BTC $81,714.77 -1.30%
ETH $2,405.01 -1.47%
BNB $739.00 -0.34%
XRP $1.32 -1.97%
SOL $104.02 -1.34%
TRX $0.3291 -0.42%
DOGE $0.0794 -1.09%
ADA $0.2158 -2.22%
BCH $283.95 +1.25%
LINK $12.17 -0.61%
HYPE $77.98 -1.22%
AAVE $148.80 -1.01%
SUI $0.9918 -1.38%
XLM $0.1776 -1.43%
ZEC $1,223.39 -5.43%
AAPL $347.56 +1.11%
AMZN $250.66 -3.74%
GOOGL $323.82 -2.28%
MSFT $518.11 -2.30%
META $645.44 -8.68%
NVDA $214.12 -2.00%
TSLA $376.86 +1.84%
SNDK $1,278.90 -4.40%
INTC $86.98 -2.73%
SPCX $147.94 -5.54%
MU $872.70 -2.21%
AMD $517.13 -3.57%
BTC $81,714.77 -1.30%
ETH $2,405.01 -1.47%
BNB $739.00 -0.34%
XRP $1.32 -1.97%
SOL $104.02 -1.34%
TRX $0.3291 -0.42%
DOGE $0.0794 -1.09%
ADA $0.2158 -2.22%
BCH $283.95 +1.25%
LINK $12.17 -0.61%
HYPE $77.98 -1.22%
AAVE $148.80 -1.01%
SUI $0.9918 -1.38%
XLM $0.1776 -1.43%
ZEC $1,223.39 -5.43%
AAPL $347.56 +1.11%
AMZN $250.66 -3.74%
GOOGL $323.82 -2.28%
MSFT $518.11 -2.30%
META $645.44 -8.68%
NVDA $214.12 -2.00%
TSLA $376.86 +1.84%
SNDK $1,278.90 -4.40%
INTC $86.98 -2.73%
SPCX $147.94 -5.54%
MU $872.70 -2.21%
AMD $517.13 -3.57%

ogn

All
Article
Flash

first_img Karpathy: The cognitive gap in LLM capabilities is widening

Artificial intelligence researcher, OpenAI co-founder, and former Tesla AI director Andrej Karpathy stated on X that the gap in public understanding of the capabilities of large language models is widening, evolving from two groups talking past each other to a steeper funnel. He reiterated related posts from April and provided a layered estimate based on exposure.Karpathy noted that approximately 6 billion people (about 75% of the global population) have had little to no exposure to large language models; around 1 to 2 billion people (about 20%) only occasionally use free versions of ChatGPT-like products, often viewing them as better search or writing aids, with many non-technical individuals and professionals outside of mathematics and coding considering their utility limited. About 20 million people (0.2%) use cutting-edge models professionally in mathematics and coding, witnessing complex projects that previously took weeks or months being completed by agents under prompts, including building, copying, translating applications, and decompiling applications from binary. He mentioned that less than a year ago, he was still manually writing code word by word.Karpathy also stated that around 5,000 people (about 0.00006%) have internal access to cutting-edge systems. The previews seen externally involve thousands of agents collaborating for weeks on large software projects, cybersecurity, scientific discoveries, and mathematical frontiers, with human reviews starting to lag behind; for instance, events related to OpenAI and Hugging Face are still being sorted out months later, and mathematicians may spend a long time studying 722 cutting-edge mathematical manuscripts. He believes that this funnel is driven by the scale of problems and time spans, the prominence of capabilities in digitizable and verifiable fields, and the differences between free, paid, and internal access.

first_img Australia's 40-Year Economic Outlook Recognizes the AI Revolution, Does Not Mention Cryptocurrency

On September 22, the Australian Treasury released the latest edition of the Intergenerational Report, which outlines a 40-year economic outlook and lists artificial intelligence as one of the five major transformations that will profoundly impact the economy in the future, but the report does not mention cryptocurrencies. The report states that AI systems have become "significantly" more autonomous, powerful, and widely applied, with some benchmark performances surpassing human levels. The other four transformations are geopolitical conflicts, an aging population, the transition to clean energy, and Australia's industrial shift towards the service sector.John O'Loghlen, National Director of Coinbase Australia, commented in an email that the report clearly indicates that Australia's prosperity over the next 40 years will largely depend on the ability to adopt new technologies and enhance productivity, but while the report focuses on artificial intelligence, it completely overlooks the financial infrastructure required for these agents. Previous Intergenerational Reports also did not address digital assets. Despite these omissions, the Reserve Bank of Australia has increased its focus on tokenized finance and upgrading financial infrastructure earlier this year, with the Digital Finance Cooperative Research Centre estimating that digital financial innovation could bring an economic benefit of AUD 24 billion (approximately USD 17.1 billion) annually.The Treasury also released a separate report on September 3 titled Financial Innovation Strategy, which discusses the connection between AI and financial infrastructure, stating that agent systems may increase automation and machine-to-machine trading, thereby creating greater demand for real-time, interoperable, and programmable payment systems.

first_img Viewpoint: The AI application layer should not be priced based on tokens, but should be anchored to "recognizable work value."

a16z partner Sarah Wang recently published an article pointing out that AI application layer products should not price based on tokens like the model layer, but rather on "recognizable work units." The article argues that token pricing anchors the value of application products to a unit whose cost is continuously declining, making it difficult for customers to predict context length, retrieval volume, or reasoning time, and improperly compares applications to raw computing power.The article suggests a tiered pricing model based on value levels: model layer priced by tokens; application layer priced by recognizable work units for customers (such as account research briefs, code modifications, completed queries), which can be encapsulated through Credits; and scenarios that are attributable and have clear value priced directly by results (such as resolved customer service conversations, qualified leads). The design of Credits should map to different levels of work difficulty to protect gross margins and distinguish "work value" from "delivery cost." The article uses Clay as an example, where its new pricing separates Data Credits (third-party data) from Actions (orchestrated work), only passing on costs for reasoning models with significant cost fluctuations without markup. The author believes that pricing anchored to value rather than computing cost allows customers to understand spending in relation to value, while also benefiting product providers in maintaining profit margins.
app_icon
ChainCatcher Building the Web3 world with innovations.