Fulkerson Advisors

Rethinking AI Caching: Gemini vs Anthropic - The Race for Efficient Context Management As AI models grow in size and capability, efficient context management becomes crucial. Google's Gemini and Anthropic's Claude are pioneering innovative caching strategies that could reshape how we interact with AI. Let's dive into the implications. Key Similarities: - Both aim to reduce costs and latency for long prompts - Caching enables more efficient use of large context windows - Particularly useful for conversational agents and document processing Unique Features: Google's Gemini 1. Direct PDF processing (up to 1000 pages!) without pre-processing 2. Multimodal capabilities within cached context 3. 48-hour persistence of uploaded files Anthropic's Claude 1. Tiered pricing model for cache writes vs. reads 2. Available across multiple Claude models (Sonnet, Haiku, Opus coming soon) 3. Emphasis on many-shot prompting and fine-tuning via examples Implications 1. Traditional RAG Disruption: These caching strategies could potentially make traditional vector store-based RAG obsolete for smaller datasets, impacting the ecosystem of RAG-focused startups. 2. Democratizing Large Context Windows: By reducing costs, we may be entering an era where even small businesses can leverage massive context in their AI applications. 3. Complexity Trade-offs: While caching simplifies some workflows, it may introduce new complexities in prompt engineering and context management. 4. Multimodal Applications: Gemini's ability to cache and process multimodal data could lead to novel applications, such as AI-guided virtual tours with detailed visual memory. 5. Privacy Considerations: The enhanced personalization enabled by caching must be balanced against data privacy concerns, especially given the 48-hour persistence of files in Gemini. 6. Competitive Landscape Shifts: These innovations may prompt responses from other AI players, potentially leading to a "caching war" similar to the "token window size war" we've witnessed. As we navigate this rapidly evolving landscape, it's clear that context caching is more than just a performance optimization—it's a fundamental shift in how we architect AI systems. We may be moving towards a future where AI models become more like databases, with persistent, queryable knowledge states. This could lead to unexpected breakthroughs in AI capabilities and novel use cases. How do you see these caching strategies impacting your projects or industry? What potential applications or challenges do you foresee as AI systems become more efficient at managing large contexts? s/o to Prompt Engineering: https://lnkd.in/d77YXZZD

Pietr Levels is a Dutch entrepreneur, developer, and digital nomad known for creating successful online businesses like Nomad List and Remote OK. With a unique approach to work and life, Pietr has become an influential figure in the startup world, challenging conventional wisdom on business, productivity, and lifestyle design.?? ?? Here are some key takeaways from his Lex Fridman podcast episode?? ?? 1. Embracing "outdated" tech: Pietr uses PHP, jQuery, and vanilla JavaScript for most projects. He argues this allows for faster development and iteration compared to modern frameworks, challenging the constant push for new technologies.?? ?? 2. Radical deployment strategies: Pietr deploys directly to production, often without staging environments. He claims this leads to faster problem-solving and user feedback, forcing more careful coding practices.?? ?? 3. Solo entrepreneurship and automation: Pietr prefers working alone and automates most aspects of his businesses. He argues this approach is more efficient than hiring teams, allowing for faster decision-making and execution.?? ?? 4. Minimalism as productivity hack: Pietr reduced his possessions to under 100 items. He credits this extreme minimalism with increased focus and productivity, arguing that material possessions often don't bring lasting happiness.?? ?? 5. Critique of European business culture: Pietr claims European regulations and culture stifle innovation. He advocates for more "third spaces" like cafes for people to work and innovate, highlighting the age gap between top European and American companies.?? ?? 6. AI and the future of work: Pietr is building AI-powered tools like Photo AI and Interior AI. He sees great potential in AI for automating various business aspects, but warns about challenges in monetizing certain AI applications.?? ?? 7. Unconventional productivity methods: Pietr uses caffeine-induced anxiety as a tool for intense focus. He advocates for extreme work sessions, sometimes coding 20+ hours straight, challenging conventional wisdom about work-life balance.?? ?? 8. Building in public: Pietr openly shares revenue numbers and business strategies on social media. He believes in transparency and letting users see the development process, which has contributed to his online following.?? ?? Thoughts?

Rethinking RAG: The Hidden Costs and Optimization Challenges of Retrieval-Augmented Generation As AI continues to evolve, Retrieval-Augmented Generation (RAG) has become a cornerstone of many production systems. But are we overlooking critical aspects of its implementation? Here are some thought-provoking insights: The Embedding Paradox: ? Cheap to compute, expensive to store ? 1 billion embeddings can require 5.5TB of storage ? Monthly storage costs can reach $14,000+ for high-dimensional embeddings The Quantization Revolution: ? Binary quantization can reduce storage costs by 32x ? Only 5% performance reduction in some cases ? Potential for 25x speed increase in retrieval Model Size Matters: ? Larger embedding models are more resilient to aggressive quantization ? Parallels the behavior of quantized LLMs The Open-Source Advantage: ? Vector stores like Qdrant now support scalar and binary quantization ? Up to 99% accuracy preservation with 4x compression for scalar quantization The Tradeoff Triangle: ? Balancing accuracy, speed, and storage costs ? No one-size-fits-all solution Key Questions: ? Are we overengineering our RAG systems? ? Could simpler, quantized models outperform complex ones in production? ? How will advancements in embedding compression reshape AI infrastructure costs? As we push the boundaries of AI, it's crucial to look beyond raw performance and consider the full spectrum of tradeoffs in our systems. What's your take on the future of RAG optimization? s/o to Prompt Engineering https://lnkd.in/d5uKryzQ

Eric Schmidt's recent commentary on the future of AI offers a glimpse into where the industry is heading—and it’s not what most people expect. His perspective, shaped by decades of experience at the highest levels of tech, challenges many of the assumptions driving today’s AI discussions. 1. The Decline of Open Source in AI: Schmidt believes the gap between the leading AI models and the rest of the field is widening, not narrowing. He’s increasingly skeptical that open-source efforts can keep pace with the immense costs required to develop top-tier AI. Mistral, a promising AI startup, is reportedly considering closing its third model due to these overwhelming costs. If Mistral’s shift to a closed model materializes, it could mark a turning point in the AI industry, where the financial demands of staying at the forefront force companies to reconsider the open-access model that has fueled innovation for so long. 2. AI as a Geopolitical Weapon: He views the race between the U.S. and China for AI dominance as more than just a competition between tech giants—it’s a national security imperative. With the U.S. holding a 10-year lead in chip technology, Schmidt underscores the importance of maintaining this advantage. The AI race, in his view, is as much about global influence as it is about technological advancement. 3. Canada’s Critical Role: While much of the AI conversation focuses on the U.S. and China, Eric Schmidt highlights Canada as a key player due to its abundant hydroelectric power and strong AI research community. He suggests that Canada could become a crucial partner for the U.S. in maintaining its AI infrastructure, especially as the demand for energy-intensive AI models grows. 4. The Financial Realities of AI: He points out that the capital required to develop and maintain cutting-edge AI models is astronomical. This is leading to a shift where even companies with a history of supporting open source may have to turn to closed ecosystems to sustain their operations. The sheer scale of investment needed could fundamentally change the dynamics of the software industry. 5. NVIDIA’s Unique Position: Schmidt explains that NVIDIA’s dominance isn’t just about their hardware—it’s the entire ecosystem they’ve built around CUDA. This software ecosystem, optimized exclusively for NVIDIA’s architecture, has made it difficult for competitors to catch up. In Eric Schmidt’s view, this gives NVIDIA a near-monopoly on the tools needed to push AI forward. Thoughts?

In today's rapidly evolving business landscape, AI and agentic workflows are no longer just buzzwords—they're critical tools for maintaining competitive edge. As a leader in a traditional industry, you face a clear choice: harness these technologies effectively or risk falling behind. Over the past year, we at Fulkerson Advisors guided over 10 diverse companies through the complexities of AI implementation. This experience has revealed several key challenges that every organization must address: 1. Error Mitigation: How will you prevent small mistakes from cascading into systemic failures? 2. Stability vs. Flexibility: What mechanisms will ensure your AI systems remain reliable while adapting to new information? 3. Collaborative Innovation: How will you foster a culture of continuous improvement and knowledge sharing? These aren't hypothetical concerns—they're real obstacles that can make or break your AI initiatives. Addressing them requires a strategic approach tailored to your specific business context. The potential benefits are substantial: 1. Streamlined operations through automation of repetitive tasks 2. Enhanced decision-making powered by data-driven insights 3. Unlocking new revenue streams and business models However, achieving these outcomes demands more than just implementing new technology. It requires a fundamental shift in how you approach problem-solving and value creation within your organization. Successful AI integration isn't about replacing human intelligence—it's about creating a symbiosis between human expertise and machine capabilities. This synergy, when properly executed, can drive unprecedented levels of efficiency and innovation. The path forward isn't always straightforward. It demands rigorous planning, ongoing assessment, and a willingness to pivot based on results. But for leaders who commit to this journey, the rewards can be transformative. Your competitors are likely already exploring these technologies. The question is: will you lead or follow in this new era of AI-driven business?