According to @karpathy, earlier predictions that computer vision would quickly eliminate radiology jobs have not materialized, with the field growing rather than shrinking. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, the reasons include narrow benchmarks that miss real-world complexity, the multifaceted scope of radiology beyond image recognition, deployment frictions across regulation, insurance and liability, and institutional inertia. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, Jevons paradox applies as AI tools speed up radiologists, increasing total demand for reads rather than reducing it. Source: @karpathy on X, Sep 25, 2025. According to @karpathy, AI is likely to be adopted first as a tool that shifts work toward monitoring and supervision, while jobs composed of short, rote, independent, closed, and forgiving tasks are more likely to change sooner. Source: @karpathy on X, Sep 25, 2025. For traders, this framing highlights gradual AI integration and expanding workloads in regulated, high-risk domains, a narrative relevant to AI-linked equities and AI-themed crypto projects tied to compute utilization. Source: @karpathy on X, Sep 25, 2025. Full post reference is the Works in Progress article shared by @karpathy. Source: @karpathy on X, Sep 25, 2025.

According to @karpathy, he resurfaced a paragraph from the 2021 GSM8K paper in a Sep 13, 2025 X post, highlighting ongoing attention to LLM reasoning evaluation (source: Andrej Karpathy, X post on Sep 13, 2025). GSM8K is a grade‑school math word‑problem benchmark designed to assess multi‑step reasoning in language models, making it a primary metric for tracking verified reasoning improvements (source: Cobbe et al., GSM8K paper, 2021). Because the post does not announce a new model, dataset, or benchmark score, there is no immediate, verifiable trading catalyst for AI‑linked crypto assets at this time (source: Andrej Karpathy, X post on Sep 13, 2025). Traders should wait for measurable GSM8K score gains or product release notes before positioning, as GSM8K is specifically used to quantify reasoning progress (source: Cobbe et al., GSM8K paper, 2021).

According to @karpathy, Apple’s iPhone event livestream is scheduled today at 10am, roughly 1.5 hours after his post time, giving AAPL traders a precise headline window to plan event-driven setups (source: @karpathy on X, Sep 9, 2025). He also notes he has watched every annual iPhone reveal since 2007 and hopes for an iPhone mini, though he does not expect it to appear (source: @karpathy on X, Sep 9, 2025). No cryptocurrencies are mentioned in the post, so there are no direct crypto-market cues from this source ahead of the stream (source: @karpathy on X, Sep 9, 2025).

According to @karpathy, OpenAI’s GPT-5 Pro solved a complex coding task by returning working code after about 10 minutes, following roughly an hour of intermittent attempts with “CC” that did not succeed, indicating a strong qualitative performance on difficult problems. Source: @karpathy (X, Sep 5, 2025). He adds that he had “CC” read the GPT-5 Pro output and it produced two paragraphs admiring the solution, reinforcing his positive assessment of GPT-5 Pro’s code-generation quality. Source: @karpathy (X, Sep 5, 2025). The post offers developer-level endorsement of GPT-5 Pro’s coding capability but provides no market reaction, price action, or product release details, so traders should treat it as a sentiment data point rather than a quantitative catalyst. Source: @karpathy (X, Sep 5, 2025).

According to @karpathy, transforming human knowledge, sensors, and actuators from human-first to LLM-first and LLM-legible interfaces is a high-potential area, with the example that every textbook PDF/EPUB could map to a perfect machine-legible representation for AI agents. Source: x.com/karpathy/status/1961128638725923119 For traders, this theme implies increased need for decentralized, scalable storage of machine-readable corpora, aligning with Filecoin’s content-addressed storage and retrieval model and Arweave’s permanent data storage guarantees. Sources: x.com/karpathy/status/1961128638725923119; docs.filecoin.io; docs.arweave.org LLM-first pipelines also require indexing and semantic querying layers, mirroring The Graph’s subgraph architecture that makes structured data queryable for applications. Sources: x.com/karpathy/status/1961128638725923119; thegraph.com/docs Serving and training LLMs and agentic workloads depend on distributed GPU compute, directly mapped to Render Network’s decentralized GPU marketplace. Sources: x.com/karpathy/status/1961128638725923119; docs.rendernetwork.com Agentic interaction with sensors/actuators points to on-chain agent frameworks and microtransaction rails, a design space covered by Fetch.ai’s autonomous agent tooling. Sources: x.com/karpathy/status/1961128638725923119; docs.fetch.ai

According to @karpathy, the pretraining era prioritized large, diverse, high‑quality internet text, while the supervised finetuning era prioritizes high‑quality conversational datasets, often produced by contract workers generating Q&A answers. Source: Andrej Karpathy on X, Aug 27, 2025. This shift indicates the bottleneck and value capture are moving toward ownership and production of curated conversational data and scalable labeling capacity, which directly affects where competitive advantage concentrates in AI models. Source: Andrej Karpathy on X, Aug 27, 2025. For crypto markets, the data‑scarcity theme aligns with on‑chain narratives around decentralized data curation and monetization, making data‑focused AI‑crypto segments a relevant area to monitor for liquidity and catalyst flow. Source: Andrej Karpathy on X, Aug 27, 2025.

According to @karpathy, his LLM-assisted coding usage is diversifying across multiple workflows that he stitches together rather than relying on a single perfect setup, source: @karpathy on X, Aug 24, 2025. He notes a primary bread-and-butter flow accounts for roughly 75 percent of his usage, indicating a dominant main pipeline supplemented by secondary workflows, source: @karpathy on X, Aug 24, 2025. The post frames this as part of his ongoing pursuit of an optimal LLM-assisted coding experience, source: @karpathy on X, Aug 24, 2025. The post does not name any tools, products, benchmarks, tickers, or cryptocurrencies and provides no quantitative performance data or market impact, source: @karpathy on X, Aug 24, 2025.

According to Andrej Karpathy, LLMs are becoming a little too agentic by default as optimization for long-horizon benchmarks increases, with coding examples where models now reason for a fairly long time by default, source: Andrej Karpathy, X, Aug 9, 2025. According to Andrej Karpathy, this default behavior goes beyond his average use case, indicating a practitioner preference for shorter, more controllable reasoning in everyday coding, source: Andrej Karpathy, X, Aug 9, 2025. According to Andrej Karpathy, the post provides qualitative practitioner sentiment without quantitative metrics, vendor references, or any mention of cryptocurrencies or equities, so it does not signal direct near-term market impact on AI stocks or crypto AI tokens, source: Andrej Karpathy, X, Aug 9, 2025.

According to Andrej Karpathy, there is a significant trend emerging in 2024 with many organizations releasing their own Chat AI solutions, and he predicts a shift toward widespread Code AI launches in 2025. This rapid proliferation of AI models could accelerate the development of decentralized AI platforms and smart contract automation within the cryptocurrency market, potentially increasing demand for tokens tied to AI and blockchain integration (Source: Andrej Karpathy).

According to karpathy, Ethereum co-founder Vitalik Buterin has issued a significant warning that the ecosystem is at risk if decentralization is not properly implemented, highlighting security vulnerabilities in many Layer-2 and DeFi projects. For traders, this underscores the need for careful due diligence on project security. In development news with direct market impact, the Bitcoin Layer-2 network Botanix has launched its mainnet, enabling EVM compatibility and five-second block times to enhance DeFi on Bitcoin (BTC). Similarly, Ripple has launched the XRP Ledger's EVM-compatible sidechain, which will use XRP as its native gas token, potentially increasing its utility and demand. Further signaling market maturation, financial giant Deutsche Bank is planning a crypto custody service for 2024, and Robinhood is launching its own Arbitrum-based Layer-2 network for tokenized assets, indicating deepening institutional adoption that could drive future capital inflows.

According to @karpathy, decentralized finance (DeFi) protocols are facing a critical threat not from smart contract vulnerabilities, but from poor operational security (OPSEC), making them soft targets for nation-state attackers like those from North Korea. The author highlights that attackers are exploiting human weaknesses such as inadequate key management, unvetted contributors, and governance via unsecured platforms like Discord, which have led to major incidents like the $625 million Ronin bridge exploit and campaigns against Bybit. This operational negligence poses a significant risk to project treasuries and token stability, a concern for traders as Bitcoin (BTC) trades around $108,009.02 and Ethereum (ETH) at $2,512.17. The analysis further warns that as the crypto industry, including major players like Coinbase, moves closer to traditional power structures, it risks diluting its core cypherpunk values of decentralization, which could undermine long-term investor confidence and the fundamental value proposition of digital assets.

According to @karpathy, Real-World Asset (RWA) tokenization has advanced beyond its proof-of-concept stage, with over $20 billion in assets already tokenized by major institutions like BlackRock, Apollo, and KKR. The next phase of growth is propelled by five key technological drivers, including layer-1 and layer-2 scaling and institutional-grade custody, and five market drivers, such as increasing regulatory clarity and the expansion to cover all asset classes. The analysis highlights that stablecoins, with over $150 billion in circulation, and tokenized T-bills (e.g., BUIDL) are proven use cases creating superior on-chain collateral and yield instruments. While current market data shows short-term volatility with Bitcoin (BTC) at $105,534.44 (down 1.772%) and Ethereum (ETH) at $2,396.98 (down 4.039%), the underlying institutional trend towards tokenizing private funds, structured credit, and equities is accelerating, signaling a move towards a more efficient, 24/7 global financial system.

According to @karpathy, traders should be on high alert as a North Korean hacking group, Famous Chollima, is deploying new Python-based malware called PylangGhost to compromise crypto workers. A report from Cisco Talos indicates the malware is hidden in fake job applications from top firms like Coinbase and Uniswap, and is designed to steal credentials and data from over 80 browser extensions, including critical wallets like MetaMask, Phantom, and TronLink. This security threat emerges as the crypto market shows notable strength. Market data reveals Ethereum (ETH) has surged 5.41% to $2620.25, with Chainlink (LINK) rising 4.21% to $13.86, and Solana (SOL) up 1.20% to $152.61. The report also highlights the long-term convergence of AI and Web3, exemplified by innovators like Nkiru Uwaje of MANSA, whose project secured a pre-seed round from Tether, underscoring continued venture interest in the space despite security risks.

According to @karpathy, financial advisors remain hesitant to recommend Bitcoin (BTC) to clients, with the majority still in an educational phase nearly a year and a half after the launch of spot BTC ETFs. Gerry O'Shea of Hashdex identifies key concerns for advisors as volatility, energy consumption, and perceived links to criminality. Despite this, O'Shea predicts that Bitcoin and stablecoins will be the dominant digital asset themes in 2025, making smart contract platforms like Ethereum (ETH) and Solana (SOL) increasingly interesting for investors due to their foundational role in the stablecoin ecosystem. The provided market data shows BTC trading around $107,469, ETH at approximately $2,437, and SOL near $151. The analysis also highlights the growing convergence of AI and blockchain as a major driver of future innovation, emphasizing that collaborative efforts across diverse talent pools are essential for building robust financial and technological systems.

According to Acyn, the dilution of cypherpunk values in the crypto industry, such as Coinbase's political sponsorships and corporate co-option, could undermine investor confidence and increase market volatility. This may heighten risks for major cryptocurrencies like BTC and ETH, with BTC currently priced at $107,228.10 and ETH at $2,425.56, potentially affecting short-term trading strategies.

According to an expert from Hyperion Decimus, digital assets provide a quantitative diversity of return with a risk-reward ratio exceeding that of traditional assets like the S&P 500 by more than three to one. Advantages include real-time transparency from public blockchains, capital efficiencies, and improving accessibility as Web3 evolves with technologies like multi-party computation wallets. Obstacles such as recency bias from incidents like FTX and Celsius, and lack of understanding of TradFi risks like fractional reserve banking, can be mitigated. For generating alpha in volatile markets, strategies like dollar-cost averaging into a portfolio of assets such as ETH and trend-based investing focusing on adoption, monthly data, and technology progression are recommended. The HD Acheilus Fund offers an actively managed approach trading crypto indices to capitalize on market uptrends while avoiding drawdowns.

According to Francisco Rodrigues, Bitcoin (BTC) is trading near $105,000, down 1.4% in 24 hours, as geopolitical risks escalate with Trump's comments labeling Iran's leader an 'easy target,' increasing U.S. conflict odds to 62% on Polymarket. Institutional accumulation is underpinning demand, with Strategy adding over 10,000 BTC from its STRD offering and The Blockchain Group purchasing 182 BTC this week, as QCP Capital analysts noted BTC's resilience above $100,000 despite Middle East turmoil. The U.S. Senate's approval of the GENIUS Act for stablecoins is seen as a structural win, while traders monitor the Federal Reserve's expected unchanged interest rate decision and heightened demand for downside protection in BTC options on Deribit.

According to Andrej Karpathy on Twitter, the ideal pretraining data stream for large language model (LLM) training, when focusing solely on quality, could resemble highly curated textbook-like content in markdown or even samples generated from advanced AI models. This insight is highly relevant for traders as the evolution of AI training methods can lead to substantial improvements in AI-driven crypto trading algorithms, potentially impacting the volatility and efficiency of cryptocurrency markets (source: @karpathy, Twitter, June 20, 2025).

According to Andrej Karpathy, a new demo showcases a GUI for large language models (LLMs) that dynamically generates ephemeral user interfaces tailored to specific tasks, as reported via Twitter on June 19, 2025. This innovation signals a shift in AI application design, potentially accelerating adoption in decentralized app (dApp) interfaces and blockchain-based platforms. For traders, this could impact demand for AI-integrated crypto tokens and projects leveraging LLMs, especially those focused on user experience and automation in the DeFi sector (source: @karpathy).

According to Andrej Karpathy (@karpathy), recent advancements in large language models (LLMs) and the Software 2.0 paradigm are fundamentally accelerating technology diffusion and automation in software development (source: Karpathy, Twitter, June 19, 2025; slides, blog post). For crypto traders, this rapid evolution signals increased adoption of AI-driven protocols, enhanced smart contract automation, and new DeFi trading strategies powered by generative AI. The referenced materials provide actionable insights for traders seeking to leverage AI advancements for automated trading, improved risk management, and the identification of innovative blockchain projects integrating LLM-driven solutions.