Bridging the operational AI gap

Enterprise-wide integration is being leveraged to extend today’s process automations into tomorrow’s agentic workflows.

TL;DR

Organizations are actively moving AI from pilot projects to production, with many redirecting budgets towards AI initiatives.
Agentic AI's rise increases the need for integrated data, applications, and systems to ensure enterprise AI initiatives succeed.
Gartner predicts over 40% of agentic AI projects may be cancelled by 2027 due to cost, inaccuracy, and governance issues.
A survey of 500 senior IT leaders revealed that 76% of companies have at least one department with a fully operational AI workflow.
AI success is most prominent in well-defined and automated processes (43% of organizations).
Two-thirds of organizations (66%) do not have dedicated AI teams for maintaining AI workflows.
Companies with enterprise-wide integration platforms are five times more likely to use diverse data sources in AI workflows compared to those without.
Organizations using integration platforms report more multi-departmental AI implementation, greater workflow autonomy, and increased confidence in future AI autonomy.

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Demis Hassabis reposted

Google DeepMind

Only 25% of organizations have moved AI into production at scale. We’re working to change that. 🛠️

@Accenture, @BainandCompany, @BCG, @Deloitte, and @McKinsey are combining our research with their expertise to bring AI innovation to more industries responsibly. 🤝

Find out more → https://t.co/iqMASZ7Kdv

Sam Altman

Really excellent work by the inference team to serve this model so efficiently!

To a significant degree, we have to become an AI inference company now.

Sam Altman reposted

OpenAI

Chain of thought monitors are a key layer of defense against AI agent misalignment. To preserve monitorability, we avoid penalizing misaligned reasoning during RL.

We found a limited amount of accidental CoT grading which affected released models, and are sharing our analysis.

https://t.co/0o3PLfafC4

Sam Altman

a general-purpose model solved a major open problem in mathematics.

we'll be saying this a lot over the coming years, but this is a kinda big milestone.

i'm very excited for AI to greatly extend our understanding of the world, but still, i have complicated feelings today.

Sam Altman

what problem do you most hope AI will solve in the future?

maybe we can help!

clem 🤗 reposted

Patrick

Harvey just published a study showing a hybrid setup, open source GLM 5.1 as primary worker, routing to Opus 4.7 only when needed beats pure Opus 4.7 on quality and costs less.

This is the multi-model routing thesis proved in production on one of the hardest benchmarks in enterprise AI.

The insight isn’t that open source beat frontier. It’s that smart routing beat brute force. Using the most expensive model for every task is not a quality strategy. It’s a laziness tax. The teams building routing layers that send each task to the right model at the right cost are now demonstrably ahead on both dimensions simultaneously.

Inference optimization just became a first-class competitive advantage. Legal proved it first because the stakes forced the discipline.

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

Fei-Fei Li

Scientific research is fundamental to advancing civilization and helping people globally to solve the most critical problems, from medicine to materials, from brain science to physics, and much beyond. This is only possible when scientists have access to the best tools of the time to conduct scientific research, including having access to AI-based tools.

clem 🤗 reposted

David Louapre

Physics researchers want AI sidekicks, not autonomous agents.

A few weeks ago, we published physics-intern, an autonomous system that took Gemini 3.1 Pro from 17.7% to 31.4% on the CritPT physics benchmark, higher than any standalone model.

Impressive…but full autonomy is the wrong mode for real research. A scientist doesn't want an oracle handing down answers; they want a sidekick they can steer and argue with.

So we rebuilt physics-intern as a set of interactive subagents that plug into Codex, Claude Code or Pi.

I tested it on a problem I'd abandoned after a 2023 paper: computing the resonant modes of 3D meshless shapes using the WoS Monte Carlo method.

After some back and forth, physics-intern convinced me my question had no answer as posed, then found the version of it that did!

Arthur Mensch

Replying to @arthurmensch

We're working with companies and governments around the world to make sure their AI systems are up and running outside of external control, improving with each model release, and with an efficient cost structure. Forge allows to continuously train models based on recorded

Aravind Srinivas

frontier ai for all

clem 🤗 reposted

Aaron Levie

Pretty remarkable what’s happening with open weights AI right now. We’re seeing models achieve SOTA results on specific tasks, and getting close to frontier on some areas of coding and other domains.

The more that open weights is able to maintain only a marginal gap from the frontier, instead of a widening gap, the more value that can be created with AI.

Incidentally, this is actually fine for the frontier labs as well; if we can lower the cost of an overall task then AI usage goes up in general. You’re still likely using frontier models for planning, orchestration, reviewing, and other parts of work.

But this is all very good for the applied layer of AI, which is now in a great position to cost optimize workloads with cheaper models or use tailored open models post-trained for specific tasks to improve performance.