The Japan Times
중도 성향
IT/기술
AI saves time but most companies waste the gain, study shows
The findings belie the premise that companies will automatically boost productivity through AI.
IT/기술 · "FINDING" · 총 25건
필터 보기현재 지수
50.3
0 = 부정 우세
50 = 중립
100 = 긍정 우세
최근 7일 기준 84,153건을 분석한 결과, 뉴스 심리지수는 50.2(균형)입니다. 긍정 4,217건(5.0%)·중립 77,846건(92.5%)·부정 2,090건(2.5%)이며, 중립 비중이 뚜렷하게 높습니다. 성향 지수는 종합 14.8(중도 균형)입니다.
The findings belie the premise that companies will automatically boost productivity through AI.
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Artificial intelligence is getting expensive — and companies are starting to rethink their embrace of the disruptive technology. Playing by a well-worn Silicon Valley playbook, AI companies charged rock-bottom prices to hook customers after ChatGPT burst onto the scene. Kevin Simback of startup incubator Delphi Labs calls it the era of “subsidised intelligence” — meaning investors were basically footing the bill so companies could offer AI on the cheap. “But the tides are beginning to turn,” Simback warned and an era where the big AI companies actually need to make money has begun — with leaders OpenAI and Anthropic looking to go public and attract main street investors later this year. Prices are rising across the board, and one big reason is AI agents. Unlike a chatbot that just answers questions, agents actually do things — book appointments, write code, manage files. And they’re expensive to run, because one task can spin up dozens of agents all working at once, each racking up charges. Those charges are measured in tokens — the basic unit AI companies use to bill customers. A single agent-powered task can burn through dozens of times’ more tokens than a simple chat message. Meanwhile, the computer chips and data centres needed to power all this AI can’t keep up with demand, creating computing shortages and adding further uncertainty to the nascent industry. “Especially in developer circles, the cost to use AI for things like coding has grown exponentially,” said Mark Barton of tech consultancy Omniux. “All the costs are really starting to skyrocket.” Some companies have been so eager to use AI that they’ve gone overboard in a usage binge called “tokenmaxxing”. “In some cases, people are seeing the cost of tokens exceed the cost of the employee within a month or two of use, just because they’re using it too much,” says analyst Jack Gold of J.Gold Associates. Smarter spending Even Meta — which earlier this year encouraged employees to use as many tokens as possible as a measure of productivity — has had second thoughts. “Nobody should be using AI tools just for the sake of using them,” chief technology officer Andrew Bosworth wrote in a memo to staff, reported by the Wall Street Journal. Uber’s chief operating officer this week went a step further, raising eyebrows by saying all this AI spending was showing no noticeable increase in productivity. To cut costs, some companies are switching to free, open-source AI models that anyone can download — not as powerful as ChatGPT or Anthropic’s Claude, but good enough for many tasks. Others are moving to smaller, more specialised models built for specific industries like real estate or finance, rather than giant general-purpose ones. And some are simply breaking big AI tasks into smaller steps, handing each piece to the cheapest model that can handle it. The price difference can be dramatic. “The big large monolithic model, it’s $15 per million tokens, but you can get that down to like five cents if you use the smaller mini model,” says Adrian Balfour of consultancy Enverso. All of this points to AI becoming more like a commodity — where the specific model matters less than finding the right one at the right price. But don’t count out the big players and their state-of-the-art models just yet. “The most advanced users” will always be willing to pay for the best, says John Belton, a portfolio manager at Gabelli Funds. “It’s a growing pie.”
Artificial intelligence is getting expensive — and companies are starting to rethink their embrace of the disruptive technology. Playing by a well-worn Silicon Valley playbook, AI companies charged rock-bottom prices to hook customers after ChatGPT burst onto the scene. Kevin Simback of startup incubator Delphi Labs calls it the era of “subsidised intelligence” — meaning investors were basically footing the bill so companies could offer AI on the cheap. “But the tides are beginning to turn,” Simback warned and an era where the big AI companies actually need to make money has begun — with leaders OpenAI and Anthropic looking to go public and attract main street investors later this year. Prices are rising across the board, and one big reason is AI agents. Unlike a chatbot that just answers questions, agents actually do things — book appointments, write code, manage files. And they’re expensive to run, because one task can spin up dozens of agents all working at once, each racking up charges. Those charges are measured in tokens — the basic unit AI companies use to bill customers. A single agent-powered task can burn through dozens of times’ more tokens than a simple chat message. Meanwhile, the computer chips and data centres needed to power all this AI can’t keep up with demand, creating computing shortages and adding further uncertainty to the nascent industry. “Especially in developer circles, the cost to use AI for things like coding has grown exponentially,” said Mark Barton of tech consultancy Omniux. “All the costs are really starting to skyrocket.” Some companies have been so eager to use AI that they’ve gone overboard in a usage binge called “tokenmaxxing”. “In some cases, people are seeing the cost of tokens exceed the cost of the employee within a month or two of use, just because they’re using it too much,” says analyst Jack Gold of J.Gold Associates. Smarter spending Even Meta — which earlier this year encouraged employees to use as many tokens as possible as a measure of productivity — has had second thoughts. “Nobody should be using AI tools just for the sake of using them,” chief technology officer Andrew Bosworth wrote in a memo to staff, reported by the Wall Street Journal. Uber’s chief operating officer this week went a step further, raising eyebrows by saying all this AI spending was showing no noticeable increase in productivity. To cut costs, some companies are switching to free, open-source AI models that anyone can download — not as powerful as ChatGPT or Anthropic’s Claude, but good enough for many tasks. Others are moving to smaller, more specialised models built for specific industries like real estate or finance, rather than giant general-purpose ones. And some are simply breaking big AI tasks into smaller steps, handing each piece to the cheapest model that can handle it. The price difference can be dramatic. “The big large monolithic model, it’s $15 per million tokens, but you can get that down to like five cents if you use the smaller mini model,” says Adrian Balfour of consultancy Enverso. All of this points to AI becoming more like a commodity — where the specific model matters less than finding the right one at the right price. But don’t count out the big players and their state-of-the-art models just yet. “The most advanced users” will always be willing to pay for the best, says John Belton, a portfolio manager at Gabelli Funds. “It’s a growing pie.”
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I have been an application-specific IC (ASIC) designer for almost three decades. Over that time, I’ve moved through the full academic trajectory, from graduate student to full professor; later, I transitioned to industry after an unsuccessful stint at entrepreneurship. When I made the switch to the private sector in 2019, I began focusing on a critically important aspect of the electronic industry: silicon intellectual property. As much as 80 percent of the physical area in today’s most advanced chips is occupied by blocks that aren’t made for specific products or even designed by the consumer-facing companies that built them. Instead, chipmakers draw heavily on established silicon IP from companies like Arm, Cadence, Rambus, Synopsys, and the company I work for, Silicon Creations. Throughout my career, I’ve designed chips for very different purposes, including enabling the research program in my academic lab and expanding the IP portfolio of my company. When I joined Silicon Creations, I had no idea how differently the industry approaches IC design and encountered a steep learning curve. Initially, it seemed that much of my two decades of academic research and training did not directly translate to the role. I had to learn new skills and adopt a new mindset. Today, demand for ASICs is rapidly growing, driven by the need for specialized chips in the automotive sector, AI applications, and more. By one market estimate, the ASIC market is expected to grow from US $23.4 billion to $38.8 billion by 2033, and the semiconductor industry as a whole is projected to hit $1 trillion by 2030. The industry needs more chip designers—but if you’re coming from an academic background as I did, there are a few things you’ll need to know. Different goals lead to different strategies The differences between industry and academe begin with a divergence in purpose. In academia, my primary objective was to generate new knowledge: to propose a novel circuit technique, validate an unconventional architecture, or explore the limits of performance in a given domain. A successful chip is one that demonstrates a concept. In industry, it is not nearly enough to prove that something can work. The goal is to ensure that it works reliably, repeatedly, and at scale. Success is measured not by novelty but by whether the silicon meets specifications, yields as expected in production, and supports a competitive product delivered on schedule. This leads to a stark contrast in risk tolerance. Academic designs often deliberately push into unproven territory, where even partial success can yield valuable insight. In industry, however, we systematically minimize risk. The cost of failure makes first-time silicon success a central requirement—especially at advanced technology nodes, where the lithography masks used to transfer circuit designs onto silicon wafers alone can cost tens of millions of dollars. As a result, industry design flows are built around eliminating uncertainty through conservative margins, extensive validation, and careful reuse of proven solutions. “Academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale.” This paradigm has existed since the 1970s, when application-specific chip design was established. However, the gulf between academia and industry has expanded since the mid-2010s, when FinFET technology, a 3D architecture using vertical “fins” of silicon, was widely adopted in industry. System designs are also becoming increasingly modular with the advent of chiplets. This fundamentally altered the economics and complexity of ASIC development, with design costs rising by almost an order of magnitude. Initiatives like Taiwan Semiconductor Manufacturing Co.’s University FinFET Program and new government-funded chip-design hubs now let some well-resourced universities design for more advanced architectures, but the technology is still out of reach for many academics. What the industry-academia split means in practice Consider a startup developing an ASIC. Its engineering team may have deep expertise in a particular algorithm, sensor interface, or system architecture, the features that define its competitive advantage. But it is unlikely to possess world-class expertise in every supporting function. Developing each of these blocks internally would require significant time, capital, and specialized talent. Doing so could delay market entry beyond the startup’s viability. Even large semiconductor companies face similar constraints. Advanced-node development demands intense focus. Allocating a team to redesign a standard interface block that has already been implemented elsewhere may be difficult to justify when differentiation lies at the system level, such as an inference chip’s ability to speed up neural network computations. The time it takes to move a new chip from conception to market and risk mitigation, not self-sufficiency, govern most decisions about in-house development versus outsourcing. The economics of advanced IC manufacturing reinforce this reality. When the development cost of a leading-edge chip reaches hundreds of millions of dollars, minimizing risk becomes a central design imperative. In this context, silicon IP emerged as a practical solution. Similar to how software developers rely on preexisting libraries rather than writing every function from scratch, ASIC designers license predesigned, preverified silicon blocks—such as processor cores, memory interfaces, and security engines—from highly specialized IP vendors. These blocks can then be integrated into larger, increasingly complex systems. Design scope, verification, and time horizons With the use of silicon IP, industry is able to widen the scope of its designs. Academic efforts tend to focus on block-level innovation: a new analog-to-digital converter architecture or an ultralow-noise amplifier, for instance. These designs typically abstract away many of the complexities of bringing a chip to market, such as packaging constraints, long-term reliability, and manufacturing yield. In industry, the focus shifts to system-level integration. Modern systems on chips, or SoCs, incorporate dozens or even hundreds of functional blocks. Managing signal integrity, timing, firmware interaction, and system-level validation becomes as critical as the design of any individual block. Verification philosophy also diverges sharply. In academia, the goal of verification is to demonstrate that the concept works under nominal conditions, which may not always reflect how it would perform in real applications. Even if only a fraction of fabricated chips from a multiproject wafer operates correctly, the design may still be considered a success if it validates the underlying idea. At my academic lab for instance, we used to receive 40 chips from a TSMC prototyping service and started testing them in batches of five. If the first five or 10 chips proved functional, we had already collected more than enough data for a publication. If some of them failed, we weren’t required to mention this when publishing the results. In industry, verification is exhaustive, critical, and often dominates the development schedule. Failures are measured in parts per million, and even rare anomalies are carefully analyzed and documented to identify root causes and prevent recurrence. When I started at Silicon Creations, I was surprised by the level of detail and scrutiny designs face. Differences in time horizons and economic constraints reinforce each of these contrasts. Academic projects operate on flexible timelines aligned with research and funding cycles. If I missed a deadline, I just had to wait for the next cycle. Industry projects are driven by fixed product schedules and market windows, frequently targeting costly leading-edge nodes to achieve competitive performance, power, and area efficiency. Missing a deadline can negate the value of an entire design and may have major financial consequences along the entire supply chain. In essence, academia explores the design space, asking what is possible, while industry exploits it, determining what is viable at scale. Both are indispensable, but they operate under fundamentally different definitions of success. As ASIC complexity continues to grow, understanding both perspectives will be essential for the next generation of engineers navigating the evolving semiconductor landscape. This article appears in the June 2026 print issue.
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