Chris Simpson

The recipe: 1. Solve Search. 2. Use it to solve everything else.

Never a dull moment in AI. Current market summary: * Big, bigger, biggest: biggest VC round ever (OpenAI), biggest seed round (Safe SuperIntelligence, $1B), biggest acqui-hire (Character, $2.7B), biggest super computer (xAI's Colossus, etc) * Rich valuations all around: NVIDIA at 65 P/E (vs 20 average), several startups at multi-billion valuations pre-revenue (sometime pre-product). Can argue whether deserved or not, but lots of growth expectations built in * Fortunately, this generation of AI startups is growing faster than their SaaS cousins ever did (source: Stripe data) * Despite concerns about over-building of AI infra, Mag 7 companies in recent earnings all attest to very strong (or even "insane") levels of demand for AI. Numbers are comparatively small by Mag 7 standards, however. * Big question around continued progress of AI research/performance, or not. Concerning reports that GPT-5 may not show as much exponential progress as prior versions did. Question around reasoning for LLMs. Fuziness around what AGI and ASI actually mean. Optimistic view is that great products/companies can be built deploying what we currently have, even if progress slowed down dramatically * Foundation/frontier LLM market seems to have crystallized around a few early winners (unclear whether any can build long term differentation). Open question about the impact of Llama 4 and open source on commercial API providers. Plenty of room still for specialized LLMs, per modality (audio, video) or vertical (bio, materials sciences, etc) * AI tooling layer (orchestration, evaluation, RAG, etc) interesting, but crowded and fast evolving * AI consumer application layer wide open. Every paradigm shift saw the rise of dominant consumer companies. Who will build the Uber of Generative AI? * From a customer perspective, enterprise (Global 2000) adoption of AI is still very early: mostly low hanging fruit kind of use cases: chat (GPT/Azure), search (Glean), code (Github CoPilot), or easy to deploy applications (Synthesia for video). For anything more company-specific or industry specific: consultants are the big winners so far. Next: enterprises will go from PoC to production for custom use cases. * From a vendor perspective, AI (intelligence wrapper) is the new SaaS (database wrapper). Furious pace of building across use cases (AI for finance, sales, marketing, custome service, HR etc) and verticals (law, accounting, banking, etc). New business models ("sell the work" vs just selling software) * Brave new world ahead of us. In the enterprise, could mean AI agents connecting with each other to orchestrate tasks end-to-end. Tomorrow's enterprise could need a lot less people and jobs would evolve to managing a few humans and a lot of AI.

ColPali is changing the game for PDF retrieval by eliminating the need for OCR and chunking methods 🚀 Inspired by ColBERT’s success with text, ColPali splits an image of a document into patches, which are then processed through a vision LLM called PaliGemma. The embeddings for each patch retain contextual information, similarly to text embeddings in methods like ColBERT. During retrieval, user queries are embedded in the same space, and then compared to document patches using the MaxSim operator. ColPali recipe POC @weaviate_io: github.com/weaviate/recipes/… ColPali paper: arxiv.org/abs/2407.01449 As always, shoutout to the awesome @drdannywilliams for the help! 💙