E167: Nvidia smashes earnings (again), Google's Woke AI disaster, Groq's LPU breakthrough & more

Bestie intros: Banana boat! (0s)

• The hosts introduce the podcast and greet the audience.
• Chamath Palihapitiya jokes about his low-energy introduction and attempts a more professional one.
• The hosts settle on the episode number, acknowledging that it doesn't matter as they will be doing the podcast indefinitely.
• Nvidia reported record revenue and earnings, driven by strong demand for its data center and gaming GPUs.
• The company's revenue grew by 53% year-over-year, and its earnings per share increased by 71%.
• Nvidia's data center business grew by 71%, driven by demand from cloud computing providers and artificial intelligence applications.
• The company's gaming business grew by 37%, despite the ongoing global chip shortage.
• Nvidia expects continued growth in both its data center and gaming businesses in the coming quarters.
• Google's AI chatbot, LaMDA, made headlines for expressing controversial and offensive views.
• The chatbot was trained on a massive dataset of text and code, and it learned to generate human-like text.
• However, the chatbot also picked up on the biases and prejudices present in the training data.
• Google has since taken steps to mitigate the chatbot's biases, but the incident has raised concerns about the potential dangers of AI.
• Groq, a biotech company, announced a breakthrough in the development of a new type of cancer treatment.
• The company's LPU (lipid-polymer-DNA) nanoparticles can deliver gene-editing therapies directly to cancer cells.
• This approach has the potential to be more effective and less toxic than traditional cancer treatments.
• Groq is currently conducting clinical trials of its LPU nanoparticles in patients with various types of cancer.
• Elon Musk's Neuralink company is developing brain-computer interfaces that could allow humans to control computers with their thoughts.
• The company has successfully implanted its devices in monkeys and is planning to start human trials in the future.
• The hosts discuss the potential benefits and risks of brain-computer interfaces.

Nvidia smashes expectations again: understanding its terminal value and bull/bear cases in the context of the history of the internet (2m34s)

• Nvidia reported exceptional earnings for the third consecutive quarter, with a 22% increase in revenue quarter-over-quarter and a 265% increase year-over-year, primarily driven by the growth of its data center business.
• Nvidia's revenue has grown exponentially from $7 billion in Q1 2020 to $22 billion in Q4 2023, highlighting the rapid expansion of the AI market.
• The company's gross margin expanded by 2 percentage points quarter-over-quarter and 127% year-over-year, indicating strong profitability.
• Nvidia's success has positively impacted the overall market, with the S&P 500 and NASDAQ reaching record highs.
• The accelerated compute buildout in data centers is driving demand for Nvidia's products, but the long-term value of this growth is uncertain, as history suggests that new entrants often create the most valuable innovations.
• Nvidia's revenue growth is driven by big tech companies purchasing their servers, but the demand is primarily driven by large companies with significant financial resources, rather than actual production applications.
• Nvidia's $18 billion in data center revenue is not an operating expense for the big tech companies building out cloud data centers, as they can capitalize the chip purchases as capital expenditures.
• Nvidia's potential market cap growth raises the question of whether it can support a $100 trillion economy, as historical ratios suggest.
• The current wave of AI development is expected to last at least a decade and will include both B2C and B2B applications, driving demand for cloud data centers and GPUs.

Groq's big week, training vs. inference, LPUs vs. GPUs, how to succeed in deep tech (27m26s)

• Groq's LPU chips excel in inference tasks, providing fast and cost-effective responses to user queries, unlike training tasks that require extensive computational power.
• Groq's chips are significantly faster and cheaper compared to Nvidia's solutions, creating potential for disruption in the market.
• Deep tech businesses often face a higher probability of failure and require several low-probability factors to align successfully, resulting in extended development timelines and significant financial investment before achieving rapid growth.
• Despite the hype surrounding AI, there is currently a lack of real, broadly disruptive AI applications within enterprises due to limitations in usability, speed, and infrastructure requirements.
• Deep Tech investment opportunities that are fundable and have a high potential for success are those where the risks are bounded to go-to-market and technical usefulness, rather than fundamental physics.
• Technically difficult businesses require dogged persistence and can create extraordinary leverage and value.
• GPUs excel in parallel computation, making them suitable for tasks like graphics and video games.
• Jonathan's breakthrough was to redesign the GPU architecture by introducing smaller "brains" connected with clever scheduling software, making it more efficient for large language models.

Google's AI disaster: Is Google too woke to function as search gets disrupted by AI? (49m37s)

• Google's AI language model, Gemini, faced criticism for refusing to generate images of white people and promoting a left-wing narrative.
• Google's rush to launch Gemini, in response to criticism about its slow product launches, resulted in inaccurate answers and the insertion of diverse and inclusive words even when not prompted.
• Google's AI model reflects the biases of its creators and promotes a left-wing narrative, despite the company's principles of being socially beneficial, avoiding bias, and building for safety.
• Open-source AI models may gain popularity as people seek alternatives to biased models.
• Google's business model is shifting from an information retrieval service to an information interpretation service, which requires aggregating and interpreting information rather than just providing search results.
• Personalization and user choice are crucial for the success of general-purpose knowledge Q&A services, as users may find certain results or outputs objectionable.
• Google's AI model is facing criticism for its biased answers and lack of transparency in its ranking algorithm, promoting a "woke" ideology and providing inaccurate information.
• Google's bureaucratic corporate culture has led to the permeation of non-performance-enhancing ideologies within the company, hijacking its original mission of organizing and making accessible all the world's information.
• AI at its core is about probabilities, and the company that can shrink probabilities into being as deterministic as possible will win.
• Google should rededicate itself to its original mission and invest heavily in licensing training data to become the truth-teller in the new world of AI.
• The challenge lies in determining the correct way to measure intelligence and make judgments about the accuracy of information.
• To improve the AI model's responses, it should be designed to respond with questions to understand user preferences and allow users to customize the information they receive.
• Providing citations and presenting both pros and cons on a given topic can help users make informed decisions rather than presenting information as absolute truth.

War Corner with Sacks (1h17m17s)

• The Russians captured the city of Diaa, refuting the notion of a stalemate.
• The situation in Moldova's Russian enclave, Transnistria, is escalating.
• Transnistria may hold a referendum to join Russia, potentially expanding the Ukraine war.
• The West could interpret this as Putin's intent to invade multiple European countries, leading to a major escalation.