Welcome!

I’ve written quite a bit over the years, but hardly published anything. I decided it’s worth sharing with whomever is interested. I imagine it’s mostly useful for:

  • People considering joining our mission at Salv. My co-founder Taavi has a tons of his thoughts online but I’m harder to find. This is a start.

  • Salvers who are thinking about how to scale up Salv. A lot of what I captured here I learned helping Wise to scale.

Personal growth

$10/hr or $10,000? - thoughts on how to value your time, maximise your impact and have a great life. [4 min read]

Pushing yourself to take big risks - reflections on when I took 600 people down a radically different path that ended up creating a key ritual in Wise’s success. [4 min read]

How to land your dream job - An unconventional approach to get a more interesting role, at a cooler company, with nicer people, with a lot less stress. [16 min read]

The most important 0.13% of my month - We each have 730 hours in a month. I transform how much I focus on what’s important to me by making Personal Commitments using just one hour a month. [8 min read]

Building a great organisation

Self-leadership & Wise - A careful reading of Frederic Laloux’s Reinventing Organisations and applied to Wise (TransferWise) circa 2015. 3 part series [8+5+15 min reads]

Prophets & Professionals - A simple model that describes why the culture in startups change as they grow. A useful framework for how to think about the tensions scaling up a startup [6 min read]

Autonomous teams at scale - how Wise learned how to scale autonomous teams and avoid creeping bureaucracy. [10 min read]

Scooters are fun. But… - On why you should choose a company with a mission where you're convinced you're making the world a better place. And on how we work at Salv. [11 min read]

The definitive guide to cross-team guilds - What they are, how to set them up and why a group of cross-team volunteers, working part time, can help an organization achieve far more than traditional teams. [14 min read]

Why we don’t have a BI team at TransferWise - How I set up the analytics team at Wise (circa 2015), and how it helped Wise scale faster. [6 min read]

So you think you’re data driven - Every team has data but many either don’t know how to use it or misuse it for planning and setting KPIs/targets/OKRs. This guide can help. Circa 2017. [7 min read]

Investing

Investing like it’s 1939 - How we’re returning to an old paradigm of investing similar to 80 years ago [10 min read]

Beyond the Beach: Seychelles in the Era of Global Sovereignty - how this enchanting beach paradise could transform into a global powerhouse. [10 min read]

Playing with AI

The Art of AI T-Shirts: Your Way to One-of-a-Kind T-Shirts - A step-by-step guide to creating affordable, custom tees with the latest print-on-demand tech. [6 min read]

Startup ideas

VC-Backed Careers - Making the case for investing serious cash into people’s careers, VC style [6 min read]

YSAC - You Suck At Coding - There’s a huge opportunity to accelerate how people learn to code [6 min read]

Compensation

A tech alternative to commission sales - How a small app and process could align incentives better to achieve higher sales team output. [5 min read]

Learning / teaching / improving education

Lean in to learning - an idea to make education more useful. I envisioned it during the first weeks of Covid but looking back it seems increasingly relevant, as remote learning becomes default. [2 min read]

Teachers of tomorrow’s leaders - an comprehensive plan for a conference that pairs up top teachers and leaders from the tech sector. [5 min read]

Anti-money laundering (AML) & Financial Crime

Why we couldn’t ignore AML - Why, of all the problems in the world, we at Salv decided to focus on AML. And why it’s such an interesting problem to work on. [6 min read]

Criminals are fast. Your AML must be faster - why speed and fast feedback loops are the critical missing ingredients that are missing in most AML teams. [5 min read]

Can Machine Learning save AML? - About the challenges of applying machine learning techniques in the real world [16 min read]. Shorter version [6 mins]