Goldman Sachs CEO: The 'AI Employment Apocalypse' Is Overstated

marsbitОпубликовано 2026-05-26Обновлено 2026-05-26

Введение

Goldman Sachs CEO David M. Solomon argues that fears of an AI-driven "job apocalypse" are overblown. While AI will significantly impact the labor market—automating up to 25% of current work hours, particularly routine tasks in white-collar professions like accounting, banking, and law—it is more likely to transform jobs than eliminate them en masse. Historical trends show that technological disruptions, from electrification to the digital revolution, have ultimately led to job creation and economic adaptation. Solomon notes that AI is already generating new roles in areas like data center construction and AI workflow management. The key challenge is not mass unemployment, but ensuring that social, educational, and corporate systems help workers transition into more complex, judgment-based roles. He calls for a combined public-private effort to support reskilling and the development of AI that augments rather than replaces human labor.

Editor's Note: Whether AI will bring about an 'employment apocalypse' is becoming one of the most pressing questions in the business world. David M. Solomon, Chairman and CEO of Goldman Sachs, argues in an article for The New York Times that this concern is exaggerated. AI will indeed impact the labor market, particularly the repetitive tasks in white-collar jobs such as accounting, banking, law, software engineering, and customer service. However, it is more likely to change the nature of work rather than simply eliminate a vast number of jobs.

Solomon's core argument is: AI is not automating 25% of jobs, but 25% of work hours. In other words, some inefficient, repetitive tasks will be taken over by machines, pushing humans toward more complex tasks that rely on judgment and client interaction. Meanwhile, new demands such as data center construction, AI workflow management, and compliance verification are already creating new employment opportunities.

What this article truly aims to address is an old question within technological transformation: each new technology brings pain, but historically, economies have often been able to recreate jobs after the initial shock. The risk of AI does not lie in it necessarily causing unemployment, but in whether society, businesses, and the education system can promptly help workers complete the transition.

The following is the original text:


Over the past few months, having spoken with hundreds of business leaders, I’ve noticed a sharp divergence in their views on artificial intelligence. One camp believes an 'employment apocalypse' and mass unemployment are imminent; the other believes AI will propel society forward in a great leap.

I belong to the latter group—with some caveats, of course. Will AI disrupt the labor market? Undoubtedly. This transition, like other major shifts in history, will present new challenges, especially as AI detaches labor from productivity on an unprecedented scale. But the U.S. has a longstanding ability to create new jobs following technological shocks, from electrification in the early 1900s to the digital revolution of the 1990s. I see no reason to believe this dynamic will stop today.

There is no doubt that AI will reshape our daily lives. Goldman Sachs economists estimate that over the next decade, AI could automate up to 25% of current work hours. The impact on professions requiring hands-on, on-site operations—like food preparation, construction, and services—remains hard to judge. But in white-collar jobs, including accounting, banking, and law, many tasks are likely to be automated. A Stanford University study shows that in the occupations most susceptible to automation, such as software engineering and customer service, employment for entry-level positions has already fallen by 16% compared to the least affected roles.

Yet, looking at jobs or industries less linked to automation, the picture changes. Our economists estimate that since 2022, the growth in data center demand has created over 200,000 construction jobs. While AI may eliminate jobs in some sectors, it could also spur job growth in others. For instance, Goldman Sachs may eventually need fewer people handling regulatory reports or client onboarding processes. But this could free up space for us to hire more bankers, traders, and asset managers who engage in continuous client interaction.

Of course, we cannot ignore the real human cost behind this disruption. The Industrial Revolution did raise living standards, but only after society endured grueling labor in factories and mines, and the foul slums brought by rapid urbanization. In recent decades, the significant decline in manufacturing employment due to automation and global outsourcing has caused immense hardship for many American families and communities, such as in Gary, Indiana, and Greenville, South Carolina.

Yet despite these challenges, I keep returning to one reality: the standard of living for the vast majority of Americans is significantly higher than in the past. I was born in 1962, when the average American adult did not have air conditioning; later, as its price fell, nearly everyone enjoyed cool air. In the 1950s, only large companies like IBM owned computers; today, roughly 90% of American adults hold a supercomputer in their hands. In 1900, global life expectancy at birth was 32 years; today, it exceeds 70.

Perhaps more crucially, job growth has outpaced population growth. Since 1962, U.S. civilian employment has increased by about 145%, while the civilian population aged 16 and over has grown by approximately 128%. During this period, some new industries emerged, while others expanded or declined. Manufacturing employment fell from 15.5 million to 12.5 million, with textile and apparel manufacturing losing nearly 2 million jobs. Meanwhile, the healthcare industry now employs over 18 million people. The U.S. economy remains the world's most innovative, dynamic, and entrepreneurial.

It's true that even the most reliable historical patterns can be broken. But I believe the U.S. economy will remain resilient and dynamic as ever, for three reasons.

First, if our estimates are correct, AI will not eliminate 25% of jobs. What is more likely is that people will find more efficient ways to allocate their time. When I was a first-year banking analyst, creating a simple stock performance chart took six hours, searching through microfilm archives of The Wall Street Journal for prices. Today, a first-year analyst can do it in seconds, and in recent years, we've hired more people than ever before. As tools advance, so does the natural complexity of work. Despite the convenience brought by Excel, email, and Zoom, do any of us really feel like we have less to do now?

Second, even if a job can be replaced, it doesn't mean it will be. Television didn't eliminate the demand for live entertainment, and the internet hasn't made real estate agents or fitness coaches unemployed. Instead, these technologies highlighted and reinforced the value of these professions. Technological change and cultural change do not advance in lockstep. After all, even after decades of ATMs, digital banking, and industry consolidation, employment in commercial banking today is roughly at the same level as it was in the mid-1990s.

Third, the U.S. labor market itself is dynamic. While annual net job creation is at most a few million, the gross flows are much larger; American businesses destroy and create 25 to 35 million jobs each year. One can imagine this pace accelerating as AI drives more innovation, and we are already seeing the economy begin to adapt. Businesses are now seeking talent who can manage so-called 'agentic AI' and apply it across a wide range of scenarios, from execution and workflow to compliance and verification. All of this requires human judgment.

If AI does destroy jobs, and possibly faster than before, then public policy must respond: either by funding large-scale retraining or by encouraging the development of AI that supports workers rather than replaces them.

This must be a joint effort between the public and private sectors. The public sector should provide incentives and resources where necessary, including increased investment in vocational schools and community colleges; the private sector should help employees upskill and redesign on-the-job training systems.

The historical pattern is clear: the U.S. economy can and will adapt to major technological advances. It's also clear that even the gravest predictions from the brightest minds often prove inaccurate. In 1930, John Maynard Keynes famously predicted that by 2030, people would need to work only 15 hours a week. Although his envisioned future of abundant leisure hasn't materialized, it remains a good reminder: fears of an 'employment apocalypse' likely underestimate AI's potential to drive an economic and productivity renaissance.

In addition to leading Goldman Sachs, David M. Solomon is an electronic dance music producer known as DJ D-Sol.

Связанные с этим вопросы

QWhat is the core argument of David M. Solomon, CEO of Goldman Sachs, regarding the impact of AI on jobs?

ADavid M. Solomon argues that fears of an AI-driven "employment apocalypse" are exaggerated. His core point is that AI is more likely to automate roughly 25% of work hours rather than eliminate 25% of jobs outright. This will change the nature of work by taking over repetitive tasks, especially in white-collar fields, and push human workers toward more complex, judgment-based, and client-interactive roles.

QAccording to Solomon, what historical pattern suggests the U.S. economy will adapt to AI's impact on employment?

ASolomon points to the historical pattern where the U.S. economy has consistently created new jobs following major technological disruptions, such as electrification in the early 20th century and the digital revolution in the 1990s. He sees no reason why this dynamic should stop with AI, as the economy has shown resilience and the ability to generate employment growth that outpaces population growth over the long term.

QWhat new job opportunities has Solomon identified as being created by the rise of AI?

ASolomon identifies several new job opportunities arising from AI. For example, the demand for data center construction has created over 200,000 building jobs since 2022. Additionally, businesses are now seeking talent to manage "agentic AI" and apply it across various domains like execution, workflow, compliance, and verification. He also suggests that within his own firm, while some roles in areas like regulatory reporting may decline, there will be more hiring for bankers, traders, and asset managers focused on client interaction.

QWhat are the three main reasons Solomon gives for his optimism about the U.S. economy's resilience in the face of AI?

ASolomon provides three key reasons for his optimism: 1) AI will likely change how people allocate their time rather than eliminate entire jobs, as seen with past tools that increased work complexity. 2) Just because a job *can* be replaced by technology doesn't mean it *will* be; technology often enhances the value of certain human-centric roles. 3) The U.S. labor market is inherently dynamic, with millions of jobs destroyed and created annually, and AI-driven innovation could accelerate this churn while creating new types of work that require human judgment.

QWhat role does Solomon believe public policy and the private sector must play in managing the AI transition?

ASolomon believes that if AI displaces jobs faster than before, a joint effort from the public and private sectors is essential. Public policy should respond by funding large-scale retraining programs, providing incentives and resources, and increasing investment in vocational schools and community colleges. The private sector must contribute by helping employees upskill and redesigning on-the-job training systems to support workers rather than simply replacing them.

Похожее

Where the AI Bubble Really Is: Which Layer of Players Are Naked

AI Bubble: Where It Really Is and Who's Swimming Naked This analysis dissects the AI industry not as a single entity but as a five-layer pyramid, arguing that bubbles are concentrated in specific tiers, not uniformly distributed. **Key Distinction from the 2000 Dot-com Bubble:** Unlike 2000, where companies had stock prices before revenue, today's leading AI players have massive, contract-backed revenue driving their valuations. Core infrastructure demand is real, with every GPU running at full capacity for paying customers. **The Five-Layer Pyramid & Bubble Assessment:** * **L0 (Fab/Manufacturing) & Top L4 (Leading AI Apps): NO BUBBLE.** Companies like TSMC, NVIDIA, major cloud providers (Microsoft, Google, Meta, Amazon), and top AI labs have real revenues and orders. Supply is tightly constrained by TSMC's disciplined capacity control and physical limits like power/land for data centers, preventing a supply glut. * **L1 (Memory): BATTLEGROUND.** Sky-high HBM margins could signal a new structural cycle or a classic "boom before bust." The oligopoly of three major players may enforce supply discipline, making this a high-stakes bet. * **L2 (Interconnect/Optical Modules): BUBBLE TERRITORY.** Companies like Lumentum and AAOI have seen stock surges (4-10x) far outpacing revenue growth. This hardware segment has lower physical barriers to expansion than fabs, allowing speculation. It mirrors the 2000 bubble's epicenter—optics. * **L3 (Infrastructure/"GPU Landlords"): VULNERABLE.** GPU leasing companies profit from the current compute shortage but own no long-term moat. Their business model relies on a temporary bottleneck that will ease as big tech expands and new tech (e.g., potential space-based data centers) emerges. * **L4 Long Tail (VC-backed Startups): STRONG BUBBLE SIGNALS.** VC funding concentration in AI is twice that of the 1999 peak. Many startups with little revenue use the valuation logic of successful giants to justify their own, creating high risk of a "valuation crunch" when funding dries up. **Critical Risks to Monitor:** 1. **GPU Depreciation & Accounting:** Companies extending the assumed useful life of GPUs artificially boost profits. The true economic life depends on future generational leaps from NVIDIA. 2. **"GPU Credit" & Off-Balance-Sheet Leverage:** Emerging structures where shell companies borrow to buy GPUs and lease them out (with chipmakers sometimes investing) move debt off major balance sheets. This echoes the "vendor financing" of 2000 and the securitization risks of 2008, though currently small-scale. 3. **TSMC Abandoning Caution:** If the primary supply bottleneck (TSMC's conservative capacity planning) breaks, runaway supply could trigger a bust. 4. **Algorithmic Efficiency Breakthrough:** A major leap in software efficiency could drastically reduce the need for raw compute hardware, undermining the investment thesis. **Conclusion:** The AI boom is expensive and has frothy areas, but its core is underpinned by real demand and physical supply constraints. The bubble risk is layered: most present in optical components, GPU leasing, and the long-tail startup ecosystem, while the foundational chip manufacturing and leading application layers remain relatively solid—for now.

marsbit14 мин. назад

Where the AI Bubble Really Is: Which Layer of Players Are Naked

marsbit14 мин. назад

Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

"Standing in the Light: Understanding the Optical Module and CPO Industry Chain" This article analyzes the critical role of optical communication technology, specifically optical modules and Co-Packaged Optics (CPO), as the "nervous system" for modern AI data centers. With exponential growth in AI computational demands (e.g., NVIDIA's Vera Rubin architecture), traditional electrical interconnects using copper cables face severe bottlenecks in bandwidth, power consumption, and signal integrity over distance. The core function of an optical module is to act as a "translator," converting electrical signals from chips into optical signals for transmission over fiber (and vice-versa). Key internal components include lasers, modulators, photodetectors, drivers, and DSP chips. The industry is currently transitioning from 800G to 1.6T modules. However, the future lies in CPO. This next-generation technology integrates the optical engine directly with the switch ASIC/XPU on the same package substrate, drastically reducing power consumption (by ~3.5x according to NVIDIA), overcoming bandwidth density limits, and minimizing signal attenuation compared to traditional pluggable modules. Key challenges for CPO include advanced packaging capacity (dominated by TSMC), thermal management, repairability, and standardization. The article details the broader technology landscape, including Near-Packaged Optics (NPO, a pragmatic intermediate step), Linear-drive Pluggable Optics (LPO), Optical I/O (OIO for chip-level integration), and Optical Circuit Switches (OCS). A comprehensive CPO industry chain is mapped, highlighting shifting power dynamics: * **Architecture Definers:** NVIDIA, Broadcom, and Marvell now hold greater influence. * **Advanced Packaging & Manufacturing:** TSMC is central; Fabrinet is a key EMS player. * **Lasers ("The Heart"):** A strategic bottleneck. EML lasers are led by Lumentum and Coherent (both receiving major NVIDIA investments). CW lasers, favored for CPO/silicon photonics, see strong Chinese players like Source Photonics and Sicoya. * **Silicon Photonics Chips:** The mainstream path for CPO engines, with key players like Broadcom, Intel, Marvell, and China's Accelink. * **Fiber Connectivity Components:** A major new, high-growth market created by CPO, including Fiber Array Units (FAU), Polarization-Maintaining Fiber (PMF), and MPO connectors. Companies like Tianfu Communication and US Conec are leaders. * **Fiber & Cable:** Experiencing a super-cycle (e.g., Corning, Yangtze Optical Fiber). * **PCB/Substrates:** Requiring advanced materials (e.g., Shengyi Tech). * **DSP & SerDes:** Functions are integrated into switch ASICs in the CPO era (e.g., Broadcom, Astera Labs). * **Optical Module Makers:** Transitioning from standalone module suppliers to providers of optical engines and NPO/LPO solutions while riding the current pluggable boom (e.g., Zhongji Innolight, Eoptolink). The investment timeline is segmented: Short-term (2026-2027) features the "last feast" for pluggable modules and CPO's initial rollout. Medium-term (2027-2029) will see CPO expand and NPO peak. Long-term (2029-2032+) involves CPO/OIO penetration into intra-rack scaling. In conclusion, optical interconnects are fundamental to AI infrastructure. The competitive landscape sees US firms leading in architecture and high-end chips, TSMC in advanced packaging, and Chinese firms holding strong positions in modules, connectivity components, CW lasers, and fiber/cable. The future belongs to companies that can navigate the technological shift from "selling shovels" (modules) to "building highways" (CPO/OIO infrastructure).

marsbit24 мин. назад

Standing in the Light: A Comprehensive Guide to the Optical Module and CPO Supply Chain

marsbit24 мин. назад

Торговля

Спот
Фьючерсы

Популярные статьи

Неделя обучения по популярным токенам (2): 2026 может стать годом приложений реального времени, сектор AI продолжает оставаться в тренде

2025 год — год институциональных инвесторов, в будущем он будет доминировать в приложениях реального времени.

1.8k просмотров всегоОпубликовано 2025.12.16Обновлено 2025.12.16

Неделя обучения по популярным токенам (2): 2026 может стать годом приложений реального времени, сектор AI продолжает оставаться в тренде

Обсуждения

Добро пожаловать в Сообщество HTX. Здесь вы сможете быть в курсе последних новостей о развитии платформы и получить доступ к профессиональной аналитической информации о рынке. Мнения пользователей о цене на AI (AI) представлены ниже.

活动图片