AI Industry on KnightLi Blog

Anthropic’s 2028 AI Leadership Report: The US, China, Compute, and Two Future Scenarios

Sun, 17 May 2026 08:56:12 +0800

On May 14, 2026, Anthropic published a policy essay titled “2028: Two scenarios for global AI leadership.” The essay is not about the capability of a specific Claude model. It is about a larger question: by 2028, which political and industrial system might hold global leadership in AI?

It is important to be clear from the start: this is a policy essay with an explicit point of view. Anthropic’s core argument is that the United States and its allies should preserve and expand their lead in frontier AI, especially by defending their compute advantage, closing export-control loopholes, restricting model distillation attacks, and promoting the global deployment of the American AI stack. The following is a structured summary of the article’s main arguments, not an unconditional endorsement of every claim.

The Core Argument

Anthropic frames the AI competition of the next few years mainly as a competition between the United States and China. It argues that advanced AI is not just a commercial product, but a general-purpose technology that could reshape national security, military capability, cyber offense and defense, research speed, and social governance.

The article’s most important claims are:

Frontier AI competition is, to a large extent, a competition for compute.
The United States and its allies currently have advantages in advanced chips, semiconductor equipment, cloud infrastructure, and capital.
If the US does not close loopholes in export controls and model access, Chinese AI labs could approach or even catch up with US frontier models by 2028.

Anthropic therefore presents 2028 as a fork in the road: one scenario where democracies maintain a commanding lead, and another where US and Chinese AI capabilities are close enough to create a more dangerous neck-and-neck race.

Why Anthropic Emphasizes Compute

The original essay repeatedly emphasizes compute: the advanced chips and computing resources needed to train and deploy frontier models.

Anthropic’s logic is that data, talent, and algorithms all matter, but without enough compute, frontier models cannot keep iterating. As AI is increasingly used to accelerate AI R&D itself, compute advantage compounds: more compute enables more experiments, more experiments lead to better algorithms, and better models help build the next generation of models.

That is why the article places export controls so high on the policy agenda. Anthropic argues that US restrictions on advanced AI chips and semiconductor manufacturing equipment flowing to China have already constrained China’s frontier AI development. It also cites external analyses suggesting that the advanced-compute gap may continue widening.

In short, Anthropic is not only asking “who has smarter researchers.” It is asking who can keep accessing the compute infrastructure needed to train and serve the strongest models.

The Loopholes Anthropic Worries About

The essay argues that current export controls have been effective but insufficient. It highlights two main loopholes.

The first is compute access. This includes smuggling advanced chips, remotely using restricted chips through overseas data centers, and incomplete controls around semiconductor manufacturing equipment. The essay notes that US export controls mainly regulate chip sales, but do not fully cover remote access to restricted chips in foreign data centers.

The second is model access, described as distillation attacks. In this context, “distillation attacks” do not refer to ordinary academic distillation, but to using large numbers of accounts to bypass access controls, systematically harvest outputs from US frontier models, and train or enhance competing models from those outputs. Anthropic describes this as systematic extraction of US model capabilities.

In Anthropic’s view, these two loopholes weaken export controls: even if Chinese companies cannot legally buy enough advanced chips, they may still maintain near-frontier capability through overseas compute and model distillation.

Two 2028 Scenarios

Anthropic uses two hypothetical scenarios to show how today’s policy choices could shape the future.

Scenario One: The US and Allies Extend Their Lead

In the first scenario, the US and its allies preserve their compute advantage. Export-control loopholes are closed, chip smuggling and foreign data-center access are restricted more effectively, and defenses and penalties against model distillation become stronger.

In this world, US frontier models are 12 to 24 months ahead. This lead is not just about benchmark scores; it affects critical sectors such as cybersecurity, finance, healthcare, and life sciences. Anthropic argues that such a lead would give democracies time to set AI rules, safety norms, and global deployment standards.

It also argues that if the American AI stack becomes core global economic infrastructure, it will further attract allies, markets, and talent, creating a self-reinforcing cycle.

Scenario Two: China’s AI Ecosystem Is Near the Frontier

In the second scenario, the US does not continue tightening loopholes, or it loosens restrictions on Chinese companies’ access to advanced compute. Chinese AI labs stay near the frontier through overseas compute, chip access, distillation attacks, and rapid domestic deployment.

In this world, Chinese models may be slightly weaker than US models, but faster domestic adoption, lower cost, more flexible on-premise deployment, and infrastructure exports into certain markets give them real influence.

Anthropic worries that this neck-and-neck state could intensify risks in military use, cyber operations, and domestic governance. It could also pressure both American and Chinese AI companies to release faster, weakening safety evaluations and governance efforts.

Four Fronts of Competition

Anthropic does not treat AI competition as only a model capability race. It lists four fronts:

Intelligence: who develops the most capable models.
Domestic adoption: who integrates AI faster across commercial and public sectors.
Global distribution: whose AI stack becomes the infrastructure of the global economy.
Resilience: who maintains political and social stability through the economic transition.

Intelligence is the most important because frontier model capability drives the other fronts. But the essay also notes that intelligence alone is not enough. If one side deploys slightly weaker models faster into the economy, military, government, and overseas markets, it may offset part of the capability gap.

This is worth noting: future AI competition is not simply about who has larger models or higher benchmarks. It is a combined competition across models, chips, cloud, applications, regulation, and international markets.

Anthropic’s Policy Recommendations

The article closes with three policy directions.

First, close compute loopholes. This includes combating chip smuggling, restricting access to export-controlled chips through overseas data centers, and strengthening controls and enforcement budgets around semiconductor manufacturing equipment.

Second, defend model innovation. This includes restricting model access, deterring distillation attacks, and enabling threat-intelligence sharing between US AI labs and the government.

Third, promote the export of American AI. In other words, make hardware, models, cloud services, and applications developed by the US and its allies the trusted global AI infrastructure, reducing the chance that China’s AI ecosystem expands through low cost and local deployment advantages.

All three recommendations serve the same goal: help the US and its allies establish a more durable frontier AI lead before 2028.

How to Read This Essay

The importance of this essay is not that it reveals new model-architecture details. Its importance is that Anthropic states its view of AI geopolitics very directly.

It represents an increasingly common policy narrative among Silicon Valley AI companies: frontier AI is not just product competition, but national capability competition. Model capability, chip supply chains, cloud infrastructure, export controls, and safety governance must be considered together.

But readers should keep distinctions clear:

The argument that the US should maintain a lead is Anthropic’s policy position.
Claims about China’s AI capability, export-control effectiveness, and the scale of distillation attacks mix facts, external citations, and Anthropic’s interpretation.
The two 2028 scenarios are thought experiments, not predictions.

In other words, the essay is best read as a document explaining how Anthropic understands AI competition, not as a neutral global AI industry report.

Summary

Anthropic’s “2028: Two scenarios for global AI leadership” presents 2028 as a key decision point. If the US and its allies defend compute, restrict distillation attacks, and promote their AI stack globally, Anthropic believes they may secure a 12-to-24-month lead in frontier capability. If they do not act, China’s AI ecosystem could move close to the frontier and gain influence through domestic adoption and low-cost global deployment.

The signal is clear: Anthropic is placing frontier AI, safety governance, chip export controls, and geopolitics into one framework. Future AI competition may be less like a contest among model companies and more like a competition among compute, supply chains, national policy, and global infrastructure.

Reference:

Anthropic: 2028: Two scenarios for global AI leadership

Which industries will LLMs disrupt first? AI impact through the lens of workforce disruption

Fri, 15 May 2026 09:03:35 +0800

Discussions about LLMs and jobs often fall into two extremes. One side says AI will replace all white-collar workers; the other says it only improves productivity and will not change job structures.

The more realistic view is that LLMs do not neatly eliminate whole industries. They reorganize tasks first. Work that involves reading, writing, summarizing, classification, retrieval, explanation, support, code, reports, and process documents will feel the pressure first.

This disruption has three layers:

Some tasks are automated.
Some roles are augmented.
Some entry-level, repetitive, or coordination-heavy work is repriced.

A simple framework

To judge whether an industry is exposed, do not start with the industry name. Look at task structure.

Highly exposed tasks usually have these traits:

Inputs are text, tables, code, images, or documents.
Outputs are text, structured data, plans, emails, code, or reports.
Judgment rules can be written as checklists.
Humans can review results quickly.
Error costs are controllable, or can be reduced through review.
The task is frequent and repetitive.

Less exposed tasks rely more on physical work, field operations, complex relationships, legal responsibility, real-world perception, licenses, or high-risk decisions.

So LLMs first affect the knowledge-processing, documentation, communication, and junior-analysis layers inside industries.

Customer support and customer operations

Customer operations are among the first areas to be transformed. Many support questions can be answered from knowledge bases, historical tickets, and process rules.

LLMs can handle intent recognition, draft replies, ticket summaries, escalation decisions, QA, tone rewriting, and multilingual support.

Affected roles include text support agents, ticket handlers, after-sales support, QA reviewers, customer success assistants, and knowledge-base maintainers.

This does not mean all support disappears. Complex complaints, major accounts, emotional communication, refund disputes, and compliance boundaries still need people. The likely change is that one person manages more conversations while low-complexity issues are automated.

Administration and back office

WEF’s Future of Jobs Report 2025 lists clerical, secretarial, cashier, ticketing, and data-entry roles among those under pressure. The ILO’s generative AI exposure study also identifies clerical work as highly exposed.

The common pattern is information organization and process handoff:

Meeting minutes
Scheduling
Email drafting
Spreadsheet cleanup
Data entry
Document filing
Reimbursement and approval materials
Internal notices

This disruption can arrive quickly because companies can connect AI to office suites, chat, email, and document systems without rebuilding the whole business.

Marketing, advertising, and content

Marketing will be deeply changed, not because AI can write slogans, but because the production chain is compressed.

A campaign used to require research, positioning, copy, visuals, video scripts, landing pages, email, social variants, and A/B assets. LLMs and multimodal tools turn this into fast parallel generation and iteration.

Affected roles include junior copywriters, SEO editors, social media operators, ad creative planners, email marketers, product-description writers, localization editors, and brand tone rewriters.

The remaining value is not just writing copy. It is understanding users, channels, conversion, and brand boundaries.

Software development and IT services

Software development will not simply be replaced; it will be re-layered.

LLMs help with code generation, explanation, test completion, refactoring suggestions, migration scripts, documentation, log analysis, and bug localization. McKinsey identifies software engineering as one of the functions with high generative AI value potential.

The most exposed tasks are simple CRUD, boilerplate, unit-test completion, scripts, API glue code, documentation, low-complexity bug fixes, and junior frontend pages.

Complex system design, cross-team coordination, architecture tradeoffs, incidents, performance, security, and legacy migration still need experience.

The developer shift is clear: writing code becomes less central; defining problems, decomposing tasks, reviewing AI output, and designing validation paths become more important.

Finance, insurance, and banking

Finance is highly exposed because it contains documentation, compliance, analysis, support, and sales processes. Banking is also one of the industries McKinsey highlights.

Affected tasks include investment summaries, customer Q&A, risk-report drafts, compliance retrieval, loan pre-review, insurance-claim text processing, AML explanation, and internal knowledge-base Q&A.

Final decisions will not easily be handed to models. Regulation, accountability, audit, and data security push AI toward analysis and documentation assistance. The compressed layer is junior analysis and back-office document processing.

Law and compliance

Legal work is exposed because much of it involves reading, searching, summarizing, clause comparison, and drafting.

Affected tasks include contract drafts, clause summaries, due-diligence organization, case retrieval, compliance Q&A, legal memo drafts, document review, and version comparison.

But legal value is not only text. Responsibility, strategy, negotiation, courtroom work, client trust, and licensing remain human barriers.

The likely change is that junior lawyers and paralegals lose many repetitive document tasks, while senior lawyers focus more on judgment and risk ownership.

Media, publishing, and translation

Media and translation are directly exposed because language generation and transformation are core LLM abilities.

Affected tasks include news rewrites, summaries, headlines, multilingual translation, subtitle cleanup, interview transcript cleanup, first-pass editing, and channel-specific rewrites.

Investigative reporting, deep interviews, fact-checking, editorial judgment, and exclusive sources still require people. But low-value, template-driven content will become cheaper.

Translation will also split: general text and internal documents will be machine-handled, while legal, medical, literary, brand, and cross-cultural work still needs professionals.

Education and training

Education will not disappear, but it will be restructured.

LLMs can provide personalized Q&A, homework feedback, quiz generation, lesson plans, course outlines, learning paths, language practice, and mock interviews.

Affected roles include teaching assistants, question-bank editors, lesson-plan writers, basic tutors, course operators, and learning-report producers.

Education is more than knowledge transmission. Motivation, companionship, classroom management, values, and complex feedback still need people. AI is more likely to replace batch tutoring and content preparation than excellent teachers.

Consulting, research, and enterprise services

Consulting, research, audit, HR, and enterprise services all rely on information collection, structured analysis, and document expression.

Affected tasks include industry research, competitor analysis, interview notes, slide drafts, weekly reports, data explanation, JD generation, resume screening, and employee-handbook Q&A.

The risk is not only to partners. Junior analysts traditionally learn by gathering materials, making tables, and writing drafts. If AI takes over those tasks, companies need a new training path.

Healthcare, pharma, and life sciences

Healthcare adoption will be cautious, but the impact can be deep.

LLMs will first enter medical-record summaries, patient communication material, literature reviews, clinical-trial documents, drug-research support, insurance materials, medical customer service, and physician assistants.

Core diagnosis and treatment responsibility will not easily move to models, but documentation and knowledge-retrieval burden will fall.

Industries moving more slowly

Industries that depend on physical work, field operations, real-world risk, and human presence will move more slowly:

Construction
Nursing and elder care
Repair trades
Logistics handling
Kitchens
Fire and emergency work
Field agriculture
High-end manual manufacturing

But “slower” does not mean untouched. Scheduling, training, quotes, support, inventory, maintenance records, quality reports, and internal knowledge bases can still be transformed.

The real change is job structure

LLM workforce disruption is not just an industry list. It is a change in role structure.

First, some junior roles shrink. Repetitive writing, research cleanup, basic analysis, simple code, and support replies are easier to automate.

Second, mid-level roles become tool-augmented. Workers who use AI well handle more tasks; those who do not may look slower.

Third, senior roles emphasize judgment. Strategy, review, responsibility, communication, system design, and risk tradeoffs become more valuable.

The real question is not whether AI affects your industry, but how much of your work can be textualized, proceduralized, and checklist-reviewed.

Summary

Current LLMs will first affect knowledge-intensive, text-heavy, process-heavy areas: support, administration, marketing, software, finance, law, media, education, consulting, medical documentation, and R&D support.

They will not change all industries at the same speed or in the same way. Regulated, high-risk, trust-heavy industries will use more augmentation; repetitive and reviewable tasks will see more automation.

For individuals, the useful preparation is to decompose your work: which tasks can go to AI, which must stay human, and which abilities make you the reviewer, orchestrator, and final owner.

References:

World Economic Forum, Future of Jobs Report 2025: https://www.weforum.org/publications/the-future-of-jobs-report-2025/
International Labour Organization, Generative AI and Jobs: https://www.ilo.org/publications/generative-ai-and-jobs-global-analysis-potential-effects-job-quantity-and
McKinsey, The economic potential of generative AI: https://www.mckinsey.com/capabilities/mckinsey-digital/our-insights/the-economic-potential-of-generative-ai-the-next-productivity-frontier
OpenAI / OpenResearch / University of Pennsylvania, GPTs are GPTs: https://openai.com/index/gpts-are-gpts/

What Jensen Huang Was Really Saying in His CMU Speech

Thu, 14 May 2026 20:59:50 +0800

Jensen Huang’s CMU speech looks, on the surface, like a mix of personal memory and startup storytelling. In reality, it was a cold shower for a group of top university graduates.

His core message was not “everything will become easier”. It was this: the AI era has arrived, and the old stable, respectable, linear career path may no longer hold. Young people need to prepare for hardship again, and they may also need to accept work that once looked less glamorous.

First Layer: I Had a Hard Childhood, and You May Have Hard Times Too

Huang talked about his childhood: waking up at 4 a.m. to deliver newspapers, then later washing dishes at Denny’s.

That story is motivational, of course, but it is not just nostalgia for struggle. He was speaking to Carnegie Mellon students, people who would normally have a clear path into investment banks, software companies, tech giants, and high-paying jobs.

So the real point was: do not assume you can graduate and keep walking along the comfortable path that worked for previous generations.

AI is rewriting the value of many jobs. The old model of rising through credentials, resumes, and big-company pipelines may be compressed. Many people may discover that they also have to go through a rougher, less polished, more foundational period of work.

Second Layer: Take Off the Gown and Do the Work That Is Actually Needed

Huang went from delivering newspapers to washing dishes at Denny’s, and described that as a major career advancement.

That sentence matters. He was saying that career value does not necessarily come from the title. It comes from whether you are inside real demand.

In today’s AI industry, the message may be: stop staring only at investment banks, internet software companies, consulting firms, and traditional white-collar jobs. The places that truly lack talent in the future may be more basic, more engineering-heavy, and more physically demanding.

For example:

building data centers;
working on power and cooling;
operating machine rooms;
handling electrical, plumbing, and infrastructure work;
deploying GPU clusters;
delivering AI factory engineering projects.

These jobs do not sound as polished as “joining a big company to write software”. But in the AI era, they may become the new key positions.

So “become a plumber, electrician, or data center builder” is not just a joke. It is a reminder to graduates: AI is not only models and code. It also needs electricity, land, data centers, networks, cooling, operations, and supply chains. Whoever can actually build those things stands in one of the hardest parts of the industry.

Third Layer: Hard Things Are Always Harder Than They Look

Huang also said that whenever NVIDIA ran into trouble, the team would ask: how hard can this be?

The answer, every time, was that it was harder than they first imagined.

That is a sentence every founder and engineer should hear. Many things look like just a project on a slide deck, just a roadmap item in a meeting, or just a trend inside a strategic narrative. But once you actually do them, you run into supply chains, capital, engineering, customers, organizations, competition, and time pressure.

This is especially true in the AI era.

Training models is hard. Deploying models is also hard. Making a demo is hard. Turning a demo into a reliable product is harder. Buying GPUs is hard. Keeping those GPUs fully utilized, stable, and commercially productive is even harder.

So Huang was not offering easy optimism. He was expressing engineering realism: you can be optimistic, but do not underestimate the difficulty.

The Real Reminder in This Speech

If the speech had to be compressed into one sentence, it would be this:

The AI era will not automatically reward smart people. It will reward people willing to enter real difficulty, real infrastructure, and real engineering work.

CMU students will of course still have many opportunities. But if they simply follow the path of previous graduates, find a stable role at a big company, and wait for career inertia to keep working, being left behind is not impossible.

What Huang was really telling them was: do not only imagine yourself walking from a graduation gown into a polished office. The future opportunities may be in data centers, power systems, cooling pipes, GPU clusters, and jobs that do not look elegant or white-collar at first.

AI will not only change software jobs. It will also redefine what counts as a good job.

Musk vs. OpenAI Trial: Nonprofit Mission, Control, and the AI Race

Fri, 08 May 2026 23:37:37 +0800

The lawsuit between Elon Musk, OpenAI, and Sam Altman looks on the surface like a falling-out between former partners. Underneath, it raises one of the central structural questions in AI: when building frontier models requires enormous capital, can an organization founded around public benefit, openness, and safety move toward a more commercial form, and under what constraints?

The dispute keeps attracting attention not only because the people involved are among Silicon Valley’s most influential figures, but also because it puts three OpenAI tensions on stage at once: nonprofit mission versus commercial financing, AI safety rhetoric versus market competition, and founder contribution versus later control.

What the trial is really about

Based on public reports, Musk’s core argument is that OpenAI had a clear public-benefit mission at founding, and that his early donations and involvement were meant to support an AI organization that would not enrich individuals but serve humanity. In his view, OpenAI’s later creation of a for-profit entity, acceptance of large investments, and rise into a highly valued company betrayed those original commitments.

OpenAI’s response is that Musk’s donations did not carry the permanent restrictions he now claims. It argues that the for-profit structure was created to obtain compute, talent, and capital needed to keep pursuing safe advanced AI. OpenAI also says Musk did not oppose for-profit structures as such, but wanted control.

So this is not a simple “nonprofit versus for-profit” dispute. The narrower questions are: what legal force did OpenAI’s original mission have? Was Musk’s $38 million contribution a normal donation or a charitable trust with enforceable conditions? Did OpenAI’s later restructuring remain under nonprofit control?

Musk’s story

Musk has argued in court that he helped create OpenAI to prevent AI from being controlled by a handful of commercial giants. He describes the structural changes at OpenAI as looting a charity and warns that allowing it would undermine the foundation of charitable giving.

This narrative is powerful because it highlights the contrast between OpenAI’s early public image and its later commercial success. OpenAI began with the image of a nonprofit research lab focused on safety, openness, and public benefit. Today it is a central commercial player in the global AI race, deeply tied to major partners such as Microsoft.

But Musk’s side also faces a question: did he once accept some form of for-profit arrangement? If he discussed creating a for-profit entity but wanted nonprofit control or greater personal control, then the case becomes less about whether a for-profit structure could exist and more about who controlled that structure.

OpenAI’s story

OpenAI’s public page and courtroom defense emphasize a different line: OpenAI has always been governed by a nonprofit, and the for-profit entity was created to raise the resources needed for its AGI mission. OpenAI frames Musk’s lawsuit as a reaction to failing to obtain control, followed by his creation of competing company xAI.

OpenAI also says Musk donated $38 million to the nonprofit, that the money was used for the organization’s mission, and that Musk is now trying to reinterpret that donation as an investment. According to OpenAI, Musk sought absolute control and even proposed folding OpenAI into Tesla before leaving after his terms were rejected.

The point of this narrative is to move the case from “OpenAI betrayed its public mission” to “Musk did not get the control he wanted.” If the jury and judge accept that framing, Musk’s moral accusation becomes weaker and the case looks more like a delayed founder control fight.

Why the nonprofit structure matters

The complexity of OpenAI is not simply that it earns commercial revenue. It is the governance structure. OpenAI is neither a traditional commercial company nor a research institute detached from markets. It tries to let a nonprofit control a for-profit subsidiary, using capital markets to obtain compute and talent while preserving the mission of benefiting humanity.

That structure has a practical rationale. Training frontier models requires data centers, chips, researchers, safety evaluations, and global product infrastructure. Donations alone are unlikely to sustain that scale.

But the more complex the structure becomes, the higher the trust cost. People naturally ask whether nonprofit control is actually effective, whether commercial partnerships change research direction, and who decides when safety promises conflict with product growth. That is why the Musk v. OpenAI case draws such broad attention.

The trial is not an AI safety referendum

The courtroom will repeatedly invoke AI safety, AGI risk, open-source promises, and public benefit. But it remains a legal case. The court is dealing with donation terms, charitable trust claims, organizational governance, control, and unjust enrichment, not writing AI safety policy for the entire industry.

In other words, even if Musk wins, the court will not necessarily produce a full AI safety governance framework. Even if OpenAI wins, questions about commercialization and mission drift will not disappear.

The important signal is how the court treats early public commitments by AI organizations. Where is the boundary between founder donation and later commercialization? How should a nonprofit-controlled AI company be supervised? Those questions matter beyond this case.

What it means for the AI industry

The lawsuit is a warning to the broader AI industry: once a grand public-benefit narrative meets enormous capital requirements, governance has to be clear enough to carry the weight. Otherwise, early mission statements, donor expectations, employee incentives, investor returns, and social risk all end up in the same legal and public-relations battlefield.

For other AI companies, that means:

Founding documents, mission statements, and donation agreements must be clearer.
The boundary between nonprofit and for-profit entities cannot be vague.
Safety commitments need auditable governance, not just marketing language.
Conflicts among founders, investors, and public benefit should be addressed before financing.

OpenAI’s size amplifies these issues, but they are not unique to OpenAI. As AI companies absorb more capital and enter medicine, education, defense, productivity, and consumer products, these governance conflicts will keep returning.

Summary

The core of Musk v. OpenAI is not only who betrayed whom. It is whether a frontier AI organization can prove that it remains bound by its mission as it moves from research lab to super-platform.

Musk’s side is trying to show that OpenAI departed from its original charitable mission. OpenAI’s side is trying to show that commercialization was necessary to pursue that mission, and that Musk’s lawsuit is a response to losing control. The outcome will depend on evidence, donation documents, organizational charters, and communications from the relevant years.

Whatever the result, the trial has already made one thing clear: AI companies cannot maintain trust with slogans about benefiting humanity alone. The closer they get to AGI and the more commercial value they control, the more transparent, verifiable, and court-tested their governance must become.

References:

Silicon Valley CTOs Are Joining Anthropic as MTS: Is It Really Just Idealism?

Wed, 06 May 2026 08:39:25 +0800

A notable trend has emerged in Silicon Valley: some people who had already become CTOs, co-founders, or CPOs are leaving their companies and joining Anthropic as Member of Technical Staff, commonly shortened to MTS.

On the surface, this looks like moving from an executive role back to an ordinary technical position. But in the context of the AI industry, it looks more like the previous generation of software and internet elites choosing a new power center, a new career label, and a new form of leverage.

The Event Itself: Executives Move Toward Frontier Labs

What makes this shift interesting is that these are not junior engineers. They are people who already held executive titles. They used to control teams, budgets, roadmaps, and organizational influence. Now they are choosing to enter frontier AI labs like Anthropic and take roles closer to hands-on technology and product implementation.

In traditional technology companies, CXO means organizational power: how many people you manage, how much budget you control, and how much say you have over the roadmap. But in frontier AI companies, the source of power is changing. What is truly scarce may no longer be the size of the organization you manage, but how close you are to models, data, productization capability, and enterprise deployment scenarios.

So MTS should not be simplistically understood as a low-level role. At companies like Anthropic and OpenAI, MTS is often a senior technical position. It may not come with a large direct team, but it can be closer to model capabilities, product decisions, and enterprise customer needs.

Why This Is Happening Now

This shift is not an isolated personal choice. It is the result of several industry forces converging.

First, technology itself has become important again. After many technical people become CTOs, their daily work shifts from coding to management, hiring, budgets, roadmaps, and company politics. With large models emerging, the technical front line has again become the place with the highest leverage. The closer someone is to models, the more likely they are to understand the next generation of product forms, organizational models, and business models.

Second, the growth narrative of traditional software companies is weakening. Mature SaaS companies can still make money, but it is hard for them to tell the early-stage story of tenfold or hundredfold growth. AI search, AI IDEs, and agent tools are also being squeezed by foundation model companies. When model companies move upward into the application layer, many previously promising markets get revalued.

Third, the career market is being repriced. In the past, the most valuable label for an executive might have been “took a company public”, “completed an acquisition”, or “helped investors exit”. But if a company’s growth stalls, the IPO window narrows, or its sector is rewritten by AI, the executive’s label can become awkward. Moving to Anthropic is essentially a way to acquire a new label that fits the AI era.

Power Shift: From Organizational Power to Model Power

Traditional technology companies derive power from organizational structure: how many people you manage, how many systems you control, and how much budget you decide.

In the AI era, the new source of power is becoming something else:

How close you are to the strongest models.
Whether you can mobilize model capabilities.
Whether you can turn model capabilities into products.
Whether you can use AI to amplify individual and team output.

From this perspective, a CTO joining Anthropic as an MTS is not necessarily a downgrade. More accurately, it is a switch from organizational power in a traditional software company to model power in a frontier AI company.

Software companies used to build moats through organization, sales, channels, compliance, customer success, and accumulated business processes. Now agents, Claude Code, enterprise automation tools, and model APIs are revaluing those moats. Whoever can embed model capabilities into real workflows can capture new growth.

The Original Companies: Maturity, Pressure, and Exit Windows

The companies these executives leave are not necessarily failures. Many still have revenue, customers, teams, and stable businesses. The problem is that their industry position has changed.

Once mature SaaS companies enter a stable growth phase, it becomes harder for them to offer executives major career upside. AI search, AI IDEs, and many vertical AI applications are directly pressured by foundation model companies. Companies that are still growing but not yet public face another practical issue: whether capital markets will accept them, whether post-IPO valuation can hold, and whether investors can exit smoothly.

This creates real pressure. Staying at the original company may bring labels such as “mature business operator”, “executive during a slowdown”, or “leader of a sector rewritten by AI”. Joining Anthropic creates the opportunity to gain labels like “frontier lab experience”, “enterprise AI productization”, and “agent-era organizational knowledge”.

Career Labels: Not Abandoning Leverage, but Switching Leverage

CTOs at growth-stage companies are not always the people who built the core system from zero to one. When a company reaches Series B or C, or prepares for IPO or acquisition, it often adds executives to complete the leadership team and make the company look more governable, auditable, and financeable.

The value of these executives lies in:

Completing technical teams and management processes.
Increasing investor confidence.
Helping the company tell a credible financing, IPO, or acquisition story.
Accompanying the company to the next financing round, IPO, or acquisition.

In venture capital terms, the most important label for this kind of person is “successful exit”. If someone has helped a company go public or get acquired, they become more valuable to investors. Conversely, if a company’s growth stalls, fails to list, or is rewritten by AI, the executive may carry an unattractive label.

So joining Anthropic is not abandoning leverage. It is switching leverage. The old leverage was “I can take a company public or through acquisition”. The new leverage is “I have worked on models, agents, and enterprise AI deployment inside a frontier AI lab”.

The next time they start a company, join a new company, enter the investment ecosystem, or help traditional enterprises with AI transformation, these experiences become a new premium.

Anthropic’s Calculation: Absorbing Old Software Expertise

Anthropic is not merely accepting people with ideals. It needs these people because model companies cannot enter the enterprise market with model researchers alone.

These executives may not be the strongest model training experts, but they understand software engineering, enterprise customers, organizational processes, hiring systems, productization, and public company governance. They know how enterprise customers buy, who pushes or blocks adoption inside large organizations, and how a tool must fit into workflows to actually sell, be used, and renew.

This matters to Anthropic. Its battlefield is no longer just model APIs or the Claude chat interface. It also wants to enter enterprise workflows, software development, knowledge management, consulting services, and AI transformation for companies backed by private equity.

To enter these scenarios, Anthropic needs people who know the old software world map: where customer pain points are, where organizational resistance appears, where budgets sit, how compliance and governance work, and how to package products into services enterprises can buy.

Industry Impact: Talent and Capital Are Voting Again

The consequences of this shift may unfold along several lines.

First, talent loss from traditional software companies may accelerate. In the past, strong executives moved among mature software companies, growth-stage SaaS firms, and pre-IPO startups. Now frontier AI labs have become a new high ground. Talent voting with its feet will also affect how capital evaluates sectors.

Second, enterprise software will be revalued. Enterprise software used to sell processes, permissions, reports, compliance, and customer success. In the future, enterprise customers may care more about whether the software can let AI agents complete work directly, reduce labor, connect to model capabilities, and become part of an automated workflow.

Third, executive career paths will change. The traditional path of joining a growth company, helping with financing, pushing toward IPO, and exiting through equity will narrow. A new path may emerge: join a frontier model company, understand AI-native organizations and products, then take that experience into the next company, startup, or enterprise AI transformation project.

Fourth, model companies will increasingly resemble enterprise service companies. They will not only sell APIs, but also tools, workflows, consulting, industry solutions, and organizational transformation. Anthropic’s attraction of old software executives is a way to build this capability.

Idealism and Realistic Interest Can Coexist

This cannot be reduced to either pure idealism or pure financial calculation.

Many technical people genuinely love technology and want to return to the front line. In a period of rapid model evolution, working close to frontier systems is highly attractive. But career labels, financial leverage, industry position, and future exits also matter.

Human motivations are usually mixed. Idealism and practical interest do not contradict each other. A person can believe in the long-term value of AGI or enterprise AI while also knowing clearly that joining Anthropic now will make their next career narrative more valuable.

Core Judgment: AI Is Reordering Industry Power

The most important point about executives moving to Anthropic is not the change in individual titles, but that AI is reordering power across the software industry.

In the past, the more people you managed, the closer the company was to IPO, and the higher your title was, the more valuable you were as a CXO. Now, people who are closer to models, better at productizing model capabilities, and more capable of wielding powerful AI systems are becoming scarce again.

For individuals, joining Anthropic means changing labels, leverage, and narrative.

For Anthropic, attracting these people means stockpiling old software-world expertise for the enterprise battlefield.

For traditional software companies, talent and capital are already voting again.

For ordinary programmers, the most important future capability may not be how many people you manage, but whether you can wield the strongest AI systems and turn them into real productivity.

Summary

Silicon Valley CTOs joining Anthropic as MTS is not simply a story of executives being demoted.

It looks more like an industry power migration: smart people from the previous generation of software companies are judging where the next center of leverage will be. On the surface, they are leaving management roles. In reality, they may be leaving old tracks and attaching themselves early to the new labels of the AI era.

If more traditional software executives, AI application founders, and mature SaaS technical leaders move toward model companies, this will no longer look like individual career choice. It will look like the talent structure and capital narrative of the software industry shifting as a whole.