Sky rocketing demands for generative artificial intelligence have accelerated demands for AI-centric data centers that can handle more advanced tasks.
The third proof point is both the increase in manufacturing capacity investment and the change in how that investment will be managed. With the interest in governments to secure future semiconductor manufacturing for both supply security and economic growth, Mr. Gelsinger went on a spending spree with investment in expanding capacity in Oregon, Ireland, and Israel, as well as six new fabs in Arizona, Ohio, and Germany. Most of the initial investment was made without the promise of government grants, such as the US Chips Act. However, Intel has now secured more than $50B from US and European government incentives, customer commitments starting with its first five customers on the 18A process node, and its financial partners. Intel has also secured an additional $11B loan from the US government and a 25% investment tax credit.
In addition to it’s own investment in fab capacity, Intel is partnering with Tower Semiconductor and UMC, two foundries with long and successful histories. Tower will be investing in new equipment to be installed in Intel’s New Mexico facility for analog products, and UMC will partner with Intel to leverage three of the older Arizona fabs and process nodes, starting with the 12nm, to support applications like industrial IoT, mobile, communications infrastructure, and networking.
The second side of this investment is how current and future capacity will be used. As strictly an IDM, Intel has historically capitalized on its investments in the physical fab structures by retrofitting the fabs after three process nodes, on average. While this allowed for the reuse of the structures and infrastructure, it eliminated support for older process nodes, which are important for many foundry customers. According to Omdia Research, less than 3% of all semiconductors are produced on the latest process nodes. As a result, Intel is shifting from retrofitting fabs for new process nodes to maintaining fabs to support extended life cycles of older process nodes, as shown in the chart below. This requires additional capacity for newer process nodes.
When you’re trying to run and manage AI projects, there’s a lot that needs to be considered in terms of determining project budgets and cost. All technology projects have three major aspects of cost: software, hardware, and services. But when it comes to AI projects an additional aspect needs to come into play: data.
If you’re trying to generate a return on investment, you need to know what the investment part is. You have to know what the return is, too. But of the two things, you’re guaranteed to spend at least what the investment part is, if not more, but you’re not guaranteed anything about the return. People underestimate the complexity and cost of dealing with data. So, it’s crucial to think about how big your AI project is. Because when it comes to AI projects, size really does matter.
Without exception, we all hate the maddening menus and mind-numbing muzak we suffer through while waiting for an actual human being to answer a customer service line.
Hopefully, salvation is around the corner.
Tenyx, a trailblazer in voice AI systems, has unveiled a comprehensive conversational AI solution that is poised to redefine how enterprises interact with their customers.
In the shadows of the digital age, a quiet revolution unfolds, reshaping the landscape of work with every passing moment. Artificial intelligence (AI), once the fodder of science fiction and speculative thought, now infiltrates every facet of our professional lives, often in ways so subtle that its impact goes unnoticed until it’s too late. This silent shift sees AI not just complementing human efforts but outright replacing them, leaving a trail of obsolescence in its wake. Thus, let’s delve into the stark realities of AI’s encroachment on human jobs, exploring the future landscape of employment and the duality of its impact, through a lens that does not shy away from the grim nuances of this transition.
Across industries, AI’s efficiency, relentless work ethic, and precision have made it an irresistible choice for employers. From manufacturing lines where robotic arms assemble products with inhuman speed and accuracy, to sophisticated algorithms that manage stock portfolios, outperforming their human counterparts, the signs are clear. AI doesn’t just work alongside humans; it often works instead of them. The adoption of AI in tasks ranging from customer service bots handling inquiries with unsettling empathy, to AI-driven analytics predicting market trends with eerie accuracy, showcases a reality where human involvement becomes increasingly redundant.
As AI continues to evolve, the future of human employment navigates a precarious path. On one hand, new realms of jobs and careers will emerge, focusing on managing, enhancing, and leveraging AI technologies. On the other, the specter of widespread job displacement looms large, a testament to the inexorable march of progress that waits for no one.
Alex Reben makes art with (and about) AI. I talked to him about what the new wave of generative models means for the future of human creativity.
Blue Origin’s New Shepard rocket hasn’t flown space tourists for over 18 months, following an explosive incident in 2022.
UBTech teams up with Baidu to enhance Walker S humanoid robot with natural speech and real-time reasoning capabilities.
A team of MIT researchers has addressed significant barriers to the practical application of 2D magnetic materials. This innovation will enable the development of the next generation of energy-efficient computers.
The team achieved a notable breakthrough by developing a “van der Waals atomically layered heterostructure” device. The device connects two 2D materials: tungsten ditelluride and iron gallium telluride, a 2D van der Waals magnet.