This Week in AI: DeepSeek Hits Chip Stocks, Meta Stays Pat on AI Spending, SoftBank Invests in OpenAI

DeepSeek was the talk of Silicon Valley and Wall Street this week after it singlehandedly wiped nearly $600 billion of market value from Nvidia. Its $5.6 million cost to train its foundation models with only about 2,000 of slower H800 Nvidia chips brought concerns of lower future chip demand.

But questions started emerging about its pre-training cost. Bank of America analysts believe other costs were excluded from the total, while OpenAI thinks DeepSeek used a method called distillation to use generated outputs from OpenAI’s own models, a violation of its terms of service.

What is undisputed is that DeepSeek introduced several engineering innovations that Silicon Valley could adopt to lower their own pre-training costs. This bodes well for enterprises, since it could lower artificial intelligence (AI) inferencing costs and make businesses more willing to deploy AI broadly.

Meta CEO Mark Zuckerberg said his company would be looking at the innovations from DeepSeek and perhaps apply them to its own AI training, during its fiscal third-quarter earnings call with analysts this week.

But Zuckerberg stood pat on his plans to spend $60 billion to $65 billion on AI infrastructure. While DeepSeek does offer a lower-cost path to AI training, he said two factors convinced him not to pull back on spending.

One is the trend toward inferencing as carrying the bulk of AI costs, Zuckerberg said. As AI models add reasoning skills, inferencing costs will be more consequential. Inferencing is when a pre-trained foundation model is given new data to understand and analyze, such as when a user inputs a prompt and gets a response.

Second, Zuckerberg said as Meta embeds its AI assistant, Meta AI, more fully into its social media sites and chat apps, they will have more AI workloads to process from its billions of users. This calls for building more data centers, servers and other infrastructure for processing.

Microsoft CEO Satya Nadella also said his company plans to keep spending on data centers, especially as its AI business is booming. In Microsoft’s just-concluded fiscal second quarter, the segment’s annual revenue run rate was $13 billion, up 175% year over year.

Nadella acknowledged that DeepSeek had some innovations worth considering but pointed out that the AI industry has been adding efficiencies and lowering costs for customers already.

Nadella said this will continue because of the AI scaling law and Moore’s Law. The scaling law dictates that the more data and computing power are given to an AI model, the better it performs. Moore’s Law, although slowing down, predicts that the number of transistors on a microchip will double roughly every two years with minimal cost increase.

These two combined will lead to cheaper and more powerful AI, he said.

Anthropic CEO Dario Amodei said talk about DeepSeek posing a threat to U.S. leadership in AI is “greatly overstated,” according to a personal essay.

DeepSeek’s inexpensive training of its AI models follows the typical curve of costs going down as AI models become more efficient. Historically, training costs drop 4x (four-fold) per year due to better technology and efficiency improvements, he said. This means that if a model cost $100 million to train last year, a similar model today would cost around $25 million.

DeepSeek’s models are on par with top U.S. AI models, but this was based on the U.S. models’ performance seven to 10 months ago, not how they are performing now, he said. To account for this lag, he doubled the four-fold cost drop to 8x (eight-fold) for DeepSeek. That means it would not be unusual for anyone to build a much cheaper AI model today.

DeepSeek “does not ‘do for $6 million (to train one model) what cost U.S. AI companies billions’.” Amodei said, adding that it costs “tens of millions” to train Anthropic’s Claude 3.5 Sonnet.

Amodei also clarified one thing Wall Street mixed up: DeepSeek’s foundation model, V3, was the purely pre-trained model with the cost savings; it was released in December. R1, DeepSeek’s reasoning model that tanked Nvidia’s stock, was the second stage of training the V3, adding a technique called reinforcement learning that made it perform on par with OpenAI’s o1 and other top reasoning models. R1 was released on Jan. 20.

DeepSeek V3 did invent “genuine and impressive innovations, mostly focused on engineering efficiency,” he said. However, many researchers and engineers have steadily been improving AI models as well, he added.

“What’s different this time is that the company that was first to demonstrate the expected cost reductions was Chinese,” Amodei said. “This has never happened before and is geopolitically significant. However, U.S. companies will soon follow suit — and they won’t do this by copying DeepSeek, but because they too are achieving the usual trend in cost reduction.”

He said what makes sense to him is to restrict exports of advanced AI chips to China because of its “authoritarian” regime. Even if the exports merely delay China’s access to these chips, “because AI systems can eventually help make even smarter AI systems, a temporary lead could be parlayed into a durable advantage,” Amodei said.

Japanese tech investor SoftBank is reportedly planning to invest between $15 billion and $25 billion in OpenAI, according to Financial Times, which would make it the largest investor in the startup after Microsoft.

The amount would be in addition to the $15 billion SoftBank plans to contribute to Stargate, a $100 billion to $500 billion project to build AI data centers and other infrastructure. OpenAI plans to invest $15 billion. MGX, a UAE sovereign investment fund, is part of the project.

According to the Financial Times report, OpenAI has raised about $20 billion thus far over several funding rounds, including Microsoft’s $14 billion. SoftBank invested $2 billion in OpenAI last year.

SoftBank CEO Masayoshi Son has been courting OpenAI for years. Getting a larger stake in OpenAI is the cornerstone of Son’s stated goal of developing AI superintelligence.

SoftBank’s investment in OpenAI will likely be the Japanese company’s largest, with a failed $16 billion investment in WeWork coming in second.

The post This Week in AI: DeepSeek Hits Chip Stocks, Meta Stays Pat on AI Spending, SoftBank Invests in OpenAI appeared first on PYMNTS.com.