AI Leads to Layoffs? Research Shows AI Is More Expensive Than the People It Replaces

marsbitОпубликовано 2026-06-09Обновлено 2026-06-09

Введение

Title: AI Layoffs? Research Shows AI is More Expensive Than the Workers It Replaces. This year, nearly 50,000 employees have been laid off due to AI, with companies initially believing AI could replace human jobs. However, recent findings indicate that the actual operational costs of AI often exceed the expense of the human labor it was meant to replace. Examples include Uber exhausting its annual AI budget in just four months, Microsoft cutting Claude Code licenses due to high costs, and an Anthropic employee incurring $150,000 in API usage in a single month. A CloudZero survey reveals that 45% of enterprises spend over $100,000 monthly on AI, yet only 8% of S&P 500 companies report any AI-related revenue, and half struggle to measure ROI. Analyst Scott Galloway predicts a shift toward cheaper Chinese AI models, which are 10 to 30 times more affordable than American counterparts. Data shows Chinese models' usage among developers surged from 1% in 2024 to over 60% by mid-2026, with 80% of U.S. AI startups adopting them. This trend may prompt regulatory responses, such as potential restrictions from the Trump administration.

Authors: Scott Galloway / Ed Elson / Mia Silverio

Translation: TechFlow

TechFlow Introduction: Nearly 50,000 people have been laid off this year due to AI, but more and more companies are finding that the cost of using AI is higher than human labor. Uber burned through its entire 2026 AI budget in four months, Microsoft is cutting Claude Code licenses in multiple departments, and an Anthropic employee used up $150,000 in API credits in a single month. Scott Galloway believes companies will ultimately turn to Chinese large language models that are 10-30 times cheaper, which will force Trump to impose restrictions.

Is AI More Expensive Than the People It Replaces?

Nearly 50,000 employees have been laid off this year, citing AI as the reason. This figure nearly equals the total for all of 2025. For companies adopting AI, the logic is simple: AI can do the jobs people do.

But in recent weeks, this logic has hit a wall. More and more companies are discovering that the actual cost of using AI is higher than the human labor it is intended to replace.

Figure: The AI Cost Shock for Businesses – AI spending and cost feedback from companies like Uber, Microsoft, Nvidia, Meta

Uber burned through its entire 2026 AI budget in just four months. The COO said it's becoming increasingly difficult to justify AI expenditures internally. Microsoft is cutting Claude Code licenses in multiple departments for one simple reason: cost.

A Nvidia executive stated that compute costs now "far exceed employee costs." Meta, Pinterest, and Spotify all cited rising inference costs as a drag on margins in their Q1 earnings reports.

How big are corporate AI budgets? A survey by cloud cost management company CloudZero shows that in 2025, 45% of businesses spent over $100,000 per month on AI, up from only 20% the previous year.

An even more extreme case within Anthropic: one employee spent $150,000 on Claude Code in a single month. For that to be financially justifiable, this engineer would need to do the work of 11 average engineers.

In the current market, the performative value of the word "efficiency" has been consistently rewarded, to the point where companies don't even need to actually calculate efficiency. 79% of S&P 500 companies mentioned AI in their recent earnings calls, but only 8% disclosed any AI-related revenue.

Figure: S&P 500 Companies' AI Rhetoric vs. Actual Revenue Disclosure

The same CloudZero report also found that only half

Chinese Large Models Will Be the Biggest Winners

Scott Galloway's judgment is: companies will ultimately turn to the cheapest models, which are Chinese large language models. Chinese models are 10 to 30 times cheaper than American models.

Data is already validating this trend: the share of Chinese models in developer usage surged from about 1% in 2024 to over 60% in May of this year, and 80% of US AI startups are using Chinese open-source AI models.

Figure: Changes in Share of Chinese Large Models in Developer Usage & Usage by US AI Startups

Связанные с этим вопросы

QWhat is the main finding about AI's cost compared to human labor based on the article?

AThe article finds that an increasing number of companies are discovering that the actual operational cost of AI is higher than the cost of the human labor it is intended to replace.

QWhich companies are mentioned as facing significant AI cost challenges?

AUber, Microsoft, Nvidia, Meta, Pinterest, Spotify, and Anthropic are mentioned as companies facing high AI costs or budgetary issues.

QAccording to the article, what percentage of S&P 500 companies disclosed AI-related revenue in their recent earnings calls?

AOnly 8% of S&P 500 companies disclosed any AI-related revenue in their recent earnings calls, despite 79% mentioning AI.

QWhat does Scott Galloway predict will be the solution for companies struggling with high AI costs?

AScott Galloway predicts that companies will eventually turn to cheaper Chinese large language models, which are 10 to 30 times less expensive than American models.

QWhat dramatic increase in usage of Chinese AI models is highlighted in the data presented?

AThe data shows that the share of Chinese models among developers soared from about 1% in 2024 to over 60% by May of the article's year, with 80% of U.S. AI startups using Chinese open-source AI models.

Похожее

Humanity Loses $31 Million in Attack, Token Price Plummets 90% Due to a Single Private Key

On June 9th, the digital identity project Humanity Protocol suffered a major security breach resulting in over $31 million in losses. According to on-chain analyst Specter, hundreds of wallets holding the project's H token were drained. The attack was confirmed by founder Terence Kwok to be caused by the compromise of a foundation member's private key. As a precaution, users are advised to avoid interacting with Humanity's cross-chain bridge or liquidity pools. The incident caused the H token price to crash over 90%, from around $0.70 to a low of $0.052, wiping its market cap from $2 billion to approximately $35.7 million. The attacker allegedly minted 100 million new H tokens and is selling them for BNB. This breach adds to existing controversies surrounding Humanity Protocol. Founded in 2024, it aimed to verify human users via palm-print biometrics and zero-knowledge proofs. However, a leaked conversation in 2025 revealed that only about 1 million of its 9 million claimed Human IDs had completed biometric verification, suggesting 88% might be bots. Furthermore, the project has faced allegations of being a repackaged product from a Chinese access control vendor, raising privacy and authenticity concerns. Founder Terence Kwok's previous venture, Tink Labs, a hotel smartphone startup that raised $170 million, failed and entered bankruptcy in 2020 after burning through its funding. The current attack highlights the persistent critical issue of private key management in crypto. Unlike smart contract exploits, a private key compromise bypasses all on-chain security mechanisms. With no user compensation plan announced yet, this $31 million breach may be a final blow to the project's credibility, already weakened by previous controversies and a heavily depreciated token.

marsbit21 мин. назад

Humanity Loses $31 Million in Attack, Token Price Plummets 90% Due to a Single Private Key

marsbit21 мин. назад

MicroStrategy Will Not Die in This Downturn: Reflexivity, STRC Anchoring Back to Par, and the Self-Rescue Logic of "Sell Stock, Not Bitcoin"

This article analyzes the recent sharp decline in Bitcoin and MicroStrategy (MSTR), framing it as a targeted "reflexivity" attack. The trigger was MSTR using its cash reserves to buy back convertible notes, raising market concerns about a liquidity crisis. The playbook follows George Soros's principle: market expectations can shape reality. Fears that MSTR might be forced to sell BTC caused panic selling, lowering BTC's price and worsening MSTR's financial ratios, thus reinforcing the negative narrative. The author argues that MSTR's Structured Convertible (STRC), while falling in price, is a floating-rate security that will eventually return to par value (100). The price drop reflects the market demanding a higher yield due to perceived risk, but as a floating-rate instrument, its coupon can adjust, naturally pulling the price back to par over time. This is crucial for MSTR's continued ability to raise funds. The core thesis is that MSTR's best move to counter the attack is to **issue new equity (sell shares)**, not sell its Bitcoin holdings. While selling BTC would solve the immediate cash crunch, it would destroy the company's core investment thesis and premium. It would dilute the BTC per share, likely erase the market premium over its net asset value (mNAV > 1), and worsen its debt-to-asset ratio. Issuing shares while mNAV is high (e.g., 1.25x) allows MSTR to raise cash for reserves without harming shareholder value or the "perpetual accumulation" narrative. It improves the debt ratio and reassures STRC holders, breaking the negative reflexivity cycle. In conclusion, while MSTR could survive this episode even by selling BTC, doing so would fundamentally alter its investment proposition and weaken it for future cycles. The optimal, value-preserving strategy is to sell equity to rebuild reserves and maintain the long-term growth flywheel.

marsbit22 мин. назад

MicroStrategy Will Not Die in This Downturn: Reflexivity, STRC Anchoring Back to Par, and the Self-Rescue Logic of "Sell Stock, Not Bitcoin"

marsbit22 мин. назад

Humanity Loses $31 Million, a Private Key Causes Token Price to Plunge 90%

On June 9th, the digital identity project Humanity Protocol suffered a major security breach resulting in over $31 million stolen from hundreds of wallets holding its H token. The attack was caused by the compromise of a private key belonging to a foundation member, leading the team to advise users against interacting with its bridge or liquidity pools. Following the incident, the price of the H token plummeted by over 90%, from around $0.70 to a low of $0.052, wiping out a significant portion of its market capitalization. The attacker allegedly minted 100 million new H tokens and began selling them for BNB. Humanity Protocol, founded in 2024, aimed to verify human users through palm-print biometrics and zero-knowledge proofs on Polygon CDK. Despite raising $50 million across two funding rounds and achieving a unicorn valuation, the project faced prior controversies. Shortly after its June 2025 token launch, reports emerged that only about 1 million of its 9 million registered IDs had completed biometric verification, suggesting 88% might be bots. Furthermore, allegations surfaced that the project might be a rebranded "shell" of a Chinese access control company, raising concerns about data privacy and authenticity. The project's founder, Terence Kwok, has a controversial business history. His previous venture, Tink Labs, burned through $170 million in funding before collapsing in 2020. The breach highlights the persistent critical risk of private key management in crypto. With no user compensation plan detailed in the initial response, the incident deals a severe blow to trust in a project already struggling with credibility issues.

Foresight News43 мин. назад

Humanity Loses $31 Million, a Private Key Causes Token Price to Plunge 90%

Foresight News43 мин. назад

How to Conduct Deep Research Using Claude's Dynamic Workflows

The article "How to Use Claude's Dynamic Workflows for Deep Research" discusses overcoming the pitfalls of technical research, where both humans and AI can get overwhelmed by information, leading to vague conclusions. It introduces Claude Code's new "Dynamic Workflows" feature, which automatically designs and executes task-specific workflows before starting a task, unlike simpler "planning modes." This approach incorporates validation, result convergence, and adversarial verification from the outset. The core of Dynamic Workflows is six predefined scheduling patterns that address how to decompose tasks and synthesize results: 1. **Classify-and-Act (Routing):** An agent classifies the task and routes it to the most suitable specialist agent for execution. It's precise and efficient but struggles with ambiguous tasks. 2. **Fan-out & Merge:** The task is split into parallel, independent subtasks whose results are later merged. It's fast and isolates contexts but is more expensive and challenging to synthesize. 3. **Adversarial Verification:** Multiple "challenger" agents critique a worker agent's conclusion, requiring majority approval. This counters confirmation bias and self-assessment errors but relies on verifiable facts. 4. **Generate & Filter:** Multiple agents generate many candidate solutions, which are then filtered against a rubric to output only the best. It fosters diversity but depends heavily on the filter's quality. 5. **Tournament:** Multiple agents compete on the same task, with pairwise comparisons eliminating contestants over rounds to select the best. This offers stable relative judgment but is complex. 6. **Loop:** An agent iteratively attempts a task, learning from errors and adjusting until a stop condition is met. It handles tasks with unknown scope but risks infinite loops without proper design. The author compares their own custom deep-research system, which involved multi-agent analysis and deduplication but lacked goal-oriented convergence, to Claude's built-in workflow. The official workflow adds critical layers: initial problem decomposition, credibility assessment of sources, cross-agent voting to delete weak conclusions (not just averaging), and output tightly focused on the user's original goals and actionable recommendations. This structurally addresses common AI issues like goal drift, premature stopping, context pollution, and output bias. In summary, Dynamic Workflows represent a shift from smarter single conversations to a structured research process, compressing what used to require many dialogues into 3-4 interactions, albeit at higher token cost. The author notes remaining challenges for their specific domain (blockchain research): the need for fact-based verification over official documentation, depth in truly novel interdisciplinary thinking, the practical validation of proposed solutions, and tailoring information density to the audience.

marsbit53 мин. назад

How to Conduct Deep Research Using Claude's Dynamic Workflows

marsbit53 мин. назад

When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

When LPs Use Doubao to Teach Investing: A Transition Story of a Private Equity GP AI is making life increasingly difficult for small private equity fund managers, as a former GP of an offshore dollar fund reveals. The fund, managing tens of millions in US stocks, outperformed the Nasdaq but struggled with fundraising. Its traditional Cayman SPC/BVI structure failed to attract major Asian LPs, who now prefer Hong Kong LPF or Singapore VCC frameworks. The rise of AI-powered quantitative strategies has further squeezed the space for funds like his, which relied on subjective, discretionary investing. AI tools have leveled the information playing field, empowering LPs—often high-net-worth individuals, entrepreneurs, or family offices—to analyze investments themselves using chatbots like Doubao. This has eroded trust in GPs' expertise, leading to more frequent challenges over investment decisions and even withdrawals, especially during market rallies when retail investors sometimes outperform funds. Friction arises not necessarily from AI's capabilities but from how LPs use it. Many rely on conversational AI for validation rather than rigorous analysis, sometimes receiving misleading or hallucinated advice. While AI democratizes research, effective investing still requires discerning real insight from plausible-sounding output. Ultimately, AI is unlikely to fully replace GPs. Asset management remains a trust-based service. However, the industry must adapt. The future may see "human私募" (private equity) learning from AI and focusing more on providing value beyond pure analysis—perhaps by mastering the emotional intelligence and trust-building that machines cannot replicate.

Odaily星球日报1 ч. назад

When LPs Teach Me Investment with Doubao: A Self-Narrative of a Private Equity GP Switching Careers

Odaily星球日报1 ч. назад

Торговля

Спот

Фьючерсы

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 PEOPLE (PEOPLE) представлены ниже.