Podcast Summary
Vertical AI enterprise: It's never too late to start a vertical AI enterprise with unique resources and partnerships, as shown by Elon Musk's XAI and its potential integration with Twitter and Tesla.
It's never too late to start a vertical AI enterprise, even if some players in the industry seem to be dominating the conversation. Elon Musk's XAI is a reminder that new competitors can still make an impact, especially when they have unique resources and partnerships. In the recent LLM war discussion, the focus has been on smaller models and the competition between GPT40 and Claude 3.5. However, there are still lurking competitors like XAI that haven't fully entered the fray. XAI, led by Elon Musk, may not have made significant strides yet, but with its integration into Twitter and potential Tesla connection, it's important not to underestimate its potential. So, if you're considering starting a vertical AI enterprise, take inspiration from XAI and remember that every player in the industry has unique strengths and resources.
Elon Musk's push for world's most powerful AI: Elon Musk and XAI aim to have the world's most powerful AI by Dec 2023, investing in 100,000 liquid-cooled H100 chips, attracting Tesla employees and competing with NVIDIA's B20 chip.
Elon Musk and his company, XAI, are making a significant push to have the world's most powerful AI by every metric by December 2023. Musk has been investing heavily in computing resources, including a new supercluster with 100,000 liquid-cooled H100 chips, making it the most powerful AI training cluster in the world. This investment has attracted a flow of Tesla employees to XAI, and Musk previously indicated that Grock 3 would be trained on these chips. Meanwhile, NVIDIA, a major player in the AI chip market, is navigating U.S. export restrictions to China by customizing offerings to stay competitive in the market. NVIDIA's latest efforts include the B20 chip, part of the Blackwell chip line, which is set to go into production later this year. These developments underscore the intense competition and investment in AI technology and the significant resources being dedicated to its advancement.
AI models competition: Apple's new small models outperform some leading open models and they have made their training data and pre-training recipes publicly available, encouraging community collaboration.
The competition in the field of AI models, specifically small models, is intense and rapidly evolving. NVIDIA is facing challenges in aligning with US regulations while staying competitive in the Chinese market, where local alternatives are gaining popularity. Apple, on the other hand, has recently released two new small models, DCLM 6.9B and 1.4B, which perform exceptionally well and outperform some leading open models. Apple has also made their training data and pre-training recipes publicly available, encouraging community collaboration. AMD, another major player, is making bold claims about the performance of its new laptop chips, but has yet to prove they can surpass Apple's offerings. The race for small models is just as competitive as the quest for Artificial General Intelligence (AGI), and companies are continually pushing the boundaries to deliver superior performance and features.
Tech Hardware Race, AI Innovation: Qualcomm and AMD are challenging Apple's MacBooks with new chips, Microsoft's Windows laptops are improving, AMD claims to surpass MacBook, Super Intelligent partners with Spotify, launches AI learning feed, and Venice offers secure AI interactions without constant surveillance.
The race for superior laptops is heating up, with both Qualcomm and AMD making strides towards challenging Apple's MacBooks. Microsoft's Windows laptops, powered by Qualcomm's new Snapdragon chips, are making significant improvements, but AMD is also entering the fray with chips that can compete. AMD even boasted about surpassing the MacBook at a recent event. However, it remains to be seen which company will truly come out on top. Meanwhile, in the world of AI, Super Intelligent is making waves with its new partnership with Spotify and the launch of its AI learning feed. Users of the Spotify app can now access Super's content directly, and the AI learning feed offers tutorials, polls, news items, and a platform for showcasing AI projects. Additionally, Super has initiated the Super Summer Challenge, which shares new challenges each week to discover new AI tools and use cases. Lastly, privacy concerns continue to be a pressing issue in the AI sphere. Many leading AI companies store users' entire conversation history, and some even attach it to their identities forever. Venice offers an alternative, providing a secure platform for AI interactions without the constant surveillance. So, whether it's in the realm of tech hardware or AI, competition and innovation are driving progress and offering new solutions for consumers.
Legal AI startups competition: Despite early investment and access to advanced AI technologies, starting a legal AI startup faces intense competition due to the industry's rapid advancement and need for a unique value proposition and quick go-to-market strategy.
The debate around starting a vertical AI startup in an industry, using the example of legal AI with Harvey, highlights the evolving landscape of enterprise AI adoption. Harvey, a legal AI company, faced intense competition despite having a head start with OpenAI's investment and early access to ChatGPT. The industry's rapid advancement, driven by generative AI technologies, shows that it's not too late to enter the market but requires a unique value proposition and quick go-to-market strategy. Meanwhile, privacy-respecting AI tools like Venice.ai offer alternatives for individuals and businesses seeking uncensored and permissionless AI applications.
Impact of generalist models on specialized LLMs: The trend of generalist models outperforming specialized models in various fields, including finance, raises questions about the value of specialized proprietary data in the context of Large Language Models.
Harvey, as a pioneer in legal Large Language Models (LLMs), had the opportunity to establish industry standards and address ethical concerns, including the hallucination issue. However, the skepticism towards Harvey's performance arises from the observation that generalist models, like Bloomberg GPT, often outperform specialized models in various fields, including finance. This pattern raises questions about the value of specialized proprietary data in the context of LLMs. Despite Harvey's claims of high client satisfaction, the potential impact of this trend on vertical industry-oriented LLMs is significant and subject to debate. Additionally, a big law partner's comment, shared by Emily, indicates that Harvey may have a high number of inactive users, potentially leading to high churn rates. This information, along with the ongoing debate about the role of specialized data in LLMs, highlights the importance of monitoring and understanding the evolving landscape of this technology.
AI implementation gap: Despite increasing claims of AI usage in businesses, particularly in the legal industry, the gap between intention and actual implementation is significant due to marketing use, capacity issues, and slow industry adaptation.
While there is a growing trend of businesses, particularly in the legal industry, claiming to use AI, the gap between intention and actual implementation is vast. This issue isn't just limited to Harvey or the legal industry, but is a common challenge faced by vertical AI startups in general. The reasons for this gap are multifaceted. While some firms may be using AI as a marketing tool without actual implementation, there are also capacity issues and the slow process of industries adapting to AI. Bruno Koba, an MBA candidate at Stanford, further emphasizes this point, stating that every vertical is now crowded with AI startups, but foundation models are not yet advanced enough to provide tangible solutions. Those building on existing models are reliant on the next big update, while those training specialized models are not outperforming general models. In summary, the implementation of AI in various industries is a complex issue with numerous challenges, and it's essential to understand the nuances behind the hype.
AI startups vs advanced AI models: As advanced AI models from big labs become more capable, vertical AI startups may face increased competition but can still differentiate by focusing on specific industries and delivering superior expertise
The role of AI startups could change significantly as we approach the launch of advanced AI models from big labs. These models, like GPT-5, might be able to cover a wide range of industries on their own, potentially making it harder for vertical AI startups to capture substantial value compared to previous innovation cycles. However, Gary Tan argues that there's still room for startups to compete by focusing on serving specific industries better than the general models. The success of these startups will depend on their ability to retain customers through superior vertical expertise. This shift in the AI landscape brings unique challenges and opportunities for entrepreneurs, requiring a strategic focus on vertical niches and a commitment to delivering value through deep industry knowledge.
Large Language Models in Enterprise: Understanding high-value use cases for LLMs and tailoring applications accordingly is crucial for enterprise businesses to create sticky products, differentiating from generic ChatGPT experiences.
The current landscape for large language models (LLMs) in enterprise businesses is characterized by exploration and experimentation, both vertically within organizations and horizontally across industries. The error lies in not fully understanding the use cases where LLMs can provide the most value. While there's a concern that big labs might dominate the enterprise market, the opportunity lies in designing product experiences around LLMs that cater to these high-value use cases. This approach ensures that products will be sticky and not just another version of ChatGPT. However, it's important to note that while the dominance of a single platform in every vertical was once a challenge for incumbents, social media has shown that this idea is being redefined. The key is to focus on understanding the unique needs of each industry and vertical and tailoring LLM applications accordingly.
Social change social networks: Despite the failure of a standalone social network for social change, enterprises still seek AI partners to generate value, indicating potential opportunities for entrepreneurs in the AI space exploring social verticals
During the early days of social networks, there existed an aggregator platform called Social Actions, which combined inputs from 40 social networks for social change. However, a standalone social network specifically for social change did not succeed, as subsequent social networks like Instagram and Snapchat offered unique content experiences based on mobile and photos or disappearing messages, respectively. Despite this, the ecosystem around social channels remains vast. For entrepreneurs in the AI space exploring specific verticals, there's still significant opportunity. Enterprises are seeking partners who can effectively utilize AI to generate value. If you believe you have a compelling solution, there might be a larger market than anticipated. This concludes today's A.I. Daily Brief. Stay tuned for more insights.