Tech giants are now racing to bring their generative AI video models to market. Google (NASDAQ: GOOGL) recently unveiled Veo, its generative video AI, to a select group of beta testers, showcasing its capabilities through a series of demos. Around the same time, OpenAI announced the upcoming release of Sora, its own generative video product, as part of a promotional campaign dubbed “The 12 Days of OpenAI.”
This was unexpected from OpenAI, as the team recently suspended Sora after a group of disgruntled artists in its early access program complained about the way OpenAI was treating them.
Generative video seems to be one of the last frontiers in AI innovation (for now). While text-to-text and text-to-image models have become mainstream, text-to-video has remained elusive—until now. The dominant players in AI, including OpenAI, Google, and others, have kept their video capabilities close to their chests for years, teasing their existence but not making them widely available. However, the era of text-to-video AI seems to be on the horizon. Although only available to a select group of users at the moment, Google has posted several examples of VEO in action.
With its launch, generative video AI will lower production costs and speed up multimedia creation, which will bleed into marketing, advertising, and creative industries. Businesses and individuals will be able to generate custom, high-quality videos within minutes without the need for expensive equipment or technical skills.
But at the same time, there will be new attack vectors created by way of this latest advancement in AI; having easily accessible, easy-to-use generative video tools will probably lead to the proliferation of deepfakes, manipulated video, image or audio recordings that appear authentic but have been altered using artificial intelligence, typically to make someone appear to say or do something they never did. This is an issue that becomes more and more problematic as models grow more advanced and become more capable.
AI’s first loser? Intel’s CEO steps down
This week, Intel’s (NASDAQ: INTC) CEO Pat Gelsinger announced his resignation after a three-year stint in the role. Leadership transitions in publicly traded companies often stem from shareholder dissatisfaction, and Gelsinger’s departure seems to have been catalyzed by unhappy investors.
Frank Yeary, Intel’s independent board chair, commented, “While we have made significant progress in regaining manufacturing competitiveness and building the capabilities to be a world-class foundry, we know that we have much more work to do at the company and are committed to restoring investor confidence.” This statement highlights and confirms the dissatisfaction among investors, particularly as Intel’s shares have declined 61% since Gelsinger took over, compared to competitor NVIDIA’s (NASDAQ: NVDA) 820% rise during the same period.
While it’s easy to blame leadership, the root of Intel’s struggles might lie deeper than the boardroom. When ChatGPT opened the floodgates of consumer AI adoption in late 2022, the demand for advanced chips surged. Most chipmakers thrived as AI service providers and hardware manufacturers raced to secure the specialized GPUs and advanced chips essential for powering AI products and data centers. However, Intel struggled to capitalize on this wave, largely because it continued to specialize in and offer traditional CPUs rather than the advanced chips dominating today’s market.
Unfortunately for Intel, the market has evolved past the need for basic CPUs, even most computers have moved on to GPUs and other more powerful chips.
Even as the market shifted, Intel’s decision to stick with older technologies was clearly a costly mistake for the company. The board’s call for new leadership reflects a desire for a turnaround, but, in my opinion, at this point, it may be a bit unrealistic to believe that Intel can catch up to NVIDIA and other competitors that have firmly established themselves as leaders in the AI-driven chip market.
U.S.-China AI battle intensifies
The Biden administration has escalated its efforts to thwart China’s AI innovation with new restrictions targeting the country’s access to advanced technologies that would assist in researching and developing artificial intelligence products. These new measures not only extend the existing bans on U.S.-derived AI chips, tools, and intellectual property but also include provisions preventing Japanese and Dutch companies from supplying key technologies to Chinese firms. Additionally, the U.S. government has added 140 Chinese entities to its blacklist, further isolating China’s chip sector from global resources.
China retaliated by banning the export of critical materials like gallium, germanium, and synthetic diamonds, which the United States frequently uses for various military and AI applications. Although China cited national security concerns for these measures, the move is widely seen as a response to U.S. policies to stifle its technological advancement.
The rivalry between the two nations goes beyond trade policies. Both the U.S. and China are vying for dominance in the AI landscape. Each nation wants to be seen as the technologically superior force when it comes to AI research, innovation, and applications. It’s always a close race between these two countries, with the U.S. currently being the global leader in AI while China is a close second. This dynamic is one of the reasons why the United States continuously works to maintain its edge by limiting China’s access to critical resources.
These limitations will likely worsen in the coming weeks as the Donald Trump administration is expected to favor stricter policy measures against China and other foreign nations.
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