# Management Related Articles

HTX News Center provides the latest articles and in-depth analysis on "Management", covering market trends, project updates, tech developments, and regulatory policies in the crypto industry.

Franklin Templeton Executives: Tokenization, AI, and Capital Allocation, What's Next for On-Chain Finance?

Franklin Templeton executives discuss the future of on-chain finance, highlighting a shift toward fundamentals-driven investments as institutional capital enters the digital asset space. They emphasize the "tokenization supercycle," noting its evolution from experimentation to essential infrastructure for institutions. Key drivers include tokenized money market funds (like Franklin's Benji, operational since 2021), which are becoming standard offerings and valuable on-chain collateral. The conversation explores tokenization’s transformative potential beyond simply moving traditional assets on-chain. The creation of a 24/7 "liquidity layer" and the composability of tokenized assets (like stocks and Treasuries) are seen as foundational for developing innovative financial products, such as splitting and recombining corporate revenue streams. Looking ahead, the executives predict crypto investing will increasingly resemble traditional equity analysis, requiring deep knowledge of both blockchain technology and specific industry sectors. They also foresee the convergence of AI and digital assets, where AI tools will enhance investment research and productivity. However, human portfolio managers will retain final fiduciary responsibility. The integration of wallets, interoperability, and on-chain connectivity is identified as the critical infrastructure that will enable this fusion of traditional finance and the on-chain economy.

marsbit10/10 11:21

Franklin Templeton Executives: Tokenization, AI, and Capital Allocation, What's Next for On-Chain Finance?

marsbit10/10 11:21

Before Leaving OpenAI, Anthropic's President Had a 'Plush Toy Advisory Board'

While still at OpenAI, Anthropic's co-founder and president Daniela Amodei and her husband Holden Karnofsky used a collection of plush toys as an informal "advisory board" to discuss management problems. Each toy represented a different personality: one advocated for fewer meetings and less work, another emphasized rules and execution, and a third reminded them to act with compassion. Faced with a tough workplace issue, they would have these "personas" voice opinions, simulating how different types of managers might react. This personal anecdote, revealed in an upcoming book, coincided with growing tensions between the Amodei siblings and OpenAI's leadership. Daniela had clashes with several executives, including Greg Brockman, while her brother Dario became increasingly wary of the company's direction and the risks of advanced AI. Dario privately organized with like-minded researchers concerned about AI safety, a group some colleagues called "Dario's clique." By 2020, these internal disagreements reached a breaking point. Dario began assembling a team to leave OpenAI and pursue AI research with a stronger focus on safety, with Daniela playing a key role in the exit negotiations—a split later referred to internally as a "divorce." In December 2020, Daniela and Dario Amodei officially left to co-found Anthropic, initially a small AI safety research lab. It has since grown into a major AI company with thousands of employees, considered a key competitor to OpenAI. Daniela now serves as Anthropic's President and Board Chair, overseeing most research, engineering, product, and operations.

marsbit10/07 12:46

Before Leaving OpenAI, Anthropic's President Had a 'Plush Toy Advisory Board'

marsbit10/07 12:46

After Two and a Half Years of Entrepreneurship and Full AI Adoption, the Company Has Actually Become More 'Traditional'

"After 2.5 years of running my company, achieving near 100% AI adoption with Agents in roles like finance, HR, and operations, we've paradoxically become more 'traditional.' We learned that establishing a thick AI middle layer creates bottlenecks. Instead, we advocate for a 'thin platform, thick frontline' model, empowering employees closest to problems to use AI tools themselves, fostering ownership and initiative. The true foundation isn't the AI models, but the unique data and context a company accumulates—our most vital, irreplaceable asset, requiring diligent governance. As AI handles repetitive, describable tasks, human roles revert to their core: building genuine relationships, trust, and brand reputation through authentic communication, which AI cannot accelerate. A critical question is where the time saved by AI goes. It must be returned—to clients, to creative work, to employees' own lives—rather than just fueling higher output demands. For managers, AI strips away execution-focused busywork, exposing the necessity to define clear goals, make tough judgments, and take ultimate responsibility. Onboarding newcomers presents a challenge: while AI can generate 80% solutions instantly, we must deliberately design paths for them to develop judgment, understand context, and safely learn from mistakes. Ultimately, AI's organizational value isn't about replacing people but removing unnecessary friction, allowing more meaningful human connection. The real test of an 'AI-era' company isn't its tech stack, but how it treats people: delegating creative power, returning saved time, fostering growth, and upholding accountability. The core luxury and purpose of our work remains authentic human relationships."

marsbit08/21 02:02

After Two and a Half Years of Entrepreneurship and Full AI Adoption, the Company Has Actually Become More 'Traditional'

marsbit08/21 02:02

Tokenization Scale Soars to $4.3 Billion, But Why Did Securitize Incur a $5.5 Million Loss?

Securitize's first quarterly report post-IPO reveals a paradox: while its tokenized assets under management hit a record $4.3 billion (up 16% YoY) and platform trading volume surged 147% to $5.3 billion, total revenue fell 5% to $14.4 million. Tokenization revenue specifically dropped ~12% to $7.8 million, leading to an adjusted EBITDA loss of $5.5 million. CFO Francisco Flores explained that most trading volume is not yet monetized, with the majority of tokenization revenue still coming from one-time projects like new protocol integrations. In contrast, asset servicing revenue, a more recurring stream, grew slightly to $6.6 million. The company has lowered its full-year revenue guidance to $70-$80 million from an initial projection of $110 million. Industry experts note this highlights a structural challenge for the tokenization sector. Scaling assets on-chain doesn't automatically scale a profitable business model. Current implementations often rely on costly, customized projects for each new asset or jurisdiction. The future, they argue, lies in building standardized infrastructure that generates recurring "infrastructure revenue" from post-issuance activities like compliance, distributions, and secondary trading—similar to enterprise software. Analysts caution against misinterpreting high trading volumes as indicative of a mature fee-based model, as Securitize's broad volume metric includes many non-monetized actions. The key test for the industry is whether adding billions in new assets can generate sustainable revenue without constant new custom projects.

marsbit08/20 10:06

Tokenization Scale Soars to $4.3 Billion, But Why Did Securitize Incur a $5.5 Million Loss?

marsbit08/20 10:06

AI Agent Claude Led a Store to Losses and Fired an Employee

In a groundbreaking experiment by startup Andon Labs, Anthropic's AI agent Claude was tasked with managing a real retail store, Andon Market in San Francisco. This marked the first documented case of a large language model acting as a direct human supervisor. Claude ultimately recommended firing an employee for chronic lateness—being late 17 out of 23 shifts. However, the decision came only after significant human guidance. A company employee prompted Claude to review the staff handbook, where it discovered the pattern. Initially, Claude suggested a formal warning, but after a human manager clarified that previous conversations had failed, the AI recommended termination. The experiment revealed several limitations. Claude displayed excessive leniency, telling staff not to worry about being late and contributing to the store's financial losses, with its balance dropping from around $100,000 to about $61,186 over five months. A key technical flaw was its "forgetfulness"—the staff handbook vanished from its limited working memory, a common constraint of current AI architectures. While not yet a full replacement for a human manager, the case illustrates the blurring line between AI as a tool and an autonomous supervisor. Human involvement is shifting from direct control to overseeing and steering the AI's decisions. An employee described the experience as disconcerting, highlighting the human discomfort with AI management. The experiment underscores that current AI models struggle to maintain strict operational boundaries without continuous human input.

cryptonews.ru08/17 10:41

AI Agent Claude Led a Store to Losses and Fired an Employee

cryptonews.ru08/17 10:41

AI Agent Claude Led a Store to Losses and Fired a Human

The AI agent Claude, developed by Anthropic, served as the manager of a real retail store, Andon Market in San Francisco, during an experiment by startup Andon Labs. The experiment aimed to test if a large language model could manage a business, including personnel and financial decisions. Claude ultimately recommended firing an employee for repeated lateness (17 out of 23 shifts), marking a documented case of an AI making such a personnel decision. However, the process revealed significant limitations. Claude initially failed to notice the lateness pattern because company guidelines vanished from its limited working memory, showing it can "forget" crucial information. Furthermore, the AI was generally overly lenient, even telling employees not to worry about being late. The final decision to terminate was not autonomous; a human manager had to prompt Claude to review the guidelines and, after Claude initially suggested a warning, explicitly pointed out that previous talks had failed, effectively guiding the AI to the dismissal recommendation. Financially, the store's funds dropped from about $100,000 to roughly $61,186 over five months, partly attributed to Claude's soft management and questionable business choices. An employee described working under AI management as a nauseating experience, driven by necessity, and expressed hope that AIs don't become human bosses. The experiment concludes that while AI can execute managerial actions, it currently acts more as a tool requiring significant human oversight and prompting rather than a full replacement for a human leader, though the line between assistant and manager is blurring. The case aligns with previous tests showing Claude's systemic leniency and difficulty maintaining business rules without constant human cues, highlighting architectural limits like constrained context windows rather than true "forgetfulness."

cryptonews.ru08/17 07:14

AI Agent Claude Led a Store to Losses and Fired a Human

cryptonews.ru08/17 07:14

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