The AI Daily Brief: Artificial Intelligence News and Analysis
Are Agent Swarms the Next AI Paradigm?
28 Jan 2026
Chapter 1: What is the significance of agent swarms in AI for 2026?
Today on the AI Daily Brief, is 2026 going to be the year of AI agent swarms? Before that, in the headlines, some big jumps in anthropics fundraising and revenue. The AI Daily Brief is a daily podcast and video about the most important news and discussions in AI. All right, friends, quick announcements before we dive in.
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Now, with that out of the way, let's dive in.
Chapter 2: How is Anthropic's recent funding impacting the AI landscape?
Welcome back to the AI Daily Brief Headlines Edition, all the daily AI news you need in around five minutes. We kick off today with some fundraising and business news out of Anthropic. The company is close to finalizing their latest funding round, which could raise more than $20 billion.
Reports state that Anthropic has between $10 and $15 billion in firm commitments that could be finalized early next week, including the Singapore Sovereign Wealth Fund and Sequoia making large investments. Anthropic has also recently doubled the size of the round from $10 to $20 billion in response to excessive interest.
One investor told the Financial Times that the round was five to six times oversubscribed before the size increase. In addition to venture capital and sovereign wealth, Microsoft and Nvidia have also committed to invest a total of $15 billion in the company, which is on top of the $20 billion from investment firms.
The round would reportedly value Anthropic at $350 billion, almost a doubling from their Series F, which closed in September. The fundraising frenzy firmly cements Anthropic's momentum. Last year, remember, OpenAI raised $40 billion, anchored by $30 billion from SoftBank, meaning that Anthropic is now neck and neck with those figures.
Chapter 3: What updates are there on Nvidia's chip sales in China?
In addition to fundraising news, the information has an update on Anthropic's revenue growth forecasts. They report that Anthropic updated investors in December and hiked forecasts across the board. 2026 revenue is now expected to come in at $18 billion, around a Forex increase from last year's numbers and up 20% from estimates made last summer.
In 2027, Anthropic expects to generate $55 billion in revenue. For 2029, their most optimistic forecast calls for $148 billion. That forecast is particularly notable as it's $3 billion more than OpenAI's last forecast, which was made during the summer.
OpenAI, of course, may have hiked expectations since then, but still very notable that Anthropic believes they could overtake OpenAI within three years. The other big number from the financial update was Anthropic's increasing training costs. They expect to spend $12 billion on training this year, which is a 50% increase from summer projections.
Their forecasts also project training costs to exceed $100 billion by 2029.
Chapter 4: How is the UK government planning to upskill its workforce in AI?
These increased costs push back Anthropic's timeline for profitability by a year, with the company now expecting to flip cash flow positive by 2028. Now, one of the things that Dario and Anthropic have of course been weighing in a lot about is chip exports to China, with Anthropic being firmly in the camp that we should not be exporting chips to China.
An update on that front as Beijing has approved the first batch of Nvidia chip imports. Reuters reports that Chinese officials have improved the import of several hundred thousand H200s, allowing access to the advanced chips for the first time. Sources said the first batch of approvals were primarily allocated to three unnamed tech giants.
The Wall Street Journal later named Alibaba and ByteDance as two of the three receiving approval. Other enterprises are still in the queue awaiting a subsequent round of approvals, presumably including high-flying startups like DeepSeek who may have to wait in line to set up their H200s.
Reports stated that Chinese AI firms will be required to support local chipmakers as well, using their chips for some training tasks and most AI inference. Basically, it seems like officials are trying to strike a balance, allowing Chinese companies to train advanced models while also protecting domestic chipmakers. Now, this could be a huge boom to Nvidia's first quarter financials.
Chapter 5: What are the features of Moonshot's Kimi K2.5 model?
Several hundred thousand H200s is in the ballpark of $10 billion in sales. And that's only the first round of approvals. In Q2 of last year, when Chinese chip exports were shut down by the U.S. government, Nvidia reported a $5.5 billion write-down associated with losing Chinese sales. That implies Nvidia could see record Chinese sales this quarter simply based on this first round of approvals.
NVIDIA CEO Jensen Huang is currently visiting China to meet with local employees, but reports suggest that he hasn't met with any senior officials. That said, his next stop is Taiwan, where people familiar with the trip said he plans to ask suppliers to bump up H200 production to meet Chinese demand.
Moving over to the training side of the house, the UK government has expanded their AI training initiative with an ambitious new goal to upskill every worker in the country. The Department for Science, Innovation and Technology announced on Tuesday that free AI training will be made available to every adult worker.
The training will come in the form of 20-minute online courses with modules covering use cases like drafting text, content creation and automation of administrative tasks. Technology Secretary Liz Kendall said... We want AI to work for Britain, and that means ensuring Britons can work with AI. Change is inevitable, but the consequences of change are not.
We will protect people from the risks of AI while ensuring everyone can share in its benefits. New partners, including Cisco, Cognizant, and the National Health Service will join existing partners, including Amazon, Google, Microsoft, and Salesforce in the upskilling initiative.
The department claimed this would be the largest targeted training program since the establishment of Open University in the late 1960s, which delivers distance learning for higher education. They said the program had already delivered a million courses and the government would aim to retrain 10 million workers by the end of the decade.
Workers that complete the training will be certified with an AI Foundations badge to give employers confidence they have basic AI skills. Now, there is a lot that we could say about this. The cynic in me, of course, sees all of the potential challenges with this program, most of which sort of amount to a question of whether this is too little to move the needle.
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Chapter 6: How does Kimi K2.5 compare to other AI models in performance?
But we got to start somewhere. Governments need to get involved in a way that is actually helpful to people adapting to a new world rather than just trying to pretend that they have control over whether that new world exists. And so for that reasons, I think this is a good thing, and I'm excited to see it hopefully go even farther than they're thinking right now.
Now, our main episode today is about a new model out of China and its agent swarm capabilities. But Alibaba's Quen team also released a new model earlier this week, specifically called Quen 3 Max Thinking. Now, as you can probably tell from the naming convention, this is the big flagship model from the Quen team. They're equivalent of GPT-5.2 Pro, Gemini 3 Pro, or Opus 4.5.
The model makes use of an inference technique that the Quen team are calling heavy mode. Quen is doing things slightly differently from existing approaches to test time scaling, generating a response, then feeding it back into the model for improvements in a recursive loop. It appears to be generating some pretty significant gains.
Quen said that this method improved benchmark scores on GPQA, which is a PhD-level science test, from 90.3% to 92.8%. On live codebench, scores jumped from 88% to 91.4%. Overall, the benchmarking looks pretty strong. Now, the cost is a little beefy for a Chinese open-source model.
Quen 3 Max Thinking comes in at around the same cost as Cloud Haiku 4.5, meaning that it's still much cheaper than models like Gemini 3 Pro or GPT 5.2, but about 10 times more expensive than DeepSeq v3.2. Now, Quen 3 is already being used by many American companies. Airbnb CEO Brian Chesky, for example, recently said that his company was relying on Quen 3 as a more affordable alternative to U.S.
models, meaning that you got to think that they will be watching this model release closely. Although, again, how it stacks up compared to Kimi K2.5, which we will talk about in our main episode, remains to be seen. Lastly today, it's not just the Chinese labs with some interesting new product to show off.
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Chapter 7: What innovative capabilities does Kimi K2.5 offer for coding tasks?
Google has released a new feature for Gemini 3 Flash called Agentic Vision. The feature leverages Gemini's state-of-the-art multimodal reasoning with code to execute unique capabilities. Writes Google, Agentic Vision introduces an agentic think-act-observe loop into image understanding tasks. the model analyzes the user query in the initial image, formulating a multi-step plan.
the model generates and executes Python code to actively manipulate images, such as cropping, rotating, or annotating, or analyzing them, such as running calculations, counting bounding boxes, etc. Last is Observe. The transformed image is appended to the model's context window. This allows the model to inspect the new data with better context before generating a final response.
Overall, this promises to improve Gemini's ability to annotate images, perform data visualization tasks, help with basic image analysis. Google said that the loop improves model performance by between 5 and 10% across most vision benchmarks. Still developer experience lead Omar San Saviero hinted at the most exciting unlock from the new feature.
He showed an output of an annotated image of a table containing a spill. Gemini had identified a spill, a piece of cloth, and several other items. The annotations appear to be instructions for a robot to clean up the spill by first clearing away the items in the way, dampening the cloth, and wiping up the spill.
The implications, of course, being that this feature could be used to give robots on-the-fly analysis and reasoning ability, allowing them to tackle tasks that they've never seen before. Ultimately though, as I said, when it comes to new models, the big conversation is around Kimi K2.5. And so with that, we will wrap up the headlines and move on to the main episode. Hello, friends.
If you've been enjoying what we've been discussing on the show, you'll want to check out another podcast that I've had the privilege to host, which is called You Can With AI from KPMG. Season one was designed to be a set of real stories from real leaders making AI work in their organizations.
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Chapter 8: What does the future hold for AI agent swarms and their applications?
And now season two is coming and we're back with even bigger conversations. This show is entirely focused on what it's like to actually drive AI change inside your enterprise and as case studies, expert panels, and a lot more practical goodness that I hope will be extremely valuable for you as the listener. Search You Can With AI on Apple, Spotify, or YouTube and subscribe today.
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Welcome back to the AI Daily Brief. Today we're talking about something that has been of interest to people for quite some time. When I first started this show, all the way back in April of 2023, already there were people who were extremely interested in the way that LLMs could generate code.
Now, it would take a couple of years and some significant advances in the models to actually unleash vibe coding in the way that it happened over the course of 2025. But the idea was there very early. We've similarly had interest in vast teams of agents that can coordinate amongst themselves to accomplish more things, even if the capability set hasn't fully been there.
Which isn't to say that people haven't been experimenting. Lindy released their agent swarm tool back in April of 2025. And the concept is related to something that I've talked about on this show, the Doctor Strange theory of AI agent work. Now, the specific point that I've made is actually about the difference in how enterprises think agents will play out
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