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A Rhetorical Question:

Who Will Teach the Next Generation?

Jeremy Ben-David, Founder, Managing Partner

 

I was recently asked a fascinating question. It went something like this: In your profession, how will the next generation of ā€œexperiencedā€ Attorneys become, well, experienced? The premise for the question is that the more that we rely on AI, the less we think, and the less we think, the less we learn. Now, when you think of an experienced professional, the picture in your mind will probably be of a person who has acquired, learned and developed by having faced various and varied challenges throughout a lengthy career. A real expert will be someone who is open to learning from such challenges, and who is prepared to adapt how she or he faces subsequent challenges, based on such experience. 

 

Experience vs. Time Elapsed

 

Therefore, if the professionals charged with training the next generation of professionals are themselves relying on AI tools for much of their work, will they be able to accumulate the necessary experience so that they themselves can improve? Experience that is simply a measure of time counts for nothing unless it is also a measure of learning opportunities, in which those opportunities have been taken!

 

Early Lessons in the mid-80’s

 

Since entering the profession in the mid-eighties of the 20th century, one of the first lessons I learned was how to think about an invention in abstract terms and how to define it in written words. This, in turn, gave me crucial insight on how to perform a relevant prior art search, how to assess the results, how to define the invention in light of and as distinct from the prior art, and thus how to craft the claims, the background and the description. None of those steps, with the possible exception of the scope of the search, and, arguably, devising additional examples base on the disclosure, could be improved by AI. Certainly not the claim drafting as an abstraction of my discussions with the inventor, nor the supporting disclosure, or the arguments that I would use during prosecution. 

 

And even though I could receive a complete patent draft from AI, subject to my review, I would only ever be reviewing someone else’s work. It would never be an expression of my understanding of the invention and my own, original thoughts, fashioned into words.

 

Therefore, on a deeper level, I would never gain the experience needed to advise and teach based on ā€œmy years of experience.ā€ Although I might have a lot of experience when measured by time or by the number of cases or actions, my own training and self-improvement would be deficient; essentially, my experience would be reduced – at best - to simply receiving and reviewing documents prepared by others.

 

Pushback

 

Now, you might object to what I’m saying, and wish to argue that I would still have reviewed the drafts, and probably made corrections here and there, and surely that should count for something. Besides which, how can I possibly know what it will be like to have years of receiving AI drafts  - maybe I’m wrong and I will learn just as much as if I had drafted the patent applications ā€œold schoolā€?

 

The Disengaged Brain 

 

Well, of course I might be wrong. But something that tells me that I might be right is something that happens to all of us. It happens every time that I go on a car journey to a destination with which I’m unfamiliar. All I need to do is to enter the address into WazeĀ® (or equivalent), and then to follow instructions.  I am acutely aware that if I had to find that address again, or to drive home from there, but without the assistance of WazeĀ®, I would be truly up the proverbial creek, without a paddle. I’d be well and truly stuck. This phenomenon has been recognized, and goes under various names or descriptors, including ā€œthe disengaged brain, Stimulus-Response vs. Spatial Mapping, and the "Use It or Lose It" Effect.

 

It makes sense. Not just that I don’t have to be familiar with the route (beyond how long it will take me), but I no longer have to use my brain or many of my sensory faculties in order to get to my destination. Waze even tells me ā€œYou have now arrived at your destinationā€ as if, without such a prompt, I might not know!

 

Old School

 

Before the advent of GPS navigation systems, in order to navigate anywhere without a navigation program, I’d have had to do the following:

 

  1. Locate the address on a map.
  2. Plan a route by looking and understanding the map.
  3. Memorize as much of the route as possible.
  4. When driving, keep on the look out for external stimuli, road signs, building numbers and the like.
  5. Consult the map from time to time, either courtesy of a passenger, or by stopping on the side of the road.

I may have missed out a few steps, but I think that the idea is clear. 

 

Learning by Thinking

 

A strong case can be made for the need of human beings to devise original, creative thoughts in adjudicating legal and business discussions or disputes. This includes the process of learning to express one’s intentions in words, including that gobbledy-gook of legalese or patentese, if you will. This cannot be stressed enough. 

 

A Patent Attorney is supposed to have knowledge and a skill set that is not possessed by most inventors and business owners. That is the reason that they come to us for advice, and for the work that we do for them. Patent Attorneys are supposed to be able to understand the vision and the implications of an invention, and to be able to express those in language which will provide appropriate patent coverage. If that understanding and with it, the ability to express in the written word, are lost due to over reliance on AI tools, then where is our skill? Would our years of experience even matter? When your hair turns gray or white, will you be any better suited to mentor the new generation than those with far less experience than you?

 

I’m not saying that there’s nothing useful to be learned by the use of AI tools. But we have to be selective, and to ensure that whatever abilities AI offers will assist our learning experiences, rather than supplant them. 

 

The question in the title of this article, namely, ā€œwho will teach the next generation?ā€ should be rhetorical. 

 

Is it?

 
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Gilad Luria is the CEO of Scenera, an Israeli deep tech company solving a critical blind spot in advanced chip manufacturing. As chipmakers stack layers of silicon to pack in more power, billions of tiny connections link those layers together, and until now, there was no way to check that all of them actually work. Scenera's technology inspects every single one, catching defects that would otherwise make it all the way to a finished device.

 

Avraham sat down with Gilad to discuss how the idea and technology evolved to fit the right problem, the challenges of building a deep tech startup in Israel, the importance of IP strategy, his advice for founders just starting out and much more.

LISTEN TO THE CONVERSATION HERE
 

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