Startups

Themes from AI Con 2024: data moats, stealthy AI use, Chatty’s UX revolution, and enduring fundamentals.

Building AI is hard and expensive. For most companies, the path to AI success is building with third-party AI interns and cheap AI cogs.

Highlights and lessons from my solo expertise biz, including value pricing, fractional cash flow, and distractions from admin & politics.

Exploring why universal advice on startup data stacks is challenging, and the importance of context-specific decisions in data infrastructure.

Reflections on building a solo expertise business in Data & AI, focusing on climate tech startups. Lessons learned from the first year of transition.

Video and key points from the second part of a webinar on a startup’s first data hire, covering tips for defining the role and running the process.

Video and key points from the first part of a webinar on a startup’s first data hire, covering data & AI definitions and high-level recommendations.

Asking identical questions about my MagicGrantMaker idea yielded near-identical responses from the top chatbot models.

Questions to assess the security posture of a startup, focusing on basic hygiene and handling of sensitive data.

Takeaways from an interview with Patty McCord on The Startup Podcast.

Questions to assess the quality of tech stacks and lifecycles, with a focus on artificial intelligence, machine learning, and analytics.

Since we’re far from a utopia where data issues are fully handled by AI, this post presents six questions humans can use to assess data projects.

Expanding on the startup health check question of tracking Kukuyeva’s five business aspects as wide events.

Questions that prospective data specialists and engineers should ask about development processes before accepting a startup role.

Three essential questions to understand where an organisation stands when it comes to Data & AI (with zero hype).

Eight questions that prospective data-to-AI employees should ask about a startup’s work and data culture.

Ten questions that prospective employees should ask about a startup’s team, especially for data-centric roles.

Fourteen questions that prospective employees should ask about a startup’s business model and product, especially for data-focused roles.

Reflections on what it takes to package expertise and deliver timely, actionable advice outside the context of employee relationships.

Reviewing the areas that should be assessed to determine a startup’s opportunities and challenges on the data/AI/ML front.

The story of how I joined Work on Climate as a volunteer and became its data tech lead, with lessons applied to consulting & fractional work.

Classifying startups as ML-centric or non-ML is a helpful exercise to uncover the data challenges they’re likely to face.

Two stories of getting AI functionality to production, which demonstrate the risks inherent in custom development versus starting with a no-code approach.

First post in a series on building a minimum viable data stack for startups, introducing key definitions, components, and considerations.

Advice for hiring a startup’s first data person: match skills to business needs, consider contractors, and get help from data people.