Carpe Exponentia
Seize the Snowball
The capability of any technology tends to follow an s-curve over time, starting out with the basics, but growing extremely quickly in a short amount of time after an inflection point which starts with an exponential \"hockey stick\" of fast-paced improvements. Over time, the pace of change slows down and incremental improvements continue… until the next hockey stick inflection point.
You never really know where you are on that curve, and the only view is backwards. Some might claim that the improvements in AI are slowing down, but far more would guess we are in the high-gradient part of the curve, with new techniques, models, and approaches appearing almost every day. Indeed, by the time you finish reading this (and definitely by the time I finish writing it), there will be new capabilities worthy of our attention.
As counterintuitive as it may seem, for my money it's more likely that we are in the early phase of the s-curve and that in spite of an extraordinary rate of invention and improvements - we haven't hit that hockey stick inflection point yet.
An Intimidating Prospect
How do you spot the inflection point if you can only look backwards? With limited resources, how do you time it just right? Too early, and there is an opportunity cost as you could have spent your resources on something else. Too late, and you miss out on growth and the opportunity to shape the curve to your advantage.
Organizations that place their bets well will be able to ride the capability curve all the way up. But place them incorrectly, and there is a chance you'll be playing catch up when the tide eventually turns. Deciding where to invest poses challenges and opportunities.
The bad news: these events are rare, unique, and hard to spot. Almost by definition, the techniques that have driven improvements so far are unlikely to deliver exponential future growth on their own.
The good news: there are some tell-tale signs. Let's take a look.
Snowball Capabilities
The most common indicators to look for are those which indicate a technique has the potential to start a snowball rolling - a set of improvements which compound on one another to grow the absolute capability in aggregate. This cascade changes the trajectory of capability more than any single improvement. There are usually three elements.
1 - the pre-requisite: Discovery of new technology with improved utility & performance. Simple tools to plant seeds in soil. Browsers. Smart phones. Very few trajectory-defining moments don't start with a meaningful new invention. These are often very general technologies because the opportunity to specialize doesn't exist without the invention existing in the first place. These inventions are a pre-requisite for growth; without it, there is no opportunity at all. They are the cold weather that precedes the snowfall.
2 - the x-factor: Deeper specialization for individual tasks. Building and using the initial invention to provide deep specialization: different tools for planting different seeds contributes to much-improved crop yield. Web-based SaaS applications specialized in oh-so-many different directions covering niche and broad areas. Smart phone apps gave rise to entirely new categories of increasingly specialized products. Useful, fun, remarkable even, although in isolation, they are beautiful and unique snowflakes.
3 - the combinator: Increasingly collaborative systems of work & play. Through efficiencies, farms could combine the use of specialized tools to grow more diverse crops, and in turn create much larger farms. The growth of one crop compounded the other. SaaS applications grew in popularity and integrated through APIs, growing the web's usefulness beyond the impact of any single web app. And for mobile devices, apps specialized to the point of becoming entirely personal (through photos, calendars, social media, etc.), and interoperate through content, sharing, memes, in ways which make every app (and the device they run on) far more useful than the sum of its parts. This is the snowball and the hill and the push-over-the-edge.
In turn - these collaborative, specialized capabilities combine together to reveal a new universal truth, insight or invention and the cycle continues. They build on each other to something significant and new.
The Autonomy Snowball
Let's play out our model with respect to generative AI.
Pre-requisite: Foundation models fit the bill perfectly for a new technology with improved utility and performance. It is the frontier models which have proved the most remarkable in debuting a software component which can perform reasoning and integration in new (albeit, limited) ways. Models themselves are seductive (and useful and fascinating!), but they are likely the cold front before the snowfall, not the snowball itself.
X-factor: Agents - AI systems which use generative AI models to work toward an objective, automatically - offer deep specialization for individual tasks. Agents can develop specialized strategies and plans to move toward a stated goal and then execute that plan, automatically.
Want to add a new feature to your specific app? There's an agent for that. Need to migrate an codebase from one version of Java to another? There's an agent for that. Want to optimize your supply chain? Progress your research? Modify your biomolecule to reduce toxicity? There's an agent for that (or there soon will be).
Combinator: Multi-agent systems - systems which can integrate, orchestrate, and collaborate with other agents - are maturing very quickly. Most are not ready for the limelight just yet, but it's only a matter of time. At the point at which they are capable of combining agents into more complex systems, is the point at which the capability derived from any individual agent rises - and with it, the utility and value of all agents, and the models they run on.
With these three elements in place, agents (a.k.a. \"agentic systems\"), seem well poised to drive similar compounding capability growth as SaaS apps did for the web, or apps did for mobile. I think this makes a pretty compelling case for experimentation, prototyping and investment today (Amazon Bedrock is a great place to do this, naturally). The agents you create will be useful immediately, and as multi-agent systems mature, you'll be ready to compound their value - exponentially.
Seize the snowball.
\uD83C\uDDE8\uD83C\uDDE6 This post is based on a short talk I gave at the Collision conference in Toronto this morning. Thanks to Nick Walsh and the Amazon Bedrock team for their help.