training great models. In my opinion, ultimately all serious companies in AI will want to train models themselves, based on open-source instead of outsourcing AI to others via APIs!

clem 🤗

Routing and post-training open-source models won't only give you more accurate systems but also meaningfully faster and cheaper systems as most companies are currently learning (in addition to giving you more control and privacy).

The idea that a "frontier" model (by frontier we mean is slightly more accurate on a few very limited benchmarks) will be better for all domains, all tasks, all setups just doesn't hold up! It's marketing for making you pay more!

clem 🤗 reposted

Chamath Palihapitiya

Your margin is my opportunity: AI version…

The biggest surprise of 2026 is that the capability gap between the best open-weight/source models and the best closed models has narrowed much faster than the pricing gap. The pricing gap remains enormous while the capability gap is quite narrow.

What does this means in practice?

For a company consuming 1 billion input tokens and 1 billion output tokens per month:

GPT-5.5 Pro: ~$105,000

Claude Opus 4.8: ~$30,000

DeepSeek V4 Pro: ~$5,220

DeepSeek R1: ~$2,740

I asked ChatGPT what it thought about this and it answered as follows:

“If I were building a company today, the economic frontier would look roughly like:

DeepSeek V4 Pro / R1 for high-volume inference.

Claude Opus for premium agent workflows where reliability matters.

GPT-5.5 Pro only for workloads where its incremental capability demonstrably produces enough business value to justify a 20–40× token premium.”

Most CEOs have no idea that, instead of this nuanced approach, their teams are running amok internally by picking the most expensive models in most cases and burning through massive budgets with zero governance, audit ability and control.

As control planes like our Software Factory become more standard, you can expect the run rate revenue growth of the frontier labs to go down meaningfully and the revenues of the open models to skyrocket.

Why? Because we can implement the nuanced approach above and be agnostic to model - instead focusing on customer intent, model task and cost management among other things.

clem 🤗 reposted

brexton

There was an inflection point recently where the tide shifted to model pickers and OSS

Mix of tokenmaxxing/cost fatigue, nemotron coalition, harness step functions, brains/claws, etc

Long live those who embrace open superintelligence like @NotionHQ @FactoryAI @PrimeIntellect

clem 🤗 reposted

Yash Patil

This is exactly right.

People are starting to look for cheaper model alternatives and realizing two things at once: open-source models are already very good, and the ability to train and serve them efficiently at scale can change the economics pretty meaningfully.

Tokens are still being subsidized, demand is ramping quickly, and the compute crunch is likely to persist. That will push companies toward using the right model for each task instead of defaulting to the most expensive one.

We’re still early, but I expect open-weight adoption to accelerate much faster than most people think.

clem 🤗 reposted

Rohan Paul

UBS says 60% of companies now watching AI budgets are moving to cheaper models and open-source Chinese models

The pressure is coming from extreme bills, including users spending up to $35K/month, teams exceeding quotas by 200%, and companies cutting internal AI tools from 5 to 2.

Companies are not abandoning AI, they are using model routing, which sends easy tasks to cheaper models and saves premium models for hard reasoning, code, and long-context work.

Chinese open-source models such as Qwen, DeepSeek, MiniMax, GLM, and Kimi now fit the enterprise cost curve because they can be run locally or used through cloud catalogs.

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news .futunn.com/en/post/75068082/ubs-group-finds-60-have-already-started-curbing-ai-spending?level=2&data_ticket=1780870170397383

clem 🤗 reposted

leo 🐾

All in on open-source models

clem 🤗

Aligned with @matanSF's intuition that in the next 12 months, 90% of tokens could be going to open models.

We're seeing more and more companies using frontier APIs for experimenting and open-source or private models for production and larger workloads! https://t.co/RGLEw1exVA

clem 🤗 reposted

Sriram Krishnan

It is clear open source models and harnesses are having a moment. There's a few factors at work

1/ It is now obvious that you can catch up to near-SOTA performance and do so with a clear training lineage. See:@thinkymachines Inkling launch today.

2/ There are several well-funded, talented teams building open weight models now in the US and abroad. Along with the explosing of other near SOTA models (Grok/Cursor, Muse Spark), it is clear we are going to have a diverse ecosystem of models atleast on coding and agentic use.

3/ Organizations are increasingly looking for control over how their data is used and are willing to trade off some access to frontier level tokens for this control. Organizations and countries are increasingly nervous about the frontier labs potentially competing with them down the road and don't want their data to enable a future competitor.

4/ Open source is a slider: you could bring your own open harness, your evals, your business context and are free to pick and choose your model of choice.

5/ Companies have now actively shifted from "how do we get our people to use tokens" to being uncomfortable with their token cost ballooning without a clear line to revenue.

6/ Geo-politically, countries will be weighing open weight models as a way to get frontier-level tokens inside controlled environments that may not be otherwise possible.

All of this leads to more choice for all of us !

Yann LeCun reposted

Sriram Krishnan

It is clear open source models and harnesses are having a moment. There's a few factors at work

1/ It is now obvious that you can catch up to near-SOTA performance and do so with a clear training lineage. See:@thinkymachines Inkling launch today.

4/ Open source is a slider: you could bring your own open harness, your evals, your business context and are free to pick and choose your model of choice.

5/ Companies have now actively shifted from "how do we get our people to use tokens" to being uncomfortable with their token cost ballooning without a clear line to revenue.

6/ Geo-politically, countries will be weighing open weight models as a way to get frontier-level tokens inside controlled environments that may not be otherwise possible.

All of this leads to more choice for all of us !