Mistral AI will acquire cloud provider Koyeb in a move to accelerate its Mistral Compute service. In a blog post, Koyeb said:

“The Koyeb platform will continue operating. In the coming months, we’re expecting to transition to become a core component of Mistral Compute and will double down on our Inference, Sandboxes and serverless capabilities for MCP Servers.

As we join Mistral AI and prepare for this transition, we're going to focus on customers on the Pro plan and above.”

Nicolas Bustamante, CEO of Fintool, penned an article on X on the AI-driven (actually more like Anthropic) software sell off and included a grid of what categories of SaaS will take the biggest hit. The article is worth a read to gauge the nuance of what SaaS vendors will be disrupted. Bustamonte riffs on the 10 moats of vertical software and what LLMs will do to each.

More:

• How three hotel giants are playing agentic commerce, AI
• How CEOs are answering the dreaded LLM disruption question
• SaaS under the microscope: How to evaluate your vendor
• SaaS death knell storyline illogical, but margin compression is here
• Observability stands out in enterprise software wreckage

Google Cloud and Unilever announced a five-year partnership that will revolve around bringing AI to consumer product goods. Unilever will use Google Cloud's enterprise AI stack for brand discovery, measurement and marketing. Unilever will build an AI-first digital backbone and develop agentic workflows using Google Cloud.

Nvidia is starting to talk about inference efficiency and total cost of ownership citing reports on low-latency agentic coding. It's highly likely you'll hear a lot more about inference total cost of ownership talk from Nvidia leading up to GTC in March.

Manus launched Manus Agents, a way to use Manus agents inside of messaging apps. Telegram is the first supported channel and it's safe to say WhatsApp will be a fast follow. After all, Meta now owns Manus.

Alibaba launched Qwen 3.5, which adds native multimodel agents. Perhaps the biggest takeaway is that Qwen 3.5 is focused heavily on inference efficiency and hybrid architecture. The blog post outlines benchmarks, a demo and use cases.

Infleqtion will start trading under the ticker "INFQ" after the company completed its SPAC deal with Churchill Capital Corp. X.

The deal will add a new pure play quantum computing company to the stock market. Infleqtion specializes in neutral atom quantum systems. Infleqtion's product portfolio includes quantum computers, quantum clocks, RF receivers and inertial sensors.

The stock is worth watching, but SPACs historically have made a big debut, tanked and then recovered. Here's a look at IonQ's stock trading history following its SPAC debut. IonQ went public via a SPAC deal in 2021 and Rigetti and D-Wave followed with similar SPAC deals in 2022.

Here's a look at how those quantum computing SPAC deals have fared over the years. Bottom line: Infleqtion may turn out to be a great quantum company, but there's no rush. You're likely to have multiple entry points.

Peter Steinberger turned AI agents into a viral phenomenon with OpenClaw and is now headed to OpenAI. OpenClaw will remain open source. In a blog post, Steinberger said:

"When I started exploring AI, my goal was to have fun and inspire people. And here we are, the lobster is taking over the world. My next mission is to build an agent that even my mum can use. That’ll need a much broader change, a lot more thought on how to do it safely, and access to the very latest models and research.

Yes, I could totally see how OpenClaw could become a huge company. And no, it’s not really exciting for me. I’m a builder at heart. I did the whole creating-a-company game already, poured 13 years of my life into it and learned a lot. What I want is to change the world, not build a large company and teaming up with OpenAI is the fastest way to bring this to everyone."

OpenAI CEO Sam Altman said multiple agents interacting with each other will become core to the company's platform.

Opaque, a startup focused on confidential AI and computing, said it raised $24 million in a Series B funding round. The funding round was led by Walden Catalyst. Opaque has raised $55.5 million and now has a valuation of $300 million.

Lenovo's third quarter earnings report offered a glimpse into how high memory prices are going to impact consumer electronics. PCs and smartphones will take a hit on price increases, but Lenovo expects to make up the sales on higher-end servers and other gear.

Yang Yuanqing, CEO of Lenovo, said:

"The supply shortage and rising cost situation is precedented and still not finished. The DRAM cost increased by 50% last quarter, but the current quarter versus last quarter almost doubled again even with the contract price. So this structural imbalance between supply and demand is not simply a short-term fluctuation. It's likely to have a prolonged impact on the industry throughout this year.

Looking ahead, high material cost will probably constrain the demand for PC smartphone. But that's just from a unit volume point of view. But given the higher pricing and the market shifting to the premier segment because of AI PC, AI phone, we believe overall PC revenue will still grow year over year."

Anthropic said it will cover the electricity price increases to consumers from its data centers. In a post, Anthropic said "AI companies shouldn't leave American ratepayers to pick up the tab."

It's a nice thought, but paying for grid upgrades, bringing net new power generation, reducing strain on the grid and investing in local communities seems like a roundabout way to lower my bill that’s already gone up.

Zoom out: Anthropic's missive follows a similar post from Microsoft. These efforts from AI giants are designed to head off NIMBY as communities realize that having a massive AI data center in your back yard may not be so swell. NIMBY was a key acronym in my 2026 predictions.

Charlie Guo read the system cards for OpenAI's GPT-5.3-Codex and Anthropic's Claude Opus 4.6 so you didn't have to. Thanks for your service Charlie! The post is worth a read, but what was most notable was this:

Both models also exhibited what researchers call “evaluation awareness” - they can often “tell” when they’re in a test environment, and they behave differently as a result...

Which, of course, creates a problem. If AI systems behave better under observation and worse when they think they’re not being watched, how do we accurately measure their true capabilities and alignment?

Good question.