Data Foundry — Independent Data & AI Consultant on Data Foundry. — Independent Data & AI Consultant

Day Trading with People's Lives

Fri, 27 Feb 2026 00:00:00 +0000

Jack Dorsey laid off more than 4,000 people at Block yesterday — roughly 40% of the company — announced with Q4 earnings, sparking a 24% stock jump. The layoffs were framed as an “intelligence-native” transformation where smaller teams using AI tools would accomplish more.

The hiring binge and the crypto bet

Between late 2019 and 2022, Block tripled headcount from 3,900 to 12,500 during an era of cheap money and crypto enthusiasm. The stock peaked above $250 in mid-2021 but now trades around $67 — a 75% decline. Bitcoin ecosystem revenue fell nearly 20% year-on-year in Q4 2025, undermining one of Block’s three revenue pillars despite Dorsey’s vocal bitcoin maximalism.

Is Your Organisation Ready for AI? A Practical Framework

Sun, 15 Feb 2026 00:00:00 +0000

Most organisations I work with are eager to adopt AI — but few have stopped to ask whether their data foundations are ready to support it. The result is wasted investment, stalled projects, and growing scepticism about whether AI can deliver for their business.

The Five Pillars of Data Readiness

After working with over 40 organisations across industries, I’ve found that data readiness comes down to five pillars:

1. Data Quality

AI is only as good as the data it learns from. If your data is incomplete, inconsistent, or outdated, no amount of model sophistication will save you. Start by auditing your core data assets: customer records, transaction data, operational metrics.

Your CTO and CDO Are Not What You Think They Are

Wed, 11 Feb 2026 00:00:00 +0000

Here’s the uncomfortable bit: the CTO and Chief Data Officer roles are not the top of the engineering and data career ladders. They’re something else entirely. And if you treat them as promotions, you’re going to have a bad time.

I know, I know. You’ve been a senior engineer for years. You’ve led teams, shipped products, wrestled with legacy systems at 3am. Surely the next step is CTO? And for the data folks — you’ve built pipelines, wrangled stakeholders, delivered dashboards that actually get used (rare, I know). CDO must be the finish line, right?

Imagine — Data Centres, but in Space!

Thu, 05 Feb 2026 00:00:00 +0000

You can’t have not seen the latest Silicon Valley obsession — “hey guys, these data centres are getting awfully big and energy hungry down here on Earth, what if we were to build them in space instead?” And everyone who’s anyone is talking about it. Sundar Pichai has Google Research burning cycles on looking into it. The World Economic Forum recently published a piece on it too, putting all of the complexity at the end — not dishonest, but perhaps putting hype before focus.

Advice for Startups

Mon, 26 Jan 2026 00:00:00 +0000

I decided I would share something short and sweet this week: a list of things that I think startups should know. I have been compiling this for a few months. Creating a successful startup is always a bit like a game of Frogger — you have to find a path through the traffic whilst still keeping an eye open for the trucks.

I’m not trying to say I have all the answers here, and in the wise words of Baz Luhrmann:

AI for Boards

Mon, 19 Jan 2026 00:00:00 +0000

I’ve been in a lot of conversations with CEOs and board members lately where the topic naturally turns to AI, and I’ve noticed something interesting: the questions being asked often reveal more about the questioner’s assumptions than they do about the technology itself. There’s a tendency to treat AI as though it were traditional software: deterministic, reliable, and fundamentally understandable. This is a category error, and it’s one that could prove expensive.

When Agents Fail: Compounding Errors in Organisational Systems

Thu, 15 Jan 2026 00:00:00 +0000

There’s a dirty little secret about multi-step agentic systems that nobody particularly wants to talk about. It’s not that they fail - of course they fail, everything fails - it’s that they fail in ways that compound mercilessly, and that this compounding is a feature of the mathematics, not a bug in the implementation. You cannot engineer your way out of it. You can only manage it.

The maths of sequential failure

Let’s start with something deceptively simple. Imagine you have a five-step process, and each step has a 95% success rate. That sounds pretty good, doesn’t it? 95% is an A grade. It’s the kind of number you’d be happy to show a stakeholder.

The Tower and the (public) Square

Thu, 28 Aug 2025 00:00:00 +0000

Let’s take a look at the world from a social point of view for this week. In general, human organisation proceeds in two roughly equi-temporal phases, which are very different in terms of power structures and politics. These phases are already playing out in the new AI frontier, and so past developments might help guide our thinking as to where we go from here.

The Square and the Tower

Historian Niall Ferguson wrote a well thought-out book on this topic, in which he posits that human power structures can be understood to adopt two different phases, each of which is unstable under the right circumstances to the other. We can roughly speaking understand these as:

Pareto Frontiers

Thu, 21 Aug 2025 00:00:00 +0000

For this week’s ramble, I’d like to take you on a tour of how cycles tend to begin and end for the development of new scaling technologies, and why I think things might be different for AI. In particular, I’d like to dig into the VC-powered business model behind our current funding structures for big AI companies like OpenAI, and smaller ones that are using these underlying technologies, and how I foresee things going in the next 12-18 months. I’ll also take a brief look at some important canaries which might suggest if you have a lot of money invested in this technology, it might be a good idea to deleverage a bit!

Rebuilding a Customer-Facing Data Platform for 10x Faster Reporting

Sun, 01 Jun 2025 00:00:00 +0000

The Challenge

The company had plenty of data — but no way to get at it. Reporting was served from a daily cache rebuilt from production databases by the engineering team. If the cache failed, dashboards went dark. If someone needed a metric that wasn’t pre-computed, they filed a ticket and waited.

Customer Success couldn’t build their own analytics. The executive leadership team couldn’t see North Star metrics on demand. And the customer-facing analytics — used by over 100K users — were slow, brittle, and constantly at risk of going down.

Shaping AI Strategy for a Climate-Tech Startup

Sun, 01 Sep 2024 00:00:00 +0000

The Challenge

Aquascope had a compelling mission — using digital twin technologies to monitor and improve water quality — but needed senior AI leadership to turn that vision into a credible technical strategy. As an early-stage startup, they faced the classic challenge: how do you build an AI/ML capability that’s ambitious enough to attract investors and talent, but practical enough to actually deliver?

They needed help across the full spectrum: technical roadmap, team building, IP strategy, fundraising narratives, and embedding a data-first culture — all without the budget for a full-time Chief AI Officer.

About

Mon, 01 Jan 0001 00:00:00 +0000

The short version

I’m Chris — a physicist turned data and AI leader. I’ve spent 20 years going from scribbling equations on blackboards at Cambridge to building ML platforms used by hundreds of thousands of people. These days I help organisations figure out what to actually do with their data, and then I help them do it. You can see the full range of services I offer or browse selected case studies to see how I’ve helped others.

Data Readiness Assessment

Mon, 01 Jan 0001 00:00:00 +0000

Data Readiness Checklist

Mon, 01 Jan 0001 00:00:00 +0000

What’s in the checklist?

A practical, no-nonsense guide to assessing whether your organisation’s data foundations are ready to support AI and advanced analytics. Based on the same five-pillar framework used in our Data Readiness Assessment.

The five pillars

Strategy & Governance — Do you have a documented data strategy? How mature is your governance?
Infrastructure & Technology — Is your tech stack modern enough to support data-driven operations?
Data Quality & Analytics — Can you trust your data? Are you getting insights beyond basic reporting?
Team & Culture — Do you have the right people and a culture that values evidence-based decisions?
AI & Change Readiness — Is your organisation prepared for AI-driven transformation?

What you’ll get

A self-assessment checklist covering all five pillars
Red/amber/green indicators for each area
Concrete next steps based on your current maturity level
A prioritisation framework for where to invest first

Get the checklist

Enter your email below and we’ll send it straight to your inbox.

Get in Touch

Mon, 01 Jan 0001 00:00:00 +0000

Media & Appearances

Mon, 01 Jan 0001 00:00:00 +0000

Privacy Policy

Mon, 01 Jan 0001 00:00:00 +0000

Last updated: March 2026

Who We Are

This website is operated by an independent data and AI consultant. For questions about this privacy policy or your data, please use the contact form.

What Data We Collect

Mailing List

When you subscribe to our mailing list, we collect your email address. We store this alongside a unique identifier (UUID), your subscription status, and the date you subscribed.

Data Readiness Questionnaire

When you take the questionnaire, we store your answers and score linked to a random UUID. This data is anonymised by default. If you choose to receive an email report, we also collect your email address, which is stored separately from your questionnaire answers and linked only by UUID.

Services

Mon, 01 Jan 0001 00:00:00 +0000

Data Strategy & Architecture

Not sure where to start with data? Already collecting data but not getting value from it? I help organisations define a clear data strategy and design the architecture to support it. Try my free data readiness assessment to see where you stand.

Data maturity assessment and roadmap
Modern data stack design (warehouses, pipelines, orchestration) — see how I rebuilt a customer-facing data platform for 10x faster reporting
Data governance and quality frameworks
Tool selection and vendor evaluation

Discuss data strategy

Machine Learning & AI Systems

Turn your data into predictions, classifications, and recommendations that drive business value. I build production ML systems, not just notebooks.

Working with Me

Mon, 01 Jan 0001 00:00:00 +0000

Thanks for your interest in working together! To ensure our collaboration is productive and respectful of everyone’s time, I’ve established these guidelines based on what works best for focused, high-value consulting engagements.

Engagement Structure

Minimum Engagement: 4 Hours

I work in minimum blocks of 4 hours (half-day). This isn’t about maximising billing, it’s about creating the space for meaningful work. Shorter sessions often lack the depth needed to make real progress, and the administrative overhead doesn’t justify smaller commitments.