AI and Business
Resources for business
Ed Nevraumont, senior advisor at an investment firm, writes the Marketing BS newsletter and keeps a detailed spreadsheet of useful AI tools for companies including sales support tools like Collectivei.com ($9K/year)
TheNeuron lists the 27 top AI applications for business
Startups
The Generalist lengthy summary of AI startups Jul 2023
and Sequoia’s Top 50 for 2024
Crypto
Education
Ryan Watkins on update your syllabus for ChatGPT suggests asking students to track changes while editing the chatGPT response.
Employment
For software development impact, see Developers and what they’re up to
Management
Ethan Mollick speculates on how AI-based organizations will look Reshaping the tree: rebuilding organizations for AI with examples of how his own Wharton startup works. Team members generate their project summaries and questions and let AI summarize and provide suggestions before a meeting.
Because AI is like a person, he thinks individual teams should develop their own best practices for using AI.
Apr 9 Tomas Pueyo AI and the Future of Work (8 minute read)
Anthropic’s latest AI, Claude, achieves high scores on Mensa tests, signaling proximity to self-improving AI and posing both opportunities and existential risks. Machine learning is already displacing jobs, as evident at Klarna, where a customer service AI replaced 700 agents, hinting at an accelerating trend in automation. Duolingo’s recent layoffs due to AI’s translation capabilities further underscore this shift, emphasizing the growing impact of AI on the future of work.
Advice for AI Company Builders
“AI is in the solution space, not the problem space”, says As Smart Bear in “AI startups require new strategies: This time it’s actually different”. There’s no such thing as an “AI market; incumbents have most of the advantages because AI is a straightforward upgrade to existing processes. Standard”Disruption Theory”, says incumbents ignore new technologies because they’re not as good as what they already have. But that doesn’t apply here, because the big companies can find immediate use for AI. This makes it tough for startups, but:
Still, the right way to analyze this is not to say “the AI market is big and growing” but rather: “Here is how AI will transform this existing market.” And then: “Here’s how we fit into that growth.”
Sangeet Paul Choudary explains that generative AI is a good way for startups to lose
Unlike the transition from on-prem to SaaS, he says, when incumbents needed to rewrite everything to compete with the entirely new way of work pioneered by companies like Salesforce, generative AI is relatively easy to incorporate into existing technology stacks. Instead, he proposes that startups focus either on “model-only” applications like MidJourney, or on “workflow+model” vertical applications like Harvey for the legal industry.
And Ashu Garg from Foundation Capital offers these suggestions based on interviews with leaders:
Co-founder of Databricks Matei Zahari says instead of using LLMs like a fancy database:
leverag[e] LLMs for their language analysis and generation skills while sourcing factual, up-to-date information from trusted external sources through retrieval mechanisms and tool calling.
and Robert Nishihara, Co-founder & CEO of Anyscale pushes open source
With the rapid advancement of AI technology, it won’t be long before open-source models are “good enough” for the majority of business tasks. The natural progression from here is to develop smaller, task-focused models using proprietary company data. As Robert puts it: “You’re going to need small, fast models. And task-specific models have a huge advantage when it comes to cost and speed because you can achieve the same quality with a smaller model if it’s specialized.”
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