2024's greatest hits on Air Street Press

Our 8 most popular essays from the last year for you to enjoy over the holidays!

We launched Air Street Press in February 2024 to bring all our writing on AI, technology progress, policy, best practice, and meetups together in one place. In that time, both the AI world and our community have continued to accelerate dramatically.

As we reach the end of 2024 and things slow down just a bit, we’re sharing a few of our favorite essays from the past year for you to enjoy over the holidays.

We’ve got a busy schedule planned for 2025, so don’t forget to subscribe!

Back in 2016, I came across a prescient guide on data acquisition strategies for AI start-ups written by Moritz Mueller-Freitag, then co-founder of Twenty Billion Neurons (TwentyBN).

We became good friends and even worked together for some time at TwentyBN. In 2021, the company was acquired by Qualcomm, where he continues to work as Director of Product Management.

A lot has changed in the last 8 years, so we teamed up with Moritz to produce an updated guide that’s relevant for AI-first founders building in 2024. The piece covers LLMs, synthetic data, scraping, open data, as well as some examples of techniques that have worked less well.

The 2024 State of AI Report devoted more space to the output of Chinese AI labs than any of its predecessors. This reflects the striking convergence in performance between US and Chinese labs over the past year. Over the summer, we looked at the strengths and weaknesses of Chinese lab output, how sanctions were and weren’t working, and some of the surrounding policy failures.

This essay was written before OpenAI unveiled o1 (and now o3) and sparked a series of Chinese imitators. We cover these in this month’s installment of the Guide to AI.

Speaking of the creative use of reinforcement learning to improve the performance of LLMs on reasoning tasks…This summer also saw us dive into open-endedness.

We wrote the piece in response to “agentic” becoming the new AI buzzword. It looked at how the poor performance of LLMs on tasks that genuinely tested reasoning (as opposed to knowledge retrieval) suggested that the agentic era wasn’t quite here, while looking at some promising areas of research. The release of o1 a couple of months after we wrote this, and now with the even more impressive reasoning results from o3, has only deepened our excitement for this domain.

We started 2024 with a lack of clarity around fair use and training data for generative AI … and we’re set to end 2024 without clarity around fair use and training data for generative AI. With almost every major lab currently embroiled in copyright-related legal battles, we make the case for compromise. Even if companies prevail in the courts, a number of other forces may still end up pressuring them to settle as amicably as possible.

We draw on past precedent to show why big tech companies rarely emerge from these kinds of social battles unscathed - so they can choose to negotiate a settlement or have one imposed upon them, potentially on much less favorable terms.

At Air Street, we believe strong storytelling is essential to stronger company building. Whether creating a new category, battling incumbents, or facing regulatory uncertainty, hoping that technology will speak for itself just isn’t good enough.

But too often, technical founders are uneasy about it: they view it as either ‘hype’ or ‘dumbing down’. So we made the case for why compelling storytelling is mission critical, why it needs to evolve as a company evolves, some successful case studies, and pitfalls to avoid. We find ourselves regularly referencing this essay…

Foundation models and pragmatism: a trilogy

You get three for the price of one here! Over the course of this year, we used the glamorous subject of foundation model economics to ask a bunch of questions about business models, chips, and AGI.

In our first piece, back in April, we expressed skepticism about the sustainability of frontier model builders continuing to make their most powerful models available at cheaper and cheaper prices. We suggested that we would eventually see a divergence, with the most powerful models being served at an economic price to the people who actually need them, while people with more everyday needs would end up using cheaper or smaller alternatives.

My friend, Eiso Kant, the Co-Founder and CTO of Poolside, was more optimistic when I interviewed him in November. He compared the unit economics of frontier models to the early days of blitz-scaled start-ups like Uber and Lyft - a temporary product of competitive dynamics that would figure themselves out.

We decided to take our theory a bit further and asked what it meant for the chip world.

If we moved away from the bigger is always better paradigm - does that loosen NVIDIA’s stranglehold on the AI chips business? Yes and no.

One of the main pushbacks on our argument about economics is that when AGI comes along, it will unlock such economic gains that our ideas about “capex” and “margin” will become quaint anachronisms.

We’re not so sure that you can wave away basic reality like that - were such a high-powered general model to exist, it would likely come with a hefty accompanying compute tag, from both building the model and running it at scale, especially in the current era of scaling inference-time compute. This would likely render it overpriced and overpowered for … quite a lot of things, which we covered in a reality check.

Sam Altman semi-agrees with us on this, saying in a recent interview that he expected “the economic disruption to take a little longer than people think, because there’s a lot of inertia in society”.

With big tech going nuclear, we took the opportunity to explore the vexed question of AI’s energy demands. We argue that nuclear likely is the solution, but the current buildout pace isn’t fast enough, in part because of regulation. It’s also clear that significant upgrades are needed to transmission infrastructure.

Following on from our essay earlier in the year about AI-first biology, we asked what this would mean for safety.

We’re yet to see evidence that the current wave of text-powered LLMs pose a grave biorisk, but think that AI-powered biological design tools pose more risk. Not only are they optimized for bio-related tasks, they often don’t require the sorts of huge GPU clusters that make actors easy to monitor. We explore their risk profile, some of the barriers to operationalizing them, and some questions that policymakers should be considering.

We’ll have plenty more to come in 2025 - so make sure you subscribe so you don’t miss out!