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Zcash researchers have completed formal verification of Ironwood, a new shielded pool introduced via the NU6.3 upgrade. They published a machine-checked proof, written in Lean and comprising over 2,700 theorems, that establishes the pool's "balance integrity." This proof confirms that, under its cryptographic assumptions, Ironwood cannot contain undetectable counterfeiting bugs, ensuring it cannot pay out more value than has publicly entered it. The verification, which took multiple teams over a month, covers the zero-knowledge proof system, circuit rules, and ledger accounting but not Ironwood's separate privacy guarantees. Ironwood was created in response to a theoretical vulnerability in the older Orchard pool that could have allowed undetectable counterfeiting, though there's no evidence of exploitation. Funds migrating from Orchard must pass through a public "turnstile" checkpoint, designed to prevent any hypothetical excess coins from entering the new pool and to provide evidence on whether the old pool was exploited.

The Russian cryptocurrency market is entering a clearer regulatory phase, with major banks preparing infrastructure for legal digital asset trading. New regulations are set to begin in September, with full implementation expected by 2027. VTB Bank aims to be among the first licensed operators, developing a comprehensive system for buying, selling, storing, and accounting for crypto assets. The market's daily turnover is already estimated at tens of billions of rubles, presenting a significant opportunity for banks to capture market share and commission income through user-friendly, secure services. Key future infrastructure elements will include blockchain, digital accounting, data protection, automation, and potentially AI. For private investors, crypto represents a volatile asset class where earnings can come from trading, investing, mining, or staking, but success depends on risk management, platform security, and understanding regulatory shifts. Bitcoin is seen as a market benchmark, while Ethereum's value is tied to its smart contract ecosystem. Market valuations remain sensitive to regulation, macroeconomic trends, and technological developments.

Coinbase reported mixed Q2 results amidst a crypto market slowdown. Total revenue fell 14% quarter-over-quarter to $1.22 billion, driven by a 24% decline in trading volume to $146.4 billion, resulting in a net loss of $359 million. However, the company grew its market share to a record 10.3%. A key positive was the expansion of non-trading services, which accounted for 48% of total net revenue. The stablecoin segment remained significant with $20 billion in average USDC balances. Most notably, revenue from prediction markets surged 106% sequentially, now generating over $100 million annually. This shift highlights Coinbase's strategic diversification beyond core trading, as it invests in new products to adapt to market conditions and user demand.

In a recent revelation, Thibault "Tibo" Sottiaux, the Codex lead at OpenAI, confirmed that Google developed a ChatGPT-like chatbot called "LMChat" a full year before OpenAI's launch. According to Tibo, who was part of the Google team at the time, the company was too cautious to release it, fearing it might disrupt its core search and advertising business. DeepMind, where the project was housed, was also restricted from launching products that could compete with Google's existing services. This account aligns with past statements from former Google Brain head Jeff Dean, who mentioned an internal chatbot that was deemed inferior to Google Search at the time. The article highlights this as a classic case of "innovator's dilemma," comparing Google's hesitation to Xerox's failure to commercialize groundbreaking technologies like the graphical user interface in the 1970s. The piece further notes a recent wave of high-profile departures from Google's AI teams to rivals like OpenAI and Anthropic, including key figures such as Transformer co-author Noam Shazeer and AlphaFold lead John Jumper. It concludes that Google's excessive caution and internal constraints, driven by the need to protect its lucrative search business, ultimately cost it a pioneering position in the generative AI revolution, allowing OpenAI to seize the initiative with ChatGPT's public release in November 2022.

While South Korean auto workers fear job losses from robotics, Elon Musk envisions a future where AI and robots render most work optional. This article explores the growing tension between immediate anxieties over automation and long-term visions of a post-work society. The piece begins with recent strikes at Hyundai's Korean plants, where unions, amid standard wage negotiations, also sought job guarantees against advancing robotics—specifically mentioning Boston Dynamics' Atlas. This reflects how anxiety about technological displacement is emerging even before robots are fully capable of replacing skilled labor on assembly lines. The author argues that while current robotics still struggle with the nuanced, experiential knowledge of veteran workers, the *perception* of imminent replacement is fueling social conflict prematurely. This modern "Luddite" sentiment is compared to the 19th-century English textile workers who smashed machines. Historically, Luddites weren't simply anti-technology; they were protesting the rapid devaluation of their skills and the unequal distribution of productivity gains. Similarly, today's workers ask who will bear the cost of transition and share in the new wealth created by machines. In contrast, figures like Elon Musk propose an optimistic endpoint: with AI and robotics driving extreme abundance, the link between work and survival could break. He suggests concepts like "Universal High Income" could allow society to share the technological bounty, transforming work from a necessity into a choice. The core challenge, however, lies in the transition. The author notes that technology's benefits diffuse slowly, while its disruptive costs—job losses, skill obsolescence—can be concentrated and immediate. The risk is a painful interim period where productivity gains are captured by a few before new social contracts, safety nets, and retraining systems are established. The conclusion calls for proactive governance. Just as past industrial revolutions gave rise to labor standards and social safety nets, the robotics era needs its own frameworks. These should address job transition support, distribution of productivity gains, safety liability, and ethical deployment. Embracing such "constraints" is not opposition to progress but a necessary step to ensure technology benefits society broadly. The discussion sparked by Hyundai's workers, therefore, is not premature but essential.