Viresh Kanabar
Summary The ‘DeepSeek moment’ selloff earlier this year was primarily driven by the unwind of crowded momentum stocks and de-grossing events, rather than the popular narratives at the time concerning DeepSeek’s model development or fears about GPU requirements. AI demand continues to soar, evidenced by an exponential increase in token processing (e.g., Google processing nearly […]
Dominique Dwor-Frecaut
Summary The AI revolution’s footprint has so far been much bigger in markets than in the real economy. The Magnificent Seven’s extremely large capex spending has a high import component that limits its GDP impact. IT employment is gently falling but represents only about 2% of private employment. As AI adoption spreads to the whole […]
Dalvir Mandara, Eric Wang, Bilal Hafeez
Not a day goes by without a new LLM model hitting the market, each claiming to outperform the rest on some benchmark leaderboard. But these self-reported scores often deserve scepticism. Much like marking your own homework, these evaluations can be misleading. For starters, many benchmark datasets – or even their answers – might appear in […]
Viresh Kanabar
Summary Inference-time scaling is the New Frontier: AI progress is shifting to focus more on inference-time scaling techniques that refine responses after initial training. Deeper Reasoning, Longer Waits: Enabling models to break complex tasks into smaller steps during inference results in longer waiting times but better responses. Better Cost-Outcome Alignment: This approach better aligns cost […]
Bilal Hafeez
Assessing and Alleviating State Anxiety in LLMs: Finds that trauma prompts can change the ‘state’ anxiety of LLMs, while relaxation ones reduce anxiety! Structured Outputs Enable General-Purpose LLMs to Be Medical Experts: The paper does away with fine-tuning an LLM on medical data and instead introduces a seven-step cognitive process (chain-of-thought) to answer questions. E.g., […]
Viresh Kanabar
Summary Despite significant strides in speed, cost, intelligence, and multi-modal capabilities, AI has yet to move from testing to widespread adoption. Key barriers to adoption include hallucinations, limited memory, reliance on good prompts, and poor real-world adaptability. For AI to deliver real business value, there must be a shift in focus from just improving models […]
Bilal Hafeez
If you spend time on X or Twitter, you would think the release of Grok 3.0 and ChatGPT’s DeepResearch models marks the dawn of superintelligence (AGI). In reality, these advances highlight the limitations of large language models (LLMs) and how far we still are from AGI. At their core, these models are only as good […]
Bilal Hafeez
Everyone has a take on DeepSeek. In most cases, it simply re-affirms their prior views (US is weak and China is winning; US is strong, and China is cheating, etc.). But here are three things that most people are missing (well, two things really): 1). Lower Costs DeepSeek has delivered way lower inference costs for […]
Bilal Hafeez
Listen to an AI-generated audio summary of this article: New Kid on the Block The AI world is abuzz with the Chinese DeepSeek LLM model that apparently costs much less to build than OpenAI’s ChatGPT, performs better than the OpenAI o1 model and is cheaper for users to run. That is at least what […]
Matthew Tibble
Everyone from Main Street to Wall Street has been scrambling to deploy AI since ChatGPT dropped in late 2022. By one estimate, 2023 saw AI-related sales jump 29% to hit a whopping $166 billion. By 2027, we will be staring at a colossal $400 billion market. And the financial sector will be responsible for a […]
Matthew Tibble
AI is all anyone can talk about since the advent of ChatGPT. From revolutionizing healthcare with personalized medicine to generating trading signals in financial markets, every industry is claiming a new future for itself thanks to the technology. And perhaps rightly so. But how smart is this new technology? And perhaps more fundamentally, how do […]
Matthew Tibble
AI has a power problem. Even simple graphics processors for AI applications consume several hundred watts. But the most powerful computer we use for machine learning? That award goes to Frontier, which consumes 20 megawatts a year. That is $40 million of electricity. Frontier boasts an exaflop of compute: a billion, billion floating point mathematical […]
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