Meta's AI Challenges: DeepSeek's Innovations and Implications for Tech Giants

Meta AI Models 'Clearly Behind' DeepSeek, Says Analysts After Mark Zuckerberg Admitted DeepSeek Did Some Novel Things 'We're Still Digesting' - Meta Platforms (NASDAQ:META), Advanced Micro Devices (NASDAQ:AMD)

The research firm has paints a bleak picture for Meta Platforms' Llama model. Piper Sandler's research dated Feb. 4 has revealed a less optimistic outlook for Meta Platforms Inc.'s META position in the artificial intelligence race. This comes as its CEO Mark Zuckerberg during Meta's fourth-quarter earnings call stated that they are "still digesting" some of the "novel things" Chinese AI startup DeepSeek has done. Analyst Takeaways: The note, based on insights from a data center engineering expert, suggests that while DeepSeek is ahead of Meta's latest Llama model, it still lags behind leading AI labs like OpenAI and Anthropic by about six months. "In terms of the tricks and methodology applied by DeepSeek, the leading labs such as OpenAI and Anthropic are fully aware of such methodologies and also employ these but such is not the case of Meta which is clearly behind," the note added. The expert also downplayed the significance of DeepSeek's reported $5.6 million training run for its R1 model, noting that a comparable run at OpenAI would likely cost around $10 million, making DeepSeek's "cost not so impressive relative to the U.S." The note also suggested that OpenAI's runs are likely more "comprehensive" and "secure". The Piper Sandler note highlights several key trends in the AI landscape: Increasing Compute Requirements: The demand for compute power is escalating rapidly, especially for inference applications, which are crucial for monetization. This trend is expected to drive significant capital expenditure increases. Winners In The Capex Race: Nvidia Corp. NVDA and Taiwan Semiconductor Mfg. Co. TSM are identified as the primary beneficiaries of this capex surge, due to their dominance in graphics processing units and chip manufacturing, respectively. Shift Towards Inference: Leading AI labs are predicted to allocate an increasing proportion of their compute resources to inference, potentially reaching 90% in the near future. DeepSeek's Impact On Capex: DeepSeek's cheaper training methods could lead to wider adoption of AI models, further fueling the demand for inference and driving capex. Talent Drain At Meta: The report suggests that Meta's lag in the AI race may be attributed to "largely HR related" issues, including compensation and equity attractiveness compared to competitors like OpenAI. The Analyst: Piper Sandler analysts Harsh V Kumar and Robert Aguanno, who cover the semiconductor sector, have given "overweight" ratings to three companies: Advanced Micro Devices Inc. AMD with a price target of $180, Micron Technology Inc. MU with a price target of $120, and Nvidia with a price target of $175. See Also: Trump Media & Technology Trading Volume Falls Amid Expensive Valuations And Bearish Charts: Here's What Technical Analysis For DJT Stock Shows Why It Matters: The Piper Sandler report is bullish on Nvidia with respect to AI sector tailwinds, "a 50/50 split between training and inference applications" and custom application-specific integrated circuits (ASICs) for increased efficiency. However, it expresses less optimism about AMD, citing their lag in software development and continuous loss of talent to competitors. While Zuckerberg's comments on DeepSeek suggest a cautious approach to evaluating DeepSeek's advancements, the Piper Sandler analysis paints a more concerning picture for Meta, indicating that the company has significant ground to make up in the rapidly evolving AI landscape. See Also: PepsiCo Revamps Lays, Cheetos, And Doritos Maker Frito Lay As North American Sales Take A Hit, Shifts Focus To Health-Conscious Customers Price Action: Shares of Meta were down 0.36% in premarket, whereas the exchange-traded fund tracking the Nasdaq 100 index, Invesco QQQ Trust, Series 1 QQQ declined 0.80%. Meta is up 17.51% on a year-to-date basis and 53.28% over the last year. The average price target among 43 analysts tracked by Benzinga is $724.86 with a 'buy' rating. The estimates range from $575 to $875 apiece. Recent ratings from RBC UBS, Citigroup, and Oppenheimer suggest a $788.67 target, implying a potential upside of 12.40%. Read Next: Trump Media or Trump Coin: Where Have Investors Lost More Money Since the 47th President Entered the White House? Photo courtesy: Shutterstock AMDAdvanced Micro Devices Inc$108.54-9.17%Overview Rating:Speculative37.5%Technicals Analysis660100Financials Analysis200100WatchlistOverviewMETAMeta Platforms Inc$700.81-0.48%MUMicron Technology Inc$90.35-0.34%NVDANVIDIA Corp$119.410.64%QQQInvesco QQQ Trust, Series 1$520.52-0.75%TSMTaiwan Semiconductor Manufacturing Co Ltd$204.160.05%Market News and Data brought to you by Benzinga APIs

Mark Zuckerberg Just Delivered Incredible News for Nvidia, AMD, and Micron Stock Investors | The Motley Fool

Investors were concerned that DeepSeek's innovative approach would trigger a collapse in demand for graphics processors (GPUs) and other data center components, which are key to developing AI. However, those concerns might be overblown. Meta Platforms (META 0.96%) is a huge buyer of AI chips from Nvidia and AMD. On Jan. 29, CEO Mark Zuckerberg made a series of comments that should be music to the ears of investors who own AI hardware stocks. Successful Chinese hedge fund High-Flyer has been using AI to build trading algorithms for years. It established DeepSeek as a separate entity in 2023 to capitalize on the success of other AI research companies, which were rapidly soaring in value. Last week's stock market panic was triggered by DeepSeek's V3 large language model (LLM), which matches the performance of the latest GPT-4o models from America's premier AI start-up, OpenAI, across several benchmarks. That isn't a concern at face value, except DeepSeek claims to have spent just $5.6 million training V3, whereas OpenAI has burned over $20 billion since 2015 to reach its current stage. To make matters more concerning, DeepSeek doesn't have access to the latest data center GPUs from Nvidia, because the U.S. government banned them from being sold to Chinese firms. That means the start-up had to use older generations like the H100 and the underpowered H800, indicating it's possible to train leading AI models without the best hardware. To offset the lack of computational performance, DeepSeek innovated on the software side by developing more efficient algorithms and data input methods. Plus, it adopted a technique called distillation, which involves using a successful model to train its own smaller models. This rapidly speeds up the training process and requires far less computing capacity. Investors are concerned that if other AI firms adopt DeepSeek's approach, they won't need to buy as many GPUs from Nvidia or AMD. That would also squash demand for Micron's industry-leading data center memory solutions. Nvidia's GPUs are the most popular in the world for developing AI models. The company's fiscal year 2025 just ended on Jan. 31, and according to management's guidance, its revenue likely more than doubled to a record $128.6 billion (the official results will be released on Feb. 26). If recent quarters are anything to go by, around 88% of that revenue will have come from its data center segment thanks to GPU sales. That incredible growth is the reason Nvidia has added $2.5 trillion to its market capitalization over the last two years. If chip demand were to slow down, a lot of that value would likely evaporate. AMD has become a worthy competitor to Nvidia in the data center. The company plans to launch its new MI350 GPU later this year, which is expected to rival Nvidia's latest Blackwell chips that have become the gold standard for processing AI workloads. But AMD is also a leading supplier of AI chips for personal computers, which could become a major growth segment in the future. As LLMs become cheaper and more efficient, it will eventually be possible to run them on smaller chips inside computers and devices, reducing reliance on external data centers. Finally, Micron is often overlooked as an AI chip company, but it plays a critical role in the industry. Its HBM3E (high-bandwidth memory) for the data center is best in class when it comes to capacity and energy efficiency, which is why Nvidia uses it inside its latest Blackwell GPUs. Memory stores information in a ready state, which allows the GPU to receive it instantaneously when needed, and since AI workloads are so data intensive, it's an important piece of the hardware puzzle. Meta Platforms spent a whopping $39.2 billion on chips and data center infrastructure during 2024, and it plans to spend as much as $65 billion this year. Those investments are helping the company further advance its Llama LLMs, which are the most popular open-source models in the world, with 600 million downloads. Llama 4 is due to launch this year, and CEO Mark Zuckerberg thinks it could be the most advanced in the industry, outperforming even the best closed-source models. On Jan. 29, Meta held a conference call with analysts about its fourth quarter of 2024. When Zuckerberg was quizzed about the potential impact of DeepSeek, he said it's probably too early to determine what it means for capital investments into chips and data centers. However, he said even if it results in less capacity requirements for AI training workloads, it doesn't mean companies will need fewer chips. Instead, he thinks capacity could shift away from training and toward inference, which is the process by which AI models process inputs from users and form responses. Many developers are moving away from training models by using endless amounts of data, and focusing on "reasoning" capabilities instead. This is referred to as test-time scaling, and it involves the model taking extra time to "think" before rendering an output, which results in higher-quality responses. Reasoning requires more inference compute, so Zuckerberg thinks companies will still need the best data center infrastructure to maintain an advantage over the competition. Plus, most AI software products haven't achieved mainstream adoption yet, and Zuckerberg acknowledges that serving many users will also require additional data center capacity over time. So, while it's hard to put exact numbers on how DeepSeek's innovations will reshape chip demand, Zuckerberg's comments suggest there isn't a reason for Nvidia, AMD, and Micron stock investors to panic. In fact, there is even a bullish case for those stocks over the long term.