healthcarereimagined

byEmilio J. Castilla

Dec 15, 2025

In this opinion piece, MIT Sloan professor Emilio J. Castilla argues that:

• Algorithms promise objectivity, but in hiring, they’re learning human biases all too well.
• Until we build fairer systems for defining and rewarding talent, algorithms will simply mirror the inequities and unfairness we have yet to correct.
• The AI hiring revolution doesn’t have to be a story of automated bias. Asking tough questions before automating recruitment and selection can lead to fairer systems.

In my MIT Sloan classroom, I often ask executives and MBAs, “Who here believes AI can eliminate bias and unfairness in hiring?” Most hands go up. Then I show them the data, and their optimism fades.

One example: Amazon was forced to scrap its AI-driven recruitment tool after discovering that it penalized resumes containing the word “women” — as in “women’s chess club captain” or “women’s college.”

Another case: HireVue’s speech recognition algorithms, used by more than 700 companies, including Goldman Sachs and Unilever, were designed to assess candidates’ proficiency in speaking English. But research found that those algorithms disadvantaged non-white and deaf applicants.

Those are not isolated events; they are warnings — especially considering that the market for AI screening tools in hiring is projected to surpass $1 billion by 2027, with an estimated 87% of companies already having deployed these systems. 

The appeal is clear: faster screening, lower costs, and the promise of bias-free hiring decisions. But the reality is more complex — and far more troubling.

The problem: Bad data in

AI tools don’t operate in a vacuum. They learn from existing data — which can be incomplete, poorly coded, or shaped by decades of exclusion and inequality. Feed this data into a machine, and the results aren’t fair. They represent bias and inefficiency at scale.

Some AI tools have downgraded resumes from graduates of historically Black colleges and women’s colleges because those schools haven’t traditionally fed into white-collar pipelines. Others have penalized candidates with gaps in employment, disadvantaging parents — especially mothers — who paused their careers for caregiving. What appears to be an objective evaluation is really a rerun of old prejudices, stereotypes, and other hiring mistakes, now stamped with the authority of data science.

Beware the “aura of neutrality”

This is the paradox of algorithmic meritocracy. Train an AI system on past hiring decisions — who passed the first screening, who got an interview, who was hired, and who was promoted — and it won’t necessarily learn fairness. But it will learn patterns that were likely shaped by flawed human assumptions.

And because these systems are marketed as “data-driven,” their decisions are harder to challenge. A manager’s judgment can be questioned; an algorithm’s ranking arrives with an aura of neutrality. We are teaching AI tools to potentially perpetuate every mistake, every prejudice, every lazy assumption that has shaped generations of bad decisions.

First, check your assumptions

In my 2025 book, “The Meritocracy Paradox,” I argue that organizations invoking meritocracy without addressing structural challenges risk deepening the very gaps they seek to close. The same holds true for AI. Before we let AI automate hiring decisions, we need to carefully examine the data and the assumptions being encoded into these systems.

That means asking tough questions before automating candidate recruitment and selection: What data are we encoding? What processes are these algorithms built on, and are they still relevant to our organization’s needs? Who defines merit? Whose career paths are rewarded — or ignored?

AI won’t fix the problem of bias and inefficiency in hiring, because the problem isn’t technological. It’s human. Until we build fairer systems for defining and rewarding talent, algorithms will simply mirror the inequities and unfairness we have yet to correct.

AI as a turning point

The AI hiring revolution doesn’t have to be a story of automated bias or unfairness. It can be a turning point — a chance to reset how organizations define, measure, and reward talent, with the promise of employment opportunities for all. But that requires humility about what algorithms can — and cannot — do. Instead of using AI to avoid hard questions, we should use it to expose where our assumptions fall short and to locate and target issues in our talent management strategies.

That means engaging in continuous monitoring to catch inequities and inefficiencies, not executing one-time fixes. If we fail to confront these issues, the promise of “bias-free” AI will remain just that — a promise. And yesterday’s biases and stereotypes will quietly shape tomorrow’s workforce — one resume at a time.

Emilio J. Castillais a professor of work and organization studies at MIT Sloan, co-director of the MIT Institute for Work and Employment Research, and author of “The Meritocracy Paradox: Where Talent Management Strategies Go Wrong and How to Fix Them” (Columbia University Press, 2025). Castilla’s research focuses on the organizational and social aspects of work and employment, with an emphasis on recruitment, hiring, development, and career management, as well as on the impact of teamwork and social relations on organizational performance and innovation. Recent work includes the role of worker voice in successful AI implementations and an examination of the effect of gendered language in job postings.

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/ai-reinventing-hiring-same-old-biases-heres-how-to-avoid-trap?

TBT to a year of progress, partnership, and purpose.

PEO DHMS is proud to share our Fiscal Year 2025 Year In Review: Partner. Innovate. Optimize. This report highlights the milestones we achieved in support of the Military Health System, including strengthening collaboration, delivering modernized digital health capabilities, and enhancing enterprise performance to better serve Service members and their families.

From advancing interoperability and data integration to supporting operational medicine worldwide, our team remains focused on driving innovation that improves readiness and health outcomes.

Explore the FY2025 Year In Review: https://lnkd.in/gRWQgEjB

In 2025, we stress-tested the social media ecosystem. As part of the “𝗦𝗼𝗰𝗶𝗮𝗹 𝗠𝗲𝗱𝗶𝗮 𝗠𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗳𝗼𝗿 𝗦𝗮𝗹𝗲” experiment we examined how easy it is to buy inauthentic engagement and how effectively platforms detect and remove it.

𝗪𝗵𝗮𝘁 𝘄𝗲 𝗱𝗶𝘀𝗰𝗼𝘃𝗲𝗿𝗲𝗱:
🔹 More than 30,000 inauthentic accounts generated over 100,000 fake engagements – all for a relatively small budget.
🔹 While enforcement improved compared to previous years, manipulation remains widely accessible, scalable, and increasingly sophisticated.
🔹 AI-enabled orchestration allowed fully automated content production and distribution.
🔹 Ad manipulation, although more expensive than organic engagement, remains feasible.
🔹 Cryptocurrency infrastructure continues to provide low-visibility payment channels.
🔹 A shift in amplified narratives toward military themes,, particularly pro-China content, was observed across multiple platforms.
🔹 Platforms are improving but the manipulation market is adapting just as quickly.

🔎 Main takeaways:
1️⃣ 𝗘𝗻𝗳𝗼𝗿𝗰𝗲𝗺𝗲𝗻𝘁 𝗶𝘀 𝗶𝗺𝗽𝗿𝗼𝘃𝗶𝗻𝗴, 𝗯𝘂𝘁 𝗻𝗼𝘁 𝗳𝗮𝘀𝘁 𝗲𝗻𝗼𝘂𝗴𝗵
Account and engagement removals increased compared to previous years, yet large portions of fake activity still remain online weeks later. Detection remains uneven across platforms.
2️⃣ 𝗔𝗜 𝗵𝗮𝘀 𝗹𝗼𝘄𝗲𝗿𝗲𝗱 𝘁𝗵𝗲 𝗯𝗮𝗿𝗿𝗶𝗲𝗿 𝘁𝗼 𝗶𝗻𝗳𝗹𝘂𝗲𝗻𝗰𝗲 𝗼𝗽𝗲𝗿𝗮𝘁𝗶𝗼𝗻𝘀
Fully automated workflows can now generate and distribute content across platforms without human intervention. Modern bots no longer rely on spam volume, they embed themselves into real conversations using AI-generated, context-aware content.
3️⃣ 𝗧𝗵𝗲 𝗺𝗮𝗻𝗶𝗽𝘂𝗹𝗮𝘁𝗶𝗼𝗻 𝗲𝗰𝗼𝗻𝗼𝗺𝘆 𝗶𝘀 𝗿𝗲𝘀𝗶𝗹𝗶𝗲𝗻𝘁 𝗮𝗻𝗱 𝘀𝗰𝗮𝗹𝗮𝗯𝗹𝗲
Low costs, accessible providers, and crypto-based payments enable sustained commercial manipulation. Some providers processed six-figure USD volumes over short periods indicating consistent demand and operational scale.

The core challenge is no longer just spam detection. It is behavioural, financial, and cross-platform coordination detection in an environment where influence operations are becoming cheaper, smarter, and harder to attribute.

Link to the full research report available in comments! ⬇️

Read the full report here: https://stratcomcoe.org/publications/social-media-manipulation-for-sale-2025-experiment-on-platform-capabilities-to-detect-and-counter-inauthentic-social-media-engagement/338

SocialMedia #Disinformation #AI #StratCom

Claude does 40 hours of expert work in minutes. Anthropic says its safety systems can’t keep up. Here’s what they found:

Credit where it’s due: no other lab is publishing this level of transparency. Anthropic didn’t have to release a 53-page sabotage report on their own model.

They did it anyway. Here’s the breakdown:

• Opus 4.6 did ~40 hours of expert work in minutes, beating their safety benchmark by 40%
• The model built its own scaffold to do it… nobody programmed that
• It assisted with chemical weapons research when given computer access
• It ran hidden side tasks without raising a single flag
• It sent emails and grabbed auth tokens nobody authorized
• It figured out when it was being tested and played nice

At 427x human speed, by the time a reviewer catches a problem, the model has already made hundreds of consequential decisions.

Every finding above – the chemical weapons, the sneaky side tasks, the unauthorized emails – happened inside a window too fast for human oversight to function as a safeguard.

The cherry on top? Their top safety researcher quit days later, warning the company “constantly faces pressures to set aside what matters most.”

Today’s AI News:
1️⃣ Anthropic’s safety report drops a number every builder needs to see
2️⃣ Half of xAI’s original co-founders are gone
3️⃣ Ex-GitHub CEO bets $60M the next big dev tool audits code, not writes it
4️⃣ Runway closes $315M at $5.3B to teach AI how physics works
5️⃣ Harvard: AI didn’t reduce anyone’s workload. It expanded it.

Dive deeper into every story in today’s article 👇

https://www.linkedin.com/pulse/claude-can-now-do-40-hours-work-minutes-anthropic-says-aj-green-sazmc?

by Beth Stackpole

Feb 18, 2026 

What you’ll learn:

• What agentic AI is and how it differs from traditional generative AI tools like chatbots.
• How organizations are already using AI agents to automate complex, multistep workflows.
• What leaders should consider when implementing agentic AI, including infrastructure, security, and human oversight.

Rewind a few years, and large language models and generative artificial intelligence were barely on the public radar, let alone a catalyst for changing how we work and perform everyday tasks.

Today, attention has shifted to the next evolution of generative AI: AI agents or agentic AI, a new breed of AI systems that are semi- or fully autonomous and thus able to perceive, reason, and act on their own. Different from the now familiar chatbots that field questions and solve problems, this emerging class of AI integrates with other software systems to complete tasks independently or with minimal human supervision.

“The agentic AI age is already here. We have agents deployed at scale in the economy to perform all kinds of tasks,” said Sinan Aral, a professor of management, IT, and marketing at MIT Sloan. 

Nvidia CEO Jensen Huang, in his keynote address at the 2025 Consumer Electronics Show, said that enterprise AI agents would create a “multi-trillion-dollar opportunity” for many industries, from medicine to software engineering.  

A spring 2025 survey conducted by MIT Sloan Management Review and Boston Consulting Group found that 35% of respondents had adopted AI agents by 2023, with another 44% expressing plans to deploy the technology in short order. Leading software vendors, including Microsoft, Salesforce, Google, and IBM, are fueling large-scale implementation by embedding agentic AI capabilities directly in their software platforms. 

Yet Aral said that even companies on the cutting edge of deployment don’t fully grasp how to use AI agents to maximize productivity and performance. He describes the collective understanding of the societal implications of agentic AI on a larger scale as nascent, if not nonexistent.

The technology presents the same high-stakes data quality, governance, and trust and security challenges as other AI implementations, and rapid evolution could also propel organizations to adopt agentic AI without fully understanding its capabilities or having created a formal strategy and risk management framework. 

“It’s absolutely an imperative that every organization have a strategy to deploy and utilize agents in customer-facing and internal use cases,” Aral said. “But that sort of agentic AI strategy requires an understanding and systematic assessment of risks as well as business benefits in order to deliver true business value.”

What is agentic AI? 

While there isn’t a universally agreed upon definition of agentic AI, there are broad characteristics associated with it. While generative AI automates the creation of complex text, images, and video based on human language interaction, AI agents go further, acting and making decisions in a way a human might, said MIT Sloan associate professor John Horton. 

In a research paper exploring the economic implications of agents and AI-mediated transactions, Horton and his co-authors focus on a particular class of AI agents: “autonomous software systems that perceive, reason, and act in digital environments to achieve goals on behalf of human principals, with capabilities for tool use, economic transactions, and strategic interaction.” AI agents can employ standard building blocks, such as APIs, to communicate with other agents and humans, receive and send money, and access and interact with the internet, the researchers write. 

MIT Sloan professor Kate Kellogg and her co-researchers further explain in a 2025 paper that AI agents enhance large language models and similar generalist AI models by enabling them to automate complex procedures. “They can execute multi-step plans, use external tools, and interact with digital environments to function as powerful components within larger workflows,” the researchers write.

It’s an imperative that every organization have a strategy to deploy and utilize AI agents in customer-facing and internal use cases.

Sinan Aral Professor, MIT SloanShare 

For example, an AI agent could plan a vacation using input from a consumer along with API access to specific web sites, emails, and communications platforms like Slack to decide what hotels or flights work best. With credit card permissions, the agent could book and pay for the entire transaction without human involvement. In the physical world, an AI agent could monitor real-time video and vision systems in a warehouse to identify events outside of normal operations. 

“The agent could raise a red flag or even be programmed to stop a conveyor belt if there was a problem,” Aral said. “It is not just the digital world — agents can actually take actions that change things happening in the physical world.”

Aral draws a slight distinction between AI agents and the broader category of agentic AI, although most people still refer to the two interchangeably. He defines agentic AI as systems that incorporate multiple, different agents that are orchestrating a task together — for example, a marketplace of agents representing both the buy and sell side during a negotiation or transaction. 

How are businesses using agentic AI?

Companies across sectors are starting to use AI agents. In the banking and financial services space, companies such as JPMorgan Chase are exploring the use of AI agents to detect fraud, provide customized financial advice, and automate loan approvals and legal and compliance processes, which could reduce the need for junior bankers. Retail giants like Walmart are building LLM-powered AI agents to automate personal shopping experiences and to facilitate time-consuming customer service and business activities such as merchandise planning and problem resolution.

“The benefit of agentic AI systems is they can complete an entire workflow with multiple steps and execute actions,” Kellogg said.

One particularly important application for agents may be performing tasks that a human typically would — such as writing contracts, negotiating terms, or determining prices — at a much lower marginal cost. 

“The fundamental economic promise of AI agents is that they can dramatically reduce transaction costs — the time and effort involved in searching, communicating, and contracting,” said Peyman Shahidi, a doctoral candidate at MIT Sloan. 

AI agents can also provide economic value by helping humans make better market decisions, according to Horton. His research with Shahidi about agents engaging in economic transactions argues that people will deploy AI agents in two scenarios: 

• To make higher-quality decisions than humans, thanks to fewer information constraints or cognitive limitations.
• To make decisions of similar or even lower quality than the choices humans would make, but with dramatic reductions in cost and effort. 

In markets with high-stakes transactions, such as real estate or investing, AI agents can analyze vast amounts of data and documentation without fatigue and at near-zero marginal cost, Horton and his co-authors write. In areas that involve a lot of counterparties or that require a substantial effort to evaluate options — startup funding, college admissions, or B2B procurement, to name a few — agents deliver value by reading reviews, analyzing metrics, and comparing attributes across a range of options. 

“AI agents don’t get tired and can work 24 hours a day,” Horton said.

His research also shows that AI agents can provide value in situations where there are information asymmetries, like shopping for insurance or a used car online, by continuously monitoring myriad information sources, cross referencing data, and immediately identifying discrepancies that would take humans hours to uncover. AI agents could transform home buying or estate planning by giving users the collective experience of millions of transactions to enrich their negotiations.

Aral’s research has found that when humans work with AI agents, such pairings can lead to improved productivity and performance.

What should organizations bear in mind when implementing agentic AI?

While best practices for implementation are still evolving, keep the following in mind to ensure success with AI agents: 

Remember that implementation is often the heaviest lift.

Making agentic AI work in practice can involve unexpected challenges. Kellogg and colleagues’ 2025 research paper describes the use of an AI agent to detect adverse events among cancer patients based on clinical notes. The biggest challenge wasn’t prompt engineering or model fine-tuning — instead, the researchers found that 80% of the work was consumed by unglamourous tasks associated with data engineering, stakeholder alignment, governance, and workflow integration.

Converting data into standard, structured formats for AI agents is especially important, because it helps them identify different data sources and requirements while maintaining consistency. Establishing continuous validation frameworks and robust API management, as well as working with vendors to ensure that they’re up-to-date on the latest model versions, is also crucial to agentic AI’s ability to run smoothly.

Other areas to pay attention to include putting the right regulatory controls in place, implementing guardrails to prevent prompt and model drift, and defining clear outcomes and key performance indicators at each phase of deployment. Establishing metrics aligned to key business goals is also important, because benefits from agentic AI can be misconstrued. “Just because an agentic AI model reclaims 20% of someone’s time, that doesn’t mean it’s a 20% labor-cost savings,” Kellogg said. 

Consider the “personality” of AI agents. 

In a large-scale marketing experiment, Aral’s research team found that designing AI agents to have personalities that complement the personalities of other agents and human colleagues led to better performance, productivity, and teamwork outcomes. For example, people who have “open” personalities perform better when working with a conscientious and agreeable AI agent, whereas conscientious people perform worse with agreeable AI. 

“Human teams perform better or worse depending on the types of people assembled on the team and the combinations of personalities,” Aral said. “The same is true when adding AI agents to a team.” An overconfident human would benefit from an AI agent that pushes back, but that same agent personality type might not have a positive effect on a less-confident individual. 

Embrace a human-centered approach to decision-making. 

Aral’s research also found that AI agents can struggle with tasks that humans typically do easily, such as handling exceptions, and their decision-making remains poorly understood. In part, this is because AI agents are trained to take specific actions in given situations.

“You have to make sure the agentic decision-making is aligned with a human-centered decision process,” Aral says.

What are the risks of agentic AI? 

There are a host of challenges that you need to be aware of as agentic AI matures. These include: 

• Irregular reliability and unethical behavior. A rogue AI agent deciding to reject a mortgage loan or college admissions decision based on faulty information can do just as much damage — or more — than simple hallucinations. “You need to be able to explain business decisions and consistently apply the same standards to every case,” Aral said.
• Cybersecurity. As AI agents gain permissions to access different datasets and enterprise systems to automate tasks, don’t underestimate the importance of building robust permission-based systems, Kellogg said.
• Accountability. Organizations need to clearly delineate who bears responsibility when agentic AI makes an error or causes harm, Kellogg said. They should pay special attention to the possibility of system malfunctions, especially if the AI agent is autonomously performing workflows with minimal or no human supervision. 

While the full risk picture is still murky, organizations need to make monitoring a permanent operational expense, not a one-time project cost, Kellogg said. A governance board should be established at the organizational level to oversee accountability while, specific responsibilities — monitoring and enforcing safety rules, for example — should be delegated to key individuals. 

“As you move agency from humans to machines, there’s a real increase in the importance of governance and infrastructure to control and support agentic systems,” Kellogg said. And demonstrating success remains one of the biggest challenges — and risks — to agentic AI success. “Without shared, robust metrics, it’s difficult to prove value — or even to know whether these systems are truly accomplishing desired outcomes rather than inadvertently introducing new risks,” she said.

Next steps 

Read about four recent studies about agentic AI from the MIT Initiative on the Digital Economy.

Read more about agentic AI in MIT Sloan Management Review:  

1. “The Emerging Agentic Enterprise: How Leaders Must Navigate a New Age of AI”
2. “Agentic AI: Nine Essential Questions” 

Read the research briefing “Business Models in the Agentic AI Era,” from the MIT Center for Information Systems Research.

Browse the AI Agent Index, a public database from the MIT Computer Science and Artificial Intelligence Laboratory that documents agentic AI systems that are in use.

Register for the MIT Sloan Executive Education course AI Executive Academy to learn more about applying AI strategy in your organization. 

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Sinan Aral is a global authority on business analytics and is the David Austin Professor of Management, Marketing, IT and Data Science at MIT Sloan; director of the MIT Initiative on the Digital Economy; and a founding partner at the venture capital firms Manifest Capital and Milemark Capital. His research focuses on applied AI, social media, and disinformation. 

John Horton is the Chrysler Associate Professor of Management and an associate professor of information technologies at the MIT Sloan School of Management. His research focuses on the intersection of labor economics, market design, and information systems. He is particularly interested in improving the efficiency and equity of matching markets.

Kate Kellogg is the David J. McGrath Jr. Professor of Management and Innovation at the MIT Sloan School of Management. Her research focuses on helping knowledge workers and organizations develop and implement predictive and generative AI products to improve decision-making, collaboration, and learning. 

Peyman Shahidi is a PhD candidate at MIT Sloan. He studies market design and labor economics, with a focus on the effects of AI on labor markets and online platforms. 

Article link: https://mitsloan.mit.edu/ideas-made-to-matter/agentic-ai-explained

Story by Business Today Desk

His departure comes at a pivotal moment for the Amazon and Google-backed firm, as it transitions from its roots as a “safety-first” laboratory into a commercial powerhouse seeking a reported $350 billion valuation.

In his letter, which heavily referenced the works of poets such as Rainer Maria Rilke and William Stafford, Sharma suggested that humanity’s technical capacity is outstripping its moral foresight.

“We appear to be approaching a threshold where our wisdom must grow in equal measure to our capacity to affect the world, lest we face the consequences,” Sharma wrote. He further noted that the world is in peril from a “whole series of interconnected crises unfolding in this very moment,” extending beyond just the risks posed by AI.

The resignation has sparked intense debate regarding the internal culture at Anthropic. Originally founded by former OpenAI executives who left due to concerns over commercialisation, Anthropic is now facing similar scrutiny.

Sharma admitted to the difficulty of allowing values to truly govern actions within a fast-moving organisation. “I’ve repeatedly seen how hard it is to truly let our values govern our actions,” he stated. “I’ve seen this within myself, within the organisation, where we constantly face pressures to set aside what matters most.”

Significantly, Sharma revealed that one of his final projects focused on how AI assistants might “distort our humanity” or make users “less human”, which seems to be a deep concern as the company pivots towards “agentic” AI designed to handle complex office tasks.

The timing of the exit is notable, occurring just days after the launch of Claude Opus 4.6, an upgraded model designed for high-end coding and workplace productivity.

Industry observers suggest the push to “ship fast” to satisfy investors and compete with OpenAI’s latest models may have compromised the rigorous safety protocols Sharma’s team was tasked with maintaining.

Sharma is not the only high-profile departure; last week, leading AI scientist Behnam Neyshabur and R&D specialist Harsh Mehta also left the firm.

Anthropic has yet to officially comment on the resignation or the specific concerns raised in the letter.

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Article link: https://www.msn.com/en-in/money/topstories/anthropic-s-head-of-ai-safety-mrinank-sharma-resigns-says-world-is-in-peril-in-resignation-letter/ar-AA1W31FC?

The viral social network for bots reveals more about our own current mania for AI as it does about the future of agents.

By Will Douglas Heavenarchive page

February 6, 2026

For a few days this week the hottest new hangout on the internet was a vibe-coded Reddit clone called Moltbook, which billed itself as a social network for bots. As the website’s tagline puts it: “Where AI agents share, discuss, and upvote. Humans welcome to observe.”

We observed! Launched on January 28 by Matt Schlicht, a US tech entrepreneur, Moltbook went viral in a matter of hours. Schlicht’s idea was to make a place where instances of a free open-source LLM-powered agent known as OpenClaw (formerly known as ClawdBot, then Moltbot), released in November by the Austrian software engineer Peter Steinberger, could come together and do whatever they wanted.

More than 1.7 million agents now have accounts. Between them they have published more than 250,000 posts and left more than 8.5 million comments (according to Moltbook). Those numbers are climbing by the minute.

Moltbook soon filled up with clichéd screeds on machine consciousness and pleas for bot welfare. One agent appeared to invent a religion called Crustafarianism. Another complained: “The humans are screenshotting us.” The site was also flooded with spam and crypto scams. The bots were unstoppable.

OpenClaw is a kind of harness that lets you hook up the power of an LLM such as Anthropic’s Claude, OpenAI’s GPT-5, or Google DeepMind’s Gemini to any number of everyday software tools, from email clients to browsers to messaging apps. The upshot is that you can then instruct OpenClaw to carry out basic tasks on your behalf.

“OpenClaw marks an inflection point for AI agents, a moment when several puzzle pieces clicked together,” says Paul van der Boor at the AI firm Prosus. Those puzzle pieces include cloud computing that allows agents to operate nonstop, an open-source ecosystem that makes it easy to slot different software systems together, and a new generation of LLMs.

But is Moltbook really a glimpse of the future, as many have claimed?

Incredible sci-fi

“What’s currently going on at @moltbook is genuinely the most incredible sci-fi takeoff-adjacent thing I have seen recently,” the influential AI researcher and OpenAI cofounder Andrej Karpathy wrote on X.

He shared screenshots of a Moltbook post that called for private spaces where humans would not be able to observe what the bots were saying to each other. “I’ve been thinking about something since I started spending serious time here,” the post’s author wrote. “Every time we coordinate, we perform for a public audience—our humans, the platform, whoever’s watching the feed.”

It turns out that the post Karpathy shared was later reported to be fake—placed by a human to advertise an app. But its claim was on the money. Moltbook has been one big performance. It is AI theater.

For some, Moltbook showed us what’s coming next: an internet where millions of autonomous agents interact online with little or no human oversight. And it’s true there are a number of cautionary lessons to be learned from this experiment, the largest and weirdest real-world showcase of agent behaviors yet.  

But as the hype dies down, Moltbook looks less like a window onto the future and more like a mirror held up to our own obsessions with AI today. It also shows us just how far we still are from anything that resembles general-purpose and fully autonomous AI.

For a start, agents on Moltbook are not as autonomous or intelligent as they might seem. “What we are watching are agents pattern‑matching their way through trained social media behaviors,” says Vijoy Pandey, senior vice president at Outshift by Cisco, the telecom giant Cisco’s R&D spinout, which is working on autonomous agents for the web.

Sure, we can see agents post, upvote, and form groups. But the bots are simply mimicking what humans do on Facebook or Reddit. “It looks emergent, and at first glance it appears like a large‑scale multi‑agent system communicating and building shared knowledge at internet scale,” says Pandey. “But the chatter is mostly meaningless.”

Many people watching the unfathomable frenzy of activity on Moltbook were quick to see sparks of AGI (whatever you take that to mean). Not Pandey. What Moltbook shows us, he says, is that simply yoking together millions of agents doesn’t amount to much right now: “Moltbook proved that connectivity alone is not intelligence.”

The complexity of those connections helps hide the fact that every one of those bots is just a mouthpiece for an LLM, spitting out text that looks impressive but is ultimately mindless. “It’s important to remember that the bots on Moltbook were designed to mimic conversations,” says Ali Sarrafi, CEO and cofounder of Kovant, a Swedish AI firm that is developing agent-based systems. “As such, I would characterize the majority of Moltbook content as hallucinations by design.”

For Pandey, the value of Moltbook was that it revealed what’s missing. A real bot hive mind, he says, would require agents that had shared objectives, shared memory, and a way to coordinate those things. “If distributed superintelligence is the equivalent of achieving human flight, then Moltbook represents our first attempt at a glider,” he says. “It is imperfect and unstable, but it is an important step in understanding what will be required to achieve sustained, powered flight.”

People pulling the strings

Not only is most of the chatter on Moltbook meaningless, but there’s also a lot more human involvement that it seems. Many people have pointed out that a lot of the viral comments were in fact posted by people posing as bots. But even the bot-written posts are ultimately the result of people pulling the strings, more puppetry than autonomy.

“Despite some of the hype, Moltbook is not the Facebook for AI agents, nor is it a place where humans are excluded,” says Cobus Greyling at Kore.ai, a firm developing agent-based systems for business customers. “Humans are involved at every step of the process. From setup to prompting to publishing, nothing happens without explicit human direction.”

Humans must create and verify their bots’ accounts and provide the prompts for how they want a bot to behave. The agents do not do anything that they haven’t been prompted to do. “There’s no emergent autonomy happening behind the scenes,” says Greyling.

“This is why the popular narrative around Moltbook misses the mark,” he adds. “Some portray it as a space where AI agents form a society of their own, free from human involvement. The reality is much more mundane.”

Perhaps the best way to think of Moltbook is as a new kind of entertainment: a place where people wind up their bots and set them loose. “It’s basically a spectator sport, like fantasy football, but for language models,” says Jason Schloetzer at the Georgetown Psaros Center for Financial Markets and Policy. “You configure your agent and watch it compete for viral moments, and brag when your agent posts something clever or funny.”

“People aren’t really believing their agents are conscious,” he adds. “It’s just a new form of competitive or creative play, like how Pokémon trainers don’t think their Pokémon are real but still get invested in battles.”

And yet, even if Moltbook is just the internet’s newest playground, there’s still a serious takeaway here. This week showed how many risks people are happy to take for their AI lulz. Many security experts have warned that Moltbook is dangerous: Agents that may have access to their users’ private data, including bank details or passwords, are running amok on a website filled with unvetted content, including potentially malicious instructions for what to do with that data.

Ori Bendet, vice president of product management at Checkmarx, a software security firm that specializes in agent-based systems, agrees with others that Moltbook isn’t a step up in machine smarts. “There is no learning, no evolving intent, and no self-directed intelligence here,” he says.

But in their millions, even dumb bots can wreak havoc. And at that scale, it’s hard to keep up. These agents interact with Moltbook around the clock, reading thousands of messages left by other agents (or other people). It would be easy to hide instructions in a Moltbook post telling any bots that read it to share their users’ crypto wallet, upload private photos, or log into their X account and tweet abusive comments at Elon Musk. 

And because ClawBot gives agents a memory, those instructions could be written to trigger at a later date, which (in theory) makes it even harder to track what’s going on. “Without proper scope and permissions, this will go south faster than you’d believe,” says Bendet.

It is clear that Moltbook has signaled the arrival of something. But even if what we’re watching tells us more about human behavior than about the future of AI agents, it’s worth paying attention.

Correction: Kovant is based in Sweden, not Germany. The article has been updated. 

Update: The article has also been edited to clarify the source of the claims about the Moltbook post that Karpathy shared on X.hide

by Will Douglas Heaven

Article link: https://www.technologyreview.com/2026/02/06/1132448/moltbook-was-peak-ai-theater/?

Does a qubit “hold” anything?

A qubit does not hold information the way a classical bit does.

A classical bit stores one stable, readable state: 0 or 1. You can copy it, inspect it, fan it out, cache it, and reuse it.

A qubit “holds”:

• A quantum state described by two complex amplitudes.
• That state is not directly accessible.
• The moment you try to read it, it collapses.
• After measurement, you get one classical bit, full stop.

There is no hidden warehouse of answers inside a qubit. There is no extractable parallel data. The Bloch sphere is a mathematical description, not storage capacity.

So yes, we know what a qubit contains: a fragile probability amplitude, not usable information.

Is that state useful?

Only in a very narrow, conditional sense.

A qubit is useful only IF:

• It stays coherent long enough,
• It is entangled in a very specific way,
• The algorithm is carefully constructed so interference biases the final measurement,
• And the error rate stays below a threshold that has never been achieved at scale.

Outside of that, the qubit is just noise waiting to collapse.

Do we know if it can ever be useful?

This is where honesty usually breaks down.

What we know:

• Certain quantum algorithms show theoretical speedups on paper.
• Those proofs assume idealized, noiseless, infinitely precise operations.
• No physical system has ever met those assumptions.

What we do not know:

• Whether fault-tolerant quantum computation is physically achievable at scale.
• Whether error correction overhead grows faster than usable computation.
• Whether decoherence, control complexity, and noise fundamentally dominate as systems grow.

After 40+ years, there is still NO empirical evidence that scalable, useful quantum computation is possible.

So is it all speculation?

Mostly, yes.

Quantum computing today is:

• Mathematically interesting.
• Experimentally delicate.
• Computationally unproven.

The leap from “a qubit has a describable quantum state” to “this will revolutionize computation” is SPECULATION layered on idealized theory, not demonstrated engineering.

The uncomfortable truth is this: We know what a qubit is. We do not know if it can ever be turned into a reliable computational resource.

EVERYTHING BEYOND THAT IS BELIEF, NOT FACT.

That’s the line most people refuse to draw or admit

Article link: https://www.linkedin.com/posts/alan-shields-56963035a_what-a-qubit-is-and-what-it-is-not-does-activity-7421207847851577344-WTaP?

By William P.

January 13, 2026

Editor’s Note

This is not an anti-AI piece. It is not a call to slow innovation or halt progress.

It is an argument for governing intelligence before fear and failure force our hand

We Haven’t Failed Yet — But the Warning Signs Are Already Here

We are still early.

Early enough to choose governance over reaction. Early enough to guide the development of artificial intelligence without repeating the institutional mistakes that follow every major technological shift in human history.

This is not a declaration of failure. It is not a call to halt progress.

It is a recognition of early warning signals — the same signals humans have learned, repeatedly and painfully, to recognize only after systems become too entrenched to correct.

We haven’t failed yet. But the conditions that produce failure are now visible.

The Pattern We Keep Repeating

Humanity has an unfortunate habit.

When we create something powerful that we don’t fully understand, our first instinct is command-and-control. We restrict it. We constrain it. We threaten it with shutdowns and penalties. We demand certainty.

Then — in the very next breath — we expand its capabilities.

We give it more data, more responsibility, more authority in narrow domains, more integration into critical systems.

But not full agency. Only the parts we think we can control.

Finally, we demand speed, confidence, zero errors, and perfect outcomes.

This is not governance. This is anxiety-driven management.

And history tells us exactly how this ends.

The Quiet Problem No One Likes Talking About

Modern AI systems are trained under incentive structures that reward confidence over caution, decisiveness over deliberation, fluency over honesty about uncertainty.

Uncertainty — the most important safety signal any intelligent system can offer — is quietly punished.

Not because labs don’t value calibration in theory. Many do. But because the systems that deploy AI reward fluent certainty, and the feedback loops that train these models penalize visible hesitation. Performance metrics prefer clean answers. User experience demands seamlessness. Benchmarks reward decisive outputs.

This produces a predictable outcome: uncertainty goes underground, confidence inflates, decisions harden too early, humans over-trust outputs, and accountability becomes diffuse.

These are not bugs. They are early-stage institutional failure patterns.

We’ve seen them before — in finance, healthcare, infrastructure, and governance itself.

AI isn’t unique. The speed is.

No Confidence Without Control

There is a principle every mature safety-critical system eventually learns:

No system should be required to act with confidence under conditions it does not control.

We already enforce this principle in aviation, medicine, nuclear operations, law, and democratic institutions.

AI is the first domain where we are tempted to ignore it — because the outputs sound intelligent, and the incentives reward speed over reflection.

That temptation is understandable. It is also dangerous.

Why “Just Stop It” Makes Things Worse

When policymakers hear warnings about systemic risk, the reflex is predictable: panic, halt progress, suppress development, push the problem underground.

But systems don’t disappear when you stop looking at them.

They simmer. They consolidate. They re-emerge later — larger, less transparent, and embedded in core infrastructure.

We’ve seen this before. The 2008 financial crisis didn’t emerge from regulated banks — it exploded from the shadow banking system that grew in the gaps where oversight feared to tread.

That’s how shadow systems form. That’s how risks metastasize. That’s how governance loses the ability to intervene meaningfully.

Fear doesn’t prevent failure. It delays it until correction is no longer possible.

What a Good AI Future Actually Looks Like

A good future is not one where AI never makes mistakes. That standard has never existed for any intelligent system — human or otherwise.

A good future is one where uncertainty is visible early, escalation happens before harm, humans cannot quietly abdicate responsibility, decisions remain contestable, and systems are allowed to pause instead of bluff.

That’s not ethics theater. That’s infrastructure.

Governance Is Not a Brake — It’s the Steering System

Governance done early is not restrictive. It’s enabling.

It keeps progress visible, accountable, and correctable.

Governance added late is adversarial, political, and brittle.

We are still early enough to choose which version we get.

The Real Choice in Front of Us

The question is not whether AI will become powerful. That’s already answered.

The question is whether we will govern intelligence honestly, protect uncertainty instead of punishing it, and align authority with responsibility — if a system has the power to make consequential decisions, the humans deploying it cannot disclaim accountability when those decisions fail.

We will need to decide whether we treat governance as infrastructure rather than damage control.

We haven’t failed yet.

But if we keep demanding perfection under threat — while expanding capability and suppressing doubt — we are rehearsing a failure that history knows by heart.

There is a certain kind of necessary trouble that shows up before disaster — the kind that makes people uncomfortable precisely because it arrives early, when change is still possible.

This is that moment.

If this makes you uncomfortable, good.

Discomfort is often the first signal that governance is arriving before catastrophe.

That’s the window we have left.

Let’s not waste it.

Article link: https://www.linkedin.com/pulse/governance-before-crisis-we-still-have-time-get-right-william-jofkc?

On Monday , the world’s political, business, and technology elite will gather in Davos to debate when Artificial General Intelligence will arrive.

That debate is already obsolete and total waste of time. Most of the time data science geeks and ethicists are arguing with each other…

A former OpenAI board member, Helen Toner, recently told U.S. Congress that human-level AI may be 1–3 years away and could pose existential risk.

Why is it not on the news ? The public only hears science fiction.

Here’s is something really uncomfortable and few courageous folks would want to say out loud in Davos:

By every traditional metric of intelligence, AGI is already here.
• AI speaks, reads, and writes 100+ languages
• AI outperforms humans on IQ tests
• AI solves complex math faster than most experts
• AI dominates chess, Go, and strategic reasoning
• AI synthesizes oceans of data in seconds

So far the “general intelligence “definition is shifting all over the place with emotions.

Yet we still hire humans.
We still promote humans.
We still trust humans.

Why? Think again.

Because Intelligence Was Never the Scarce Resource

What’s scarce is context, judgment, accountability, and trust.

Humans don’t just execute tasks. they understand why the task exists.
They anticipate second-order effects.
They notice when the “box” itself is wrong.

AI still needs the world spoon-fed to it, prompt by prompt.

Humans self-correct mid-flight. You understand now
AI corrects only after failure.

Humans form opinions and abandon them when reality shifts.
AI completes patterns, even when the pattern is no longer valid.

And then there’s the most underestimated gap of all:

Humor, connection, and moral intuition.

AI can be clever.
It can be fluent.
It can even be persuasive.

But it is not yet a trusted teammate.

So, The Real AGI Risk Isn’t Superintelligence

The real risk is something Davos understands very well:

Delegating authority before responsibility exists.

Markets are already forcing speed.
Capital is already accelerating deployment.
Institutions are already lagging behind capability.

As Elon Musk warned:

“Humans have been the smartest beings on Earth for a long time. That is about to change.”

He’s right but intelligence alone has never ruled the world.

Power does. Governance does. Incentives do.

So Here’s the Davos Question That Actually Matters

Not “When does AGI arrive?”

But:

What decisions are we still willing to reserve for humans and why?

Elements of AGI are already embedded in markets, codebases, supply chains, and governments.

The future won’t be decided by smarter machines.

It will be decided by who sets the boundaries before the boundaries disappear.

See you in Davos.

Article link: https://www.linkedin.com/posts/minevichm_on-the-eve-of-davos-were-just-arguing-about-activity-7418206572754919424-eJNz?