The Coming AI Economic Revolution

The low-hanging fruit for online marketplaces may have been picked, but top Founders understand that the largest opportunities are often the hardest to grasp. The new USC School of Advanced Computing will position the university to be the leading source of tech talent on the West Coast. Within the next decade, more than 28,000 USC students will graduate with computing-related proficiency across different disciplines and degrees, preparing them for tech-forward professions of the future, university projections show. Ultimately, the university’s endeavor to launch a renaissance in computing could propel greater investment, growth and innovation across the Southern California region, the state and the world. The initiative is expected to increase the university’s economic value to the region (already estimated at $8 billion in a 2017 analysis) and the globe. The initiative will result in thousands more students earning computer science degrees and gaining digital expertise to bring their technological talents to jobs across multiple professions worldwide.

Today, large language models (LLMs) are the primary arena of AI innovation and competition, and countries are racing to develop more advanced and capable LLMs. In general, LLM performance improves with scale – more parameters, more and better training data, more training runs and more computation. While GPT2 boasted some 1.5 billion parameters, subsequent models have grown by orders of magnitude, with GPT4 reportedly incorporating 1 trillion such variables. The massive open data ecosystem of the internet has played a fundamental role in the development of these models.

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For the foreseeable future, we expect geopolitics to increasingly driving economic decision-making in Washington and Beijing, and in capitals around the world. The US, China, and other countries are using new and old tools to advance political objectives through economic means. The table below outlines a few of the most prominent techno-economic tools shaping technology competition.

• Each of these approaches contributes uniquely to the AGI landscape, blending theory, experimentation, and technological innovation.
• It not only reshapes the modern workplace but also sets the foundation for the sophisticated cognitive capabilities that AGI promises.
• States and regions also face challenges in boosting local data and computational resources.
• Then there is the question of drift; data and models can drift independently or interdependently.

But ensuring that it does so in the right way will require new forms of international economic governance. But many emerging economies will also benefit from this technology, and for them, access may be slow and uneven. The extent to which AI can be developed and used in an equitable way worldwide will determine the magnitude of its effect on the global economy. Another concern is that AI could achieve wholesale automation of many sectors, triggering large-scale job losses. These concerns are real, but they overlook the barriers to full automation in many workplaces, as well as the compensatory job gains—some from growing demand for existing occupations, others from the rise of new occupations, as a result of AI, including generative AI. For example, research suggests that over the next couple of decades, some occupations—roughly 10 percent of all occupations according to some estimates—whose constituent tasks can almost all be automated, will likely decline.

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If worker transitions and other risks can be managed, generative AI could contribute substantively to economic growth and support a more sustainable, inclusive world. Much recent debate has focused on the dangers that AI poses and the need for international regulations to prevent catastrophic harm. As important, however, will be the introduction of positive policies that foster AI’s most productive uses. To unleash the full force of an AI-powered economy, then, will require not only a new policy framework but also a new mindset toward artificial intelligence.

While metaverse has admittedly had a flat year, after being one of the biggest buzzwords of 2022, the next year could be where metaverse interest starts renewing in slightly advanced forms. Most brands have seen that a flat, 2D view of metaverse experiences is not entirely immersive in the true sense—it is because of this that the launch of the Apple Vision Pro headset was seen to be pivotal in the industry. As a result, 2024 could be the year when the first generation of truly immersive metaverse experiences are built and experimented with in terms of what value additions could do.

European Commission considering 3- to 5-year ban of facial-recognition tech

These insights explore how early adopters are taking advantage of the opportunity—and the challenges many face with integration. Today, data is no longer merely a record of a past event, but also a kind of energy source for the creation and improvement of intelligent behavior and the nascent capability of AI to reason. While AI training is starting to near the limits of the raw data that the open web can provide, new proprietary sources of data continue scaling. The importance of these countries in multilateral cooperation has been clear for some time. The UK’s AI strategy can set it apart from its neighbors due to its recent separation from the EU. In 2022, 1.9 times as many AI companies were funded in the US as compared to the EU and the UK combined.

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