I'm a Fractional CTO and AI Engineer.
I help founders and companies figure out what software is worth building, and what it takes to build it. I can handle everything from first assessment to running the technical side day-to-day.
Most engagements start with a conversation. See how I work.
I've been building software for 15+ years (search systems, ML platforms, credit engines, recommendation systems) across biotech, fintech, and martech. The implementation layer is getting cheaper every quarter, which makes judgment about what to implement, when, and with what tradeoffs, more valuable than it used to be. That's where most of my time goes now.
It's funny how the fundamentals of business don't change, even though so much around it is in turmoil. I'm talking about AI and applying it to businesses, and the challenges today are the same ones you'd face ten years ago when you wanted to optimize some part of your work: the hard part is seeing the work. It's asking, habitually: what here could be done better?
Every few months there's a new round of arguing about AGI, and we talk about "timelines" as if the thing is definitely coming and the only open question is when, while still not really agreeing on what it would even mean if it arrived. The most recent flare-up is Demis Hassabis vs Yann LeCun, where LeCun says general intelligence is "complete BS" because human cognition is specialized for the physical world and our sense of generality is an illusion produced by being unable to imagine what we're blind to, and Hassabis disagrees, saying brains are "approximate Turing Machines" capable of learning anything computable, pointing to chess and mathematics and civilization as the evidence.
I think they're both wrong, and not in the boring "truth is in the middle" way. They're having the wrong argument entirely. Both of them treat intelligence as a function, something that takes inputs and produces outputs and can be measured on benchmarks and in principle copied between substrates and scaled up. They disagree about whether the function is "general" or... [more]
Most programmers will tell you about code smells, that ick you get from looking at messy code where you can't always say why it's wrong but you know something is off. And the intuition isn't unique to programming either — mathematicians talk about "elegant" proofs, physicists get suspicious when their equations grow baroque, and the whole lineage from Occam's razor to Kolmogorov complexity points at the same thing, which is that compression and understanding are intimately related. If you've actually grokked a problem you can express its solution minimally, and if you can't, you're probably still confused and compensating somehow.
Which brings me to LLMs. I use them all the time and they've genuinely changed how I work, but they still generate code that smells. Lots of bloat, defensive boilerplate, unnecessary abstractions, cargo-culted idioms, code that looks like code because it was trained on code that looked like that.
Venkatesh Rao has this idea I keep coming back to, that LLMs are cameras, not engines. We think they're generating something, but really they're photographing... [more]
Thinking more about my earlier note on LLMs.
LLMs interpolate, they don't compose. They can't combine ideas into genuinely new structures because they have no mechanism for compositional reasoning, just proximity matching. Real composition might require consequences that happen to a continuous self, whereas LLMs rebuild from scratch each token. Humans can compose from sparse data.
Show a kid one lever, they'll start using sticks to flip things. LLMs need many examples to build a statistical manifold around "lever-ness." It's about composing from principles extracted from single experiences vs interpolating across massive experience-spaces. Just different kinds of cognition, not different points on the same scale.
Your periodic reminder: the big story isn't AI automating our jobs, it's AI revealing entirely new territories of thought, creativity, and opportunity
Someone recently tweeted about being in awe of LLMs while sensing they're missing something "ineffable yet essential" about general intelligence. I think maybe the fact that it's ineffable is a hint. That in fact there are no such things as general intelligences, and it's a whole slew of context-dependent, dynamic, embodied processes that emerge from continuous interaction between specific systems and their environments.
What irks us about LLMs is they're unreliable in a very inhuman way. When they fail, they violate our expectations of how minds work. We expect intelligence to come bundled with self-awareness, doubt, the ability to know what you don't know. LLMs show us intelligence decoupled from understanding, and that just doesn't feel right.
So I think it's a category error to compare human intelligence to LLMs, and asking "when will LLMs achieve human-level intelligence" is the wrong question. Maybe more interesting: what novel forms of cognition are these systems showing?
Visited teamLab in Tokyo.
Made me think there's a whole lot to be explored in wonder/transcendence/psychedelic territory - especially for people not keen on diving into LSD or DMT.
The idea is to get a taste of "ecodelic insight" (see Richard Doyle's Darwin's Pharmacy), that moment when you get that you're not just an individual but a piece in a complex ecosystem.
Like a "grokking of Gaia", where you suddenly feel part of a much larger, interconnected life system.
May require a new approach to meaning, where you need to let go of the need to interpret and control and let the experience stand for nothing but itself (Leary's "non-symbolic patterns").
Both Germany and Japan care about efficiency, but their approaches are shaped by different cultural backgrounds.
Japan: collectivist, values social harmony and consensus. Technology is adopted if it enhances collective well-being. The restrictive aspects come from a desire to maintain social cohesion.
Germany: individualistic, focus on precision engineering and planning. Less emphasis on collective consensus and more on functionality and standards.
Take the Shinkansen and Autobahn as metaphors:
Shinkansen: collective harmony through rigid protocols. There's a place and protocol for everything, and individual deviations aren't encouraged.
Autobahn: individual freedom within a framework of expertise and shared rules. Go as fast as you'd like on good roads, as long as you abide by the rules.
As a collective, humanity has made itself healthier, safer, more equal, and more knowledgeable. We’ve built institutions that level the playing field. We’ve peeked into the microscopic world to make ourselves live longer, and we’ve peered beyond our blue dot and gasped at the weirdness of it all.
Our ability to understand and control the world has meant that, so far, we’ve steered away from the near-inevitable wave of rolling genocide that targets other species—ones that rely only on their genetic knowledge.
What we have achieved is only overshadowed by the possibilities of what we can do next. Yet there is a growing pessimism among some in our society. When faced with challenges like climate change, increased consumption in a world with limited resources, loss of biodiversity, or pollution, we jump to blaming ourselves and our way of life. If only we slowed down economic growth, limited air travel, and had fewer babies, everything would be fine.
The problem with this line of reasoning is that it achieves very little. We will always be faced with the question of how to plan for an unknowable future. We will never be able to afford to sit back and hope for... [more]
I thought Netflix's The Social Dilemma was bad.
It portrays people as helpless automata under control of the algorithm, and massively overplays the influence of these tools. It is a manipulative horror film wrapped in the appearance of a serious documentary, using the same techniques in filmmaking that it calls evil.
By all means these are relevant warnings, and how to best balance information consumption with other aspects of life is important. But none of this is new or unique to social media.
We will never live in hermetically sealed bubbles where our information diet is exactly what the doctor ordered. The world is messy and takes critical thinking to navigate. Tech isn't the devil and it isn't the saviour either.
Fearmongering is counterproductive.