MIT Scares, Meta's Success, & DeepSeek

TLDR;

Today, we take a look at DeepSeek’s latest reveal, studies on AI adoption, productivity, and the viral MIT claim that 95% of AI implementations fail, ending with this week’s trend of the week, Meta’s success with AdLlama and what it tells us about the future of the company and AI.

NEWSREEL

DeepSeek Drops V3.1

Out of the blue, DeepSeek has dropped a slightly larger updated version of their flagship non-reasoning model, DeepSeek v3 (the model that’s also the base of their reasoning model, R1).

DeepSeek cites three things as the most relevant to point out:

It’s a hybrid reasoner, a non-reasoner and reasoner model at the same time, meaning it will “think” for longer on tasks that require more compute; think of this as GPT-4o and o3 combined in the same model, the same principal labs like Google follow.

It’s very fast, thanks to fewer output tokens on average. As you can see in the above graph, the model requires fewer tokens to answer, implying it’s a very efficient model. They offer dirt-cheap prices on their API, although you send data to them at your own risk.

If you're interested in using DeepSeek models, I recommend exploring LLM providers like Fireworks or TogetherAI, as DeepSeek doesn’t have a particularly great history with cybersecurity.

Tailored for agentic use cases. As with any release these days that aims to be actually used, the model excels at tool-calling, utilizing tools to execute actions as any agent requires.

Beyond that, there’s not much to report for now, besides Artificial Analysis’s results that, well, aren’t particularly great. As you can see below, the model is a marginal improvement over R1, and still below all US frontier models and even behind another Chinese model, Qwen 3 235B:

But jumping to conclusions based on a single benchmark can backfire. If you look at coding, the improvements are considerable:

Moreover, others have noted that the Aider polyglot, a tier 1 coding benchmark, achieved preliminary results with a 71% score, surpassing Claude Opus 4 and making DeepSeek Pareto again. In layman’s terms, it puts the model in the performance-to-cost curve, meaning it’s the best model at its size. Crucially, DeepSeek’s entire run for that benchmark had a total cost of a US dollar, while other frontier models’ costs grow into the hundreds of dollars.

→ READ DEEPSEEK ANNOUNCEMENT

TheWhiteBox’s takeaway:

A bizarre release that makes no sense unless they are setting the stage for the big reveal of their next big model. Meanwhile, the entire world waits patiently for the release as a means to assess China’s true stance on the US in the ongoing AI War.

However, some people have speculated that we won’t be getting a DeepSeek R2 model, the natural expectation, but a hybrid DeepSeek V4, which now appears to be much more likely based on this update. In other words, DeepSeek is telling us that they are no longer releasing separate reasoning and non-reasoning models and instead committing to hybrids.

But to me, this…

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