Imagine a lifeguard calculating the quickest path to a drowning swimmer.
Renowned physicist Richard Feynman used this scenario to explain the principle of least action – the idea that the most efficient path is rarely all-or-nothing.
The lifeguard can run faster on land than swim in the water. So, what's the quickest way to reach the swimmer? There are a few options:
Straight Line (Shortest path): The lifeguard could take a straight line to the swimmer, which is the shortest distance. However, this path requires more swimming, which is slower than running.
Straight Line from the Shoreline (Path of Least Water): The lifeguard could run along the beach to the point closest to the swimmer and then swim straight to them. This minimizes the amount of swimming but increases the total distance traveled.
Compromise (Quickest path): Turns out that the optimal path is a blend. The lifeguard runs at an angle along the beach for a certain distance and then swims diagonally to the swimmer. If you were there with a stopwatch this turns out to be the quickest route.
So it's about finding the optimal balance, let’s call this the 'Feynman Point’. Once you’ve internalised it, you’ll see it pop up everywhere.
Marking up contracts
For example, every experienced lawyer performs a balancing act akin to this when marking up contracts. They're not just guarding against risks; they're weighing them.
Too much caution, and the deal gets bogged down in red tape. Too little, and the company is exposed to unnecessary hazards. The best lawyers find their 'Feynman Point' – that sweet spot of just the right amount of protection.
Legal AI tools
I feel it’s the same with legal AI tools. AI tools promise efficiency and precision, but their real value lies in finding their 'Feynman Point' with human input.
If an AI tool tries to handle a contract end-to-end, it often misses nuances that a lawyer, with their contextual understanding and experience, would catch. The result? The lawyer feels the solution is not fit for purpose, to much reworking of the output.
On the other hand, if the tool does too little, it becomes just another item in the toolbox, rarely used. The key is in the calibration – designing tools that understand the balance between automated efficiency and the invaluable input of a seasoned lawyer.
The future of legal tech isn't in replacing lawyers with AI. It's in creating centaur solutions – part human, part machine – where each complements the other, leading to outcomes neither could achieve alone. I.e. faster contract review without sacrificing quality for speed.
In your next brainstorm or when evaluating new legal tech, ask yourself. Are we aiming this at our 'Feynman Point? It’s worth thinking whether you are at risk of swimming further than is necessary, or perhaps on the flip side running too far before getting in the water!
I believe that in this balance, in this perfect compromise, lies the path to true efficiency and effectiveness in legal practice.
Thanks for being here,
Daniel