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4 May 2026, 14:39

Cryptopolitan Report: 38% of Our Readers Have Never Used an AI Agent. Here’s What That Tells Us About Where Adoption Actually Stands

The global AI agent market currently stands at an approximate valuation of over $12 billion in 2026 and this is growing at nearly 45% annually. Despite this explosive rise, our newsletter poll highlights an adoption gap that continues to exist even among a digitally savvy and tech forward audience. This report breaks down the growth of this space, the results from the poll and what the numbers tell us about awareness and, ultimately, the obstacles holding back adoption. In its simplest form, an AI agent is software that not only responds to a user’s prompt but also takes action. Standard large language models interaction is based on the user asking a question and receiving an answer. An agent on the other hand can plan multi-step tasks, use external tools, browse the internet, write and execute code, manage files, send emails and make decisions autonomously on behalf of the user. The key differentiator here is autonomy. This is a massive departure and leap from the chatbot era. ChatGPT revolutionised the way we find and dissect information and every new model continues to excel in this aspect. That said, agents are built to complete workflows and that brings a whole new set of use cases for enterprises and individuals. A customer service agent can handle a complaint end to end without human intervention. A coding agent can write, test, debug, and deploy. A research agent can gather, synthesise, and present findings across dozens of sources in minutes. How Fast Is This Market Growing? The numbers show that the growth is nothing short of remarkable. The global AI agents market is valued at $12.06 billion in 2026, up from $8.29 billion in 2025 and is estimated to touch $53.2 billion by 2030 at a CAGR of 44.9%, according to Research and Markets . Other longer term forecasts have a higher growth rate as a report from Grand View Research places this figure to reach $182.97 billion by 2023. That’s roughly a 15x from where we stand today. Enterprise adoption of AI agents is exploding at the same time. According to McKinsey’s 2025 State of AI survey covering 1,993 participants across 105 countries, 88% of organisations now use AI in at least one function, up from 78% the prior year and a significant leap from 20% in 2017. 62% of organisations are at least experimenting with AI agents, with 23% actively scaling and an additional 39% having begun experimenting, per the same survey. Gartner forecasts that 40% of enterprise applications will embed task-specific AI agents by 2026, up from less than 5% in 2025. The use cases driving adoption are concentrated but expanding. According to a Langchain survey of over 1,300 professionals conducted as far back as late 2024, the top applications are research and summarisation at 58%, personal productivity and workflow automation at 53.5%, and customer service and ticket resolution at 45.8%. Critically, 90% of non-tech companies either use or plan to use AI agents, nearly matching the tech sector at 89%. In 2026, this is not a niche technology story. It is a broad enterprise shift. Reader Pulse: The Poll The poll, conducted via the Cryptopolitan newsletter, draws responses from a digitally engaged, investment-aware audience that actively tracks developments across crypto, AI, and emerging technology. These are readers who consume this space daily, making the results a sharper and more meaningful signal than a broad consumer survey would produce. The fact that non-usage still leads in this cohort is precisely what makes the data worth examining. No (38.46%): The leading response, and the most revealing one. Nearly four in ten respondents from an audience that actively follows AI and crypto developments have never used an AI agent. This is not a general population survey. The fact that non-usage leads among this cohort underscores a critical point: awareness of AI does not automatically translate to hands-on engagement with its most powerful applications. Yes (~36.75%): Roughly one in three have used an AI agent, a figure that broadly tracks with enterprise-level adoption data. The Langchain report cited above found 51% of over 1,300 surveyed professionals rely on AI agents, while broader surveys suggest around 35% of organisations report widespread usage. Our poll’s “yes” cohort sits within that range, suggesting the Cryptopolitan audience reflects the wider digitally engaged professional population reasonably well. What’s an AI agent? (~24.79%): Nearly one in four respondents did not know what an AI agent is. This is arguably the most important data point in the poll. In an audience that reads about AI daily, a 25% awareness gap signals that the technology, despite its rapid growth in enterprise deployment, has not yet crossed into mainstream comprehension. The terminology itself remains a barrier. The gap between market growth statistics and actual user understanding is wider than the headline numbers suggest. Combined, the non-users and unaware respondents represent over 63% of the poll. In an audience predisposed to emerging technology, this signals that AI agents are still in the early adopter phase for individual users, even as enterprise deployment accelerates rapidly. The Barriers Holding Back Broader Adoption Enterprise momentum is real, but so are the constraints. Over 40% of agentic AI projects are projected to be cancelled by the end of 2027, with escalating costs, unclear business value, and inadequate risk controls as primary drivers, according to Gartner . According to a UiPath 2025 Agentic AI Research Report , based on a survey of 252 U.S. IT executives at companies with over $1 billion in revenue, IT security tops the list of concerns around adopting agentic AI at 56%, followed by cost of implementation at 37% and integration with existing systems at 35%. When asked what would be most critical to effective implementation, executives ranked ensuring safety and privacy as the single most important factor, ahead of regulatory compliance and system integration For individual users, the barrier is simpler: the tools are not yet intuitive enough to onboard people who do not already know what they are looking for. The “What’s an AI agent?” response in our poll is, in part, a product design and communication problem as much as it is an awareness problem. What the Numbers Mean for 2026 2026 has been widely described as the year AI agents move from experiment to infrastructure. According to Salesforce’s annual CIO study , based on a survey of 200 CIOs across 24 countries, full AI implementation jumped from 11% in 2024 to 42% in 2025, a 282% increase year on year, with CIOs now dedicating 30% of their AI budgets specifically to agentic AI, The enterprise side of the equation is moving quickly. The individual side is where the story becomes more nuanced, and where our poll adds something the market size reports do not. The roughly 37% of our audience who have used an AI agent are ahead of the curve. The 38% who have not, and the 25% who have not yet encountered the concept, represent the next wave of potential users. Whether that wave arrives in months or years depends on how quickly the technology becomes accessible, intuitive, and visible in everyday digital life. If you're reading this, you’re already ahead. Stay there with our newsletter .

4 May 2026, 14:14

Orbs Launches SPOT, a DeFi Trading Interface Built for AI Agents

Orbs has introduced a new decentralized finance (DeFi) trading interface designed not for human users, but for autonomous AI agents. The system, called SPOT (Spot Advanced Swap Orders), marks a departure from conventional crypto trading platforms by offering a machine-readable interface that enables algorithmic agents to execute complex on-chain trades without human intervention. The launch comes at a time when AI-driven systems are increasingly being deployed in financial markets, including crypto, where automation and algorithmic strategies have long played a role. However, most DeFi infrastructure remains tailored to human interaction, relying on dashboards, user interfaces, and APIs that require translation layers for machine use. SPOT aims to eliminate that gap by providing an interface that AI agents can interpret and act on directly. At its core, SPOT allows AI agents to execute a wide range of trading strategies across Ethereum Virtual Machine-compatible blockchains. These include market orders, limit orders, time-weighted average price (TWAP) execution, stop-loss and take-profit triggers, and delayed-start swaps. The system supports more than 25 decentralized exchange integrations, giving agents access to deep liquidity across multiple ecosystems. One of the defining features of SPOT is its non-custodial design. Agents can execute trades directly from wallets without relinquishing control of funds, a key requirement in decentralized finance. Additionally, the interface supports gasless execution for certain transactions, reducing friction for high-frequency or automated strategies that would otherwise incur significant transaction costs. Unlike traditional DeFi platforms, SPOT does not rely on a graphical user interface. Instead, it is composed of structured markdown files that function as the interface itself. These files include a central SKILL.md entry point, along with supporting documentation such as quickstart guides, parameter definitions, lifecycle instructions, and token address references. This documentation-first approach allows AI agents and large language models to parse instructions, understand trade parameters, and execute transactions without the need for APIs or middleware. The files are hosted on GitHub and accessible through tools such as npm and MCP, making them immediately usable within agentic frameworks and autonomous systems. According to Orbs, this design reflects a broader shift in how software interfaces are being built. As AI agents become more capable of managing wallets and interacting with blockchain protocols, the need for machine-readable infrastructure is increasing. SPOT is positioned as a response to that demand, offering a standardized way for agents to access and execute DeFi strategies. The system is powered by Orbs’ Layer-3 trading infrastructure, which operates as an additional execution layer on top of existing blockchains. This infrastructure includes protocols such as dLIMIT, dTWAP, Liquidity Hub, Perpetual Hub, and dSLTP, all of which are already live in production. Collectively, these protocols have processed more than $3 billion in cumulative trading volume and generated over $3 million in protocol revenue. The network is secured by more than 1 billion staked ORBS tokens, providing economic backing for its operations. By building SPOT on top of this existing infrastructure, Orbs is positioning the interface as a production-ready solution rather than an experimental tool. Trades executed through SPOT are validated by a cosigned oracle, which independently verifies execution parameters before transactions are signed and broadcast on-chain. This adds an additional layer of verification to ensure that trades are executed as intended. The timing of the launch aligns with a growing trend in crypto markets: the rise of AI-driven trading systems. Advances in large language models and autonomous agents have made it increasingly feasible for software to manage portfolios, execute trades, and respond to market conditions in real time. This shift is creating new requirements for infrastructure. Traditional APIs and user interfaces are not always optimized for machine interaction, leading developers to build custom integrations or rely on intermediary layers. SPOT seeks to simplify this process by providing a native interface that agents can use without modification. In addition to its technical capabilities, SPOT is designed to be open and permissionless. Developers and agent frameworks can access the interface without registration or API keys, lowering barriers to entry for experimentation and integration. The documentation is publicly available, allowing anyone to build on top of the system. The interface has also been listed in several directories focused on AI-compatible tools, including ClawHub, Awesome MCP Servers, the Anthropic MCP Registry, and LobeHub. These listings are intended to increase visibility among developers building agent-based systems and to facilitate integration into broader ecosystems. For Orbs, the launch of SPOT represents an extension of its broader strategy as a Layer-3 infrastructure provider. The protocol focuses on enabling advanced trading functionality that goes beyond what is typically possible with standard smart contracts. By offloading complex logic to a supplementary execution layer, Orbs aims to bring more sophisticated trading capabilities to decentralized markets. The introduction of a machine-native interface is a natural progression of that approach. As the role of AI agents in financial markets continues to expand, the tools they use will need to evolve accordingly. SPOT is an early attempt to define what those tools might look like. While it remains to be seen how quickly AI-driven trading will be adopted in DeFi, the underlying trend is clear. Automation is becoming more prevalent, and the systems that support it are becoming more specialized. Interfaces like SPOT could play a key role in shaping how autonomous agents interact with blockchain-based financial systems in the years ahead. The post Orbs Launches SPOT, a DeFi Trading Interface Built for AI Agents appeared first on Finbold .

4 May 2026, 12:20

Anthropic advances $1.5 billion venture to sell AI tools to private-equity companies

Anthropic is close to forming a $1.5 billion AI venture with Blackstone (BX), Goldman Sachs (GS), Hellman & Friedman, General Atlantic, and other Wall Street companies. The planned company will sell artificial intelligence tools to businesses owned by private-equity funds. That means the first customers will likely be companies already sitting inside buyout portfolios, where owners are always hunting for lower costs, faster work, better software, tighter cyber checks, and cleaner financial reporting. The announcement could come as soon as Monday. Anthropic , Blackstone, and Hellman & Friedman are each expected to put in about $300 million. Goldman Sachs is expected to invest around $150 million as a founding backer. General Atlantic and other investors are also part of the plan. The total backing is expected to reach about $1.5 billion. Wall Street companies back Anthropic as private-equity companies look for AI tools that cut real operating costs The new business is being designed as a consulting-style arm for Anthropic. Its job will be to help companies add AI into their daily work. That can include customer service, legal review, finance, coding, cybersecurity, research, document handling, and internal data searches. A private-equity company can test the tools in one company, then push the same playbook across other holdings if the numbers work. That gives Anthropic a way to reach many businesses through a smaller group of investors and owners. The deal also puts Anthropic deeper into the enterprise AI race . OpenAI, Google parent Alphabet (GOOGL), Microsoft (MSFT), Amazon (AMZN), and Nvidia (NVDA) are all fighting for the same corporate budget. Most companies are past the cute demo stage now. They want AI that can save money, protect systems, help workers find answers faster, and avoid creating a compliance disaster. For private-equity companies, higher financing costs have made margin gains more important. That is the part Wall Street cares about. Nobody is writing a $1.5 billion check for vibes. Defense officials still treat Anthropic as a risk while Mythos pulls interest from national security agencies Meanwhile, Department of Defense CTO Emil Michael said on Friday that Anthropic remains a supply chain risk. At the same time, Emil separated that dispute from Mythos, the company’s cyber-focused AI model. He told reporters that the Mythos matter is being handled across the government, not only inside the Department of War. Emil said the model has special ability to find cyber weaknesses and help patch them, so government networks need stronger protection. The dispute started after the DOD and Anthropic failed to agree on how the agency could use Anthropic’s models. The Pentagon then placed the company under a supply chain risk label as a danger to U.S. national security. Anthropic sued the Trump administration in March to fight the Pentagon’s blacklist. The cases are still active in San Francisco and Washington, D.C. One hard question remains open: how can the DOD use Mythos while the broader Anthropic risk label still exists? Emil said the Pentagon still wants guardrails. He also said those terms can be negotiated because each AI company has its own view. After a meeting on the matter, President Donald Trump told CNBC that a deal between Anthropic and the DOD is possible. Trump also said the company is “very smart” and could “be of great use.” Even with the risk label, the DOD has used Anthropic models to support military work tied to the war in Iran. The National Security Agency, which sits under the DOD, has reportedly used Mythos, Axios reported. Emil said national security reviews must look at frontier AI models, including Chinese systems. He said the NSA and Commerce Department test models to see what they can do at the edge. Also on Friday, the DOD announced agreements with seven AI companies to place their tools on classified networks for “lawful operational use.” The list includes Google, OpenAI, Nvidia, Microsoft, Amazon Web Services, SpaceX, which merged with Elon Musk’s xAI, and Reflection, a startup building open-weight models. The smartest crypto minds already read our newsletter. Want in? Join them .

4 May 2026, 11:17

Cerebras launches IPO roadshow, targeting $115-$125 per share

Cerebras Systems will start pitching its stock to investors on Monday, with plans to sell shares at somewhere between $115 and $125 each, according to someone with knowledge of the plans who spoke to Reuters. The artificial intelligence chip maker is trying to go public for the second time. The company pulled its first attempt in October last year. Cerebras reported stronger financial results for the year that ended December 31. The company brought in $510 million in revenue, a jump from $290.3 million the year before. It also made a profit of $1.38 for each share, compared to losing $9.90 per share in the previous year. Morgan Stanley, Citigroup, Barclays and UBS are handling the stock sale. Industry is taking a shift Cerebras’ strategy is not random. The AI industry is taking a shift from the development of new AI models to running them for actual use. This shift is a golden chance for small companies competing with Nvidia’s (NASDAQ: NVDA) monopoly. As reported by Cryptopolitan, even OpenAI isn’t convinced by Nvidia’s inference hardware. This is because running AI models, known as inference, requires different capabilities than training them. This creates openings for specialized chip makers to find their spot in the market. Processing large batches of information needs a different balance of computing power, memory and data transfer speeds than running an AI chatbot or coding assistant. This variety in requirements has made the inference market more diverse. Some tasks work better on traditional graphics chips, while others need more advanced equipment. Nvidia’s purchase of Groq last December for $20 billion shows how this is playing out. Groq built chips packed with fast SRAM memory that could process AI responses faster than standard graphics chips. But the company struggled to scale up because its chips had limited computing power and were built on older technology. Nvidia solved this problem by splitting the work. It uses its regular graphics chips for the heavy computing part of generating AI responses, called prefill, while using Groq’s chips for the faster decode step that requires less computing but needs quick data access. Other big companies are doing something similar. Amazon Web Services announced its own split system shortly after a major tech conference. It combines its custom Trainium chips for prefill work with Cerebras’ wafer-sized chips for decode operations. Intel joined in too, revealing plans to pair graphics chips with processors from another startup called SambaNova. The graphics chips will handle prefill while SambaNova’s chips tackle decode. Most of the smaller chip companies have found success with decode work. SRAM memory doesn’t hold much information, but it’s extremely fast. With enough chips, or one very large chip like Cerebras makes, these systems excel at decode tasks. But companies aren’t stopping there. New technologies challenge split-chip approach Lumai, another startup, announced this week it built a chip that uses light instead of electricity for the math operations at the core of AI work. This approach uses much less power than traditional chips. The company expects its upcoming Iris Tetra systems to deliver an exaOPS of AI performance while using just 10 kilowatts of power by 2029. The chips mix light-based and electrical components, but light handles most of the work during inference. Lumai plans to use these chips first as standalone replacements for graphics chips in batch processing jobs. Later, the company wants to use them for prefill work too. Not everyone thinks splitting the work between different chips makes sense. Tenstorrent rolled out its Galaxy Blackhole systems this week, and CEO Jim Keller criticized the approach. “Every company in the industry is pairing up to build the accelerator accelerator accelerator. CPUs run code. GPUs accelerate CPUs. TPUs accelerate GPUs. LPUs accelerate TPUs. And so on. This leads to complex solutions which are unlikely to be compatible with changes in AI models and uses. At Tenstorrent, we thought something more general and simpler would work,” Keller said. The smartest crypto minds already read our newsletter. Want in? Join them .

3 May 2026, 21:55

National BTC Adoption Validates Bitcoin’s Core Ethos, Not a Betrayal: Adam Back

BitcoinWorld National BTC Adoption Validates Bitcoin’s Core Ethos, Not a Betrayal: Adam Back Blockstream CEO Adam Back has made a compelling argument that national BTC adoption does not represent a betrayal of Bitcoin’s original ethos. Instead, he views it as a clear sign of the cryptocurrency’s success. In an exclusive interview with Cointelegraph, Back explained that government interest in Bitcoin is a natural progression for any transformative technology. Understanding National BTC Adoption and Its Alignment with Bitcoin’s Ethos Back’s central thesis is straightforward: technologies that shift power dynamics often start with individuals before reaching higher-level entities. He cited the internet and cryptography as prime examples. These innovations first spread among early adopters and hobbyists. Over time, governments and large institutions recognized their value and began to integrate them. This pattern, Back argues, is now playing out with Bitcoin. The Bitcoin ethos has always emphasized decentralization and individual sovereignty. However, Back believes that state-level interest does not contradict these principles. It merely signals that the technology has matured enough to attract serious attention from powerful actors. The Historical Precedent: How Transformative Technologies Evolve To understand Back’s perspective, it helps to look at historical parallels. The internet was initially a tool for academics and researchers. It later became a platform for commerce, communication, and government services. Similarly, cryptography was once the domain of spies and mathematicians. Today, it secures everything from banking to national defense. Bitcoin follows a similar trajectory. Its early adopters were cypherpunks and libertarians. They valued its ability to bypass traditional financial systems. Now, nations like El Salvador and the Central African Republic have adopted it as legal tender. Other countries are exploring strategic Bitcoin reserves. This evolution, Back suggests, is a testament to Bitcoin’s robustness and utility. Why Government Interest Strengthens the Bitcoin Ethos Critics often argue that government adoption dilutes Bitcoin’s anti-establishment roots. Back disagrees. He contends that the very act of a government adopting Bitcoin proves its power as a neutral, decentralized asset. It cannot be controlled or manipulated by any single entity, including the state. This argument resonates with many in the crypto community. It reframes the narrative from one of co-option to one of validation. When a nation-state chooses to hold Bitcoin, it acknowledges the asset’s unique properties: scarcity, immutability, and borderless transferability. These are the same features that attracted early adopters. Real-World Impacts of National BTC Adoption The implications of this shift are profound. Countries that adopt Bitcoin can potentially reduce their dependence on the US dollar. They can offer citizens a hedge against inflation. They can also attract investment from the global crypto ecosystem. For example, El Salvador’s adoption has spurred tourism and remittance flows. It has also created a new market for Bitcoin-backed bonds. While challenges remain, the experiment has provided valuable data. It shows that national-level adoption is feasible, even for smaller economies. Expert Analysis: What This Means for the Future Industry experts largely agree with Back’s assessment. Many see government adoption as a necessary step for Bitcoin’s long-term survival. It brings regulatory clarity, institutional investment, and mainstream acceptance. These factors can stabilize the market and reduce volatility. However, there are also risks. Heavy-handed regulation could stifle innovation. Governments might use Bitcoin to surveil transactions, undermining privacy. Back acknowledges these concerns but remains optimistic. He believes the core technology is resilient enough to withstand such pressures. Timeline of Key Events in National BTC Adoption To provide context, here is a brief timeline of major milestones: 2021: El Salvador becomes the first country to adopt Bitcoin as legal tender. 2022: The Central African Republic follows suit. 2023: Several US states introduce bills to create strategic Bitcoin reserves. 2024: Discussions begin in Switzerland and Singapore about national Bitcoin holdings. 2025: Adam Back’s interview reinforces the narrative of adoption as success. Comparing Individual vs. National Adoption Aspect Individual Adoption National Adoption Motivation Financial freedom, privacy Economic strategy, hedge Scale Small, personal Large, systemic Risk Profile High, self-managed Moderate, state-backed Impact on Ethos Pure, ideological Pragmatic, evolutionary Addressing Criticisms of Government Bitcoin Adoption Not everyone agrees with Back’s viewpoint. Some purists argue that any government involvement corrupts Bitcoin’s decentralized nature. They point to potential surveillance risks and regulatory overreach. Others worry that large state holdings could concentrate power, contradicting the goal of democratizing finance. Back addresses these concerns directly. He notes that Bitcoin’s code is open and transparent. No government can change its fundamental properties. Moreover, state adoption creates a powerful incentive to protect the network. Governments that hold Bitcoin have a vested interest in its security and stability. Data-Backed Reasoning: Adoption Trends Recent data supports Back’s optimism. According to Chainalysis, global crypto adoption has grown steadily, even during bear markets. Institutional investors now account for a significant portion of trading volume. Central banks are exploring digital currencies, often built on blockchain technology. These trends suggest that Bitcoin is moving from the fringes to the mainstream. National adoption is a logical next step. It does not represent a betrayal of the original vision. Instead, it shows that the vision is succeeding on a larger scale than anyone anticipated. Conclusion Adam Back’s interview provides a timely and thoughtful perspective on national BTC adoption . He argues convincingly that government interest validates Bitcoin’s core ethos rather than undermining it. The technology’s journey from individual enthusiasts to national treasuries mirrors the path of other transformative innovations. As more countries explore Bitcoin, the narrative of success will likely continue to strengthen. For investors, policymakers, and enthusiasts, this evolution offers both opportunities and challenges. The key is to embrace the change while preserving the principles that make Bitcoin unique. FAQs Q1: Does national BTC adoption contradict Bitcoin’s original ethos? A1: According to Adam Back, no. He argues that government adoption is a natural sign of success, not a betrayal. It reflects the technology’s maturation and utility. Q2: What examples exist of national BTC adoption? A2: El Salvador and the Central African Republic have adopted Bitcoin as legal tender. Other countries are exploring strategic reserves or regulatory frameworks. Q3: How does government adoption benefit Bitcoin? A3: It brings regulatory clarity, institutional investment, and mainstream acceptance. It also creates incentives for network security and stability. Q4: What are the risks of national BTC adoption? A4: Risks include potential surveillance, heavy-handed regulation, and concentration of power. However, Bitcoin’s open code and decentralized nature mitigate these concerns. Q5: Why does Adam Back believe adoption is a sign of success? A5: He draws parallels to the internet and cryptography, which also started with individuals before being adopted by governments. This pattern shows the technology’s enduring value. This post National BTC Adoption Validates Bitcoin’s Core Ethos, Not a Betrayal: Adam Back first appeared on BitcoinWorld .

3 May 2026, 18:50

Chinese AI stocks to draw $1.75B while banning what Silicon Valley does best

A wave of investment is heading toward Chinese technology companies, at the same time courts in the country have ruled that businesses cannot fire employees simply to replace them with automated systems. The timing raises questions about whether protecting jobs might actually strengthen rather than weaken artificial intelligence development. Wall Street bank Morgan Stanley expects between $1.25 billion and $1.75 billion to move into Hong Kong’s technology stock index when two artificial intelligence firms join the benchmark on June 8. The forecast, shared with Cryptopolitan, comes even as the Hang Seng Tech Index has fallen more than 11 percent since January started. Knowledge Atlas Technology, which operates under the name Zhipu AI, and MiniMax both began trading in Hong Kong this past January. Share prices for both companies have climbed sharply. Morgan Stanley analysts bumped up their target price for Knowledge Atlas to 990 Hong Kong dollars from 560 dollars. MiniMax saw its target rise to 1,100 Hong Kong dollars from 990 dollars. The two companies represent the first major Chinese businesses focused on AI models to sell shares publicly. Rivals like Moonshot, which runs the Kimi AI model, and StepFun have stayed private. Zhipu stands out for models that handle coding tasks well. MiniMax has built a reputation for offering a wide range of capabilities, from creating text to generating audio. Many people using OpenClaw AI agent tools have picked MiniMax partly because Chinese AI models typically cost less than American alternatives. That price gap is shrinking, though. In the first three months of this year, accessing Chinese AI models cost at least 17 percent of what American models charged. A year earlier, the figure stood at just 5 percent. Morgan Stanley analysts think the leading Chinese AI model makers will each bring in at least $1 billion in revenue this year, with that amount more than doubling in the following year. The bank’s analysts wrote that AI and large language model companies will become much bigger forces in Hong Kong stock markets, changing how the index looks, performs, and attracts money. They noted strong backing from regulators, pointing out that technology firms accounted for 40 percent of money raised through Hong Kong initial public offerings so far this year and 43 percent of deals in the pipeline. Tencent and Alibaba, the two biggest stocks by market value in the Hang Seng Tech Index, have both dropped by double-digit percentages this year. Morgan Stanley picked Alibaba as its top choice among Chinese internet stocks, viewing the e-commerce company as an AI investment opportunity across cloud computing and AI models. Courts ban firing workers to make room for automation Meanwhile, a Chinese court made a ruling last month that could reshape how companies there use automation. The Hangzhou Intermediate People’s Court decided that businesses cannot legally fire workers just to replace them with AI systems. The case involved a worker told to accept a lower position because his job had been automated. He refused the demotion and was fired. The court said the company broke the law. The ruling stated that employers are prohibited from shifting operating costs to employees. A longer section explained that AI technology can improve how businesses run, free up workers, and make conditions better for employees. Companies can adjust to new technology trends, the court said, but they must consider workers’ legitimate rights and cannot use technological changes as an excuse to cut pay or end contracts on their own. After Nigeria and India, China ranks third globally in trust toward AI, according to survey data. Multiple surveys have found similar patterns. The contrast with America is stark. Americans say they dislike the economy despite strong job numbers and stock markets. They also express negative views about AI and the executives running AI companies. As reported by Cryptopolitan previously, Polymarket is also on rising tech layoffs. Micro drama boom shows AI creating new content markets In entertainment, China has created a new format called micro dramas, short episodes lasting one or two minutes, designed for vertical phone screens. The format took off during the pandemic and reached an estimated 660 million viewers in China during 2024. The shows are spreading to other countries quickly. A South Korean production company called Vigloo now spends roughly 30 percent of its budget on AI tools. The company can finish a show in one month instead of three and at one-fifth the usual cost. But Vigloo’s CEO, Neil Choi, said competition from China’s micro drama industry keeps intensifying as the country backs AI-driven content production. There’s a middle ground between leaving money in the bank and rolling the dice in crypto. Start with this free video on decentralized finance .