Business

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.

On moving away from weekly blog posts in favour of deeper inconsistent articles and LinkedIn engagement.

Podcast chat on the reality of Data & AI and my consulting focus: Helping climate & nature tech startups ship data-intensive solutions.

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

My views on separating AI hype and bullshit from the real deal. The general ideas apply to past and future hype waves in tech.

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

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

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

Summary of the main messages from the book The Passion Economy by Adam Davidson.

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).

Overview of the book The Economy of Algorithms by Marek Kowalkiewicz.

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.

An high-level overview of things I learned from Justin Welsh’s LinkedIn Operating System course.

Jonathan Stark makes a compelling argument why you should have the three Cs before quitting your job to go solo consulting.

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

When focusing on a market segment defined by personal beliefs, it’s often fine to position yourself as a generalist in your craft.

Repeated exposure to media personas creates relationships that help justify premium fees.

With the commodification of data scientists, the problem of positioning has become more common: My takeaways from Genevieve Hayes interviewing Jonathan Stark.

It turns out that problems like finding a niche and defining the ideal clients are key to any solo business.

While I found the story of Gumroad interesting, The Minimalist Entrepreneur seems to over-generalise from the founder’s experience.

A summary of the second chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

A summary of the first chapter of Rob Walling’s Start Small, Stay Small, along with my thoughts & reflections.

Being a data scientist can sometimes feel like a race against software commodities that replace interesting work. What can one do to remain relevant?

Updating my definition of data science to match changes in the field. It is now broader than before, but its ultimate goal is still to support decisions.

There’s a lot of misleading content on the estimation of customer lifetime value. Here’s what I learned about doing it well.

Seven common mistakes to avoid when working with data, such as ignoring uncertainty and confusing observed and unobserved quantities.

It seems like anyone who touches data can call themselves a data scientist, which makes the title useless. The work they do can still be useful, though.

Hiring data scientists prematurely is wasteful and frustrating. Here are some questions to ask before you hire your first data scientist.

Progress since leaving my last full-time job and setting on an independent path that includes data science consulting and work on my own projects.

Update on BCRecommender traction using three channels: blogger outreach, search engine optimisation, and content marketing.

Ranking 19 channels with the goal of getting traction for BCRecommender.

Discussing the hierarchy of needs proposed by Jay Kreps. Key takeaway: Data-driven algorithms & insights can only be as good as the underlying data.