What’s driving electricity demand? It isn’t just AI and data centers. - Related to model, redefine, could, electricity, ai,

Breaking down Grok 3: The AI model that could redefine the industry

Less than two years since its launch, xAI has shipped what could arguably be the most advanced AI model to date. Grok 3 matches or beats the most advanced models on all key benchmarks as well as the user-evaluated Chatbot Arena, and its training has not even been completed yet.

We still don’t have a lot of details about Grok 3, as the team has not yet released a paper or technical findings. But from what xAI has shared in a presentation and based on different experiments AI experts have run on the model, we can guess how Grok 3 might affect the AI industry in the coming months.

With competition increasing between AI labs (just look at the release of DeepSeek-R1), we can expect model release cycles to become shorter. In the Grok 3 presentation, xAI founder Elon Musk mentioned that people may “notice improvements almost every day because we’re continuously improving the model.”.

“Competitive pressure from DeepSeek and Grok integrated into a shifting political environment for AI — both domestic and international — will make the established leading labs ship sooner,” writes Nathan Lambert, machine learning scientist at Allen Institute for AI. “Increased competition and decreased regulation make it likely that we, the individuals, will be given far more powerful AI on far faster timelines.”.

On the one hand, this can be a good thing for people as they constantly get access to the latest and greatest models as opposed to waiting for month-long rollouts. On the other, it can have a destabilizing effect for developers who expect consistent behavior from the model. Previous research and empirical evidence from people has shown that various versions of models can react differently to the same prompt.

Enterprises should develop custom evaluations and regularly run them to make sure new updates do not break their applications.

The recent release of DeepSeek-R1 undermined the massive spending that big companies are making to create large compute clusters. But xAI’s sudden rise is a vindication of the massive investments tech companies have been making in AI accelerators. Grok 3 was trained in a record time thanks to xAI’s Collosus supercluster in Memphis.

“We don’t have specifics, but it’s reasonably safe to take a datapoint for scaling still helps for performance (but maybe not on costs),” Lambert writes. “xAI’s approach and messaging has been to get the biggest cluster online as soon as possible. The Occam’s Razor explanation until we have more details is that scaling helped, but it is possible that most of Grok’s performance comes from techniques other than naive scaling.”.

Other analysts have pointed out that xAI’s ability to scale its computer cluster has been the key to the success of Grok 3. However, Musk has alluded that there is more than just scaling at work here. We’ll have to wait for the paper to get the full details.

There is a growing shift toward open sourcing large language models (LLMs). xAI has already open-sourced Grok 1. , the organization’s general policy is to open source every model except the latest version. So, when Grok 3 is fully released, Grok 2 will be open-sourced. (Sam Altman has also been entertaining the idea of open sourcing some of OpenAI’s models.).

xAI will also refrain from showing the full chain-of-thought (CoT) tokens of Grok 3 reasoning to prevent competitors from copying it. It will instead show a detailed overview of the model’s reasoning trace (as OpenAI has done with o3-mini). The full CoT will only be available once xAI open insights Grok 3, which will probably come after the release of Grok 4.

Despite the impressive benchmark results, reactions to Grok 3 have been mixed. Former OpenAI and Tesla AI scientist Andrej Karpathy placed its reasoning capabilities at “around state-of-the-art,” along with o1-Pro, but also pointed out that it lags behind other state-of-the-art models on some tasks such as creating compositional scalable vector graphics or navigating ethical issues.

Other individuals have pointed out flaws in Grok 3’s coding abilities in comparison to other models, although there are also many instances of Grok 3 pulling out impressive coding feats.

Based on my own experience with leading models, I advise you do your own vibe check and research. I never judge a model based on a one-shot prompt. Have a set of tests that reflect the kind of tasks you accomplish in your organization (see a few examples here). Chances are, with the right approach, you can get the most out of these advanced models.

Dixon Technologies India reported a consolidated profit after tax (PAT) of INR 375 Cr in FY24 on an operating revenue of INR 17,691 Cr.

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What’s driving electricity demand? It isn’t just AI and data centers.

Take the US, for example. The IEA analysis points to other research showing that the 10 states hosting the most data center growth saw a 10% increase in electricity demand between 2019 and 2023. Demand in the other 40 states declined by about 3% over the same period.

One caveat here is that nobody knows for sure what’s going to happen with data centers in the future, particularly those needed to run AI. Projections are all over the place, and small changes could drastically alter the amount of energy required for the technology. (See the DeepSeek drama.).

One bit I found interesting here is that China could see data centers emerge as yet another source of growing electricity demand in the future, with demand projected to double between now and 2027 (though, again, it’s all quite uncertain).

What this all means for climate change is complicated.

Growth in electricity demand can be seen as a good thing for our climate. Using a heat pump rather than a natural-gas heating system can help reduce emissions even as it increases electricity use. But as we add demand to the grid, it’s essential to remember that in many places, it’s still largely reliant on fossil fuels.

The good news in all this is that there’s enough expansion in renewable and low-emissions electricity data to cover the growth in demand. The rapid deployment of solar power alone contributes enough energy to cover half the demand growth expected through 2027. Nuclear power is also expected to see new heights soon, with recovery in France, restarts in Japan, and new reactors in China and India adding to a stronger global industry.

However, just adding renewables to meet electricity demand doesn’t automatically pull fossil fuels off the grid; existing coal and natural-gas plants are still chugging along all over the world. To make a dent in emissions, low-carbon data need to grow fast enough not only to meet new demand, but to replace existing dirtier data.

It isn’t inherently bad that the grid is growing. More people having air-conditioning and more factories making solar panels are all firmly in the “positive” column, I’d argue. But keeping up with this breakneck pace of demand growth is going to be a challenge—one that could have major effects on our ability to cut emissions.

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The Download: selling via AI, and Congress testing tech

Imagine you run a meal prep firm that teaches people how to make simple and delicious food. When someone asks ChatGPT for a recommendation for meal prep companies, yours is described as complicated and confusing. Why? Because the AI saw that in one of your ads there were chopped chives on the top of a bowl of food, and it determined that nobody is going to want to spend time chopping up chives.

It may seem odd for companies or brands to be mindful of what an AI “thinks” in this way but it’s already becoming relevant as consumers increasingly use AI to make purchase recommendations.

Congress used to evaluate emerging technologies. Let’s do it again.

The US Office of Technology Assessment, an independent office created by Congress in the early 1970s, produced some 750 reports during its 23-year history, assessing technologies as varied as electronic surveillance, genetic engineering, hazardous-waste disposal, and remote sensing from outer space.

The office functioned like a debunking arm. It sussed out the snake oil. Lifted the lid on the Mechanical Turk. The reports saw through the alluring gleam of overhyped technologies.

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