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hello everyone I am really excited about
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this video I've been planning it for a
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while and I kept just adding to it as I
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was doing more and more research uh
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particularly because I was using deep
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research to make it better and better so
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let's Dive Right In uh I have talked
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about the concept of the automation
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Cliff for a while and uh I didn't come
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up with this idea so what I wanted to do
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was actually share some of my personal
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experience but also some research as
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well as some projections based on the
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automation Cliff now uh basically this
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is the idea of the automation cliff and
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we're going to unpack this but just keep
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this graph in mind where you've got the
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stair step versus this more kind of
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catastrophic plunge uh the tldr is that
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if you focus on incremental improvements
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in technology you'll end up with this
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kind of gradual stairstep Improvement of
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level of automation versus level of
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human involvement so before automation
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human involvement is high and then level
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of of automation so you can think of
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this as similar to like uh Tesla's
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levels of FSD you know because right now
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they're at like level three um but
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ideally full self-driving like true
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self-driving is going to be level five
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which is zero human involvement needed
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ever whereas level three is 99% of the
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time it can work but it can't handle
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edge cases and does still require human
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intervention um I don't know the exact
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definition of FSD level three but you
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get the idea so right now most
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Industries are using the stairstep
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approach and we'll talk about why this
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is um but in an Ideal World and in also
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some cases you end up with more of this
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kind of edifice approach where you just
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have this escarpment that just you just
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go careening off of hence the thumbnail
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that you saw for this all right moving
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on so what is the automation Cliff uh
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basically the the principle of the
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automation Cliff says that what you
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should do is wait until you have the
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full process automated end to endend and
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then just full sent just send it off and
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there you go um one of the key
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principles here is tasks should be
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controlled completely by either humans
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or completely by automation systems with
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no middle ground personally as an
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automation engineer in my past life this
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is what I would have advocated for and
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the reason is because you know it does
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nothing until you turn it on and you do
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all the full testing and then you turn
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it on and it automates everything all at
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once that's what I mean by the
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automation Cliff now there's lots and
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lots of other principles in but there's
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also like some problems with getting
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there so let's start to unpack that uh
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in just a moment but what I want to talk
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about is drop in Technologies so let me
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go back a couple slides and show you
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what happens so when you have an
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automation Cliff like this this usually
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happens when you have what's called a
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dropin technology and a dropin
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technology basically means you know
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here's the oldfashioned way of doing
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things where it's 100% humans uh in the
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loop and then you have this new brand
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new technology that means you don't need
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humans in the the loop at all whatsoever
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so let's give you a couple of examples
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about drop in Technologies now in some
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cases these are not automations but
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these are technologies that could just
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come in and completely change everything
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so first was USB um everyone is familiar
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with USB when I was little and people my
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age and people uh older were younger you
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had serials and you had parallels and
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you had all kinds of different
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connections now everything is USB
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Universal serial bus um Cloud
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integration so SAS software is a service
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is another example of where you can just
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switch between softwares um and if you
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get you know uh actually chat Bots are a
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prime example you can go sign up for
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Claude you can sign up for chat GPT you
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can sign up for Gemini and most of them
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are pretty interchangeable so those are
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examples of drop-in Technologies or
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fungible Technologies GPS was another
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thing that once it was there it was
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ubiquitous and now you can use it for
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all kinds of stuff it enabled Google
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Maps uh and you know your your Fitbit
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and everything there's all kinds of
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different cascading effects uh smart
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retrofitting of buildings and other
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infrastructure so an example of a drop
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in technology was dialup modems which
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used that was that's probably honestly
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the best example because you started
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with existing phone lines and then you
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said well let's make them digital so
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then you can just have modems call each
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other and exchange uh information that
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way and that was really kind of most
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people's first uh experience of the
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internet and then we did the same thing
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with cable uh cable modems were just
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using the fatter uh bandwidth that uh
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the digital cable had uh available uh
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and then streaming media is another
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example you know you can swap between
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Netflix and Disney and all those other
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things so these are examples of drop in
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technologies that once you have enough
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infrastructure you can just put in a new
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technology and you can adopt it very
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quickly personally uh on an individual
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consumer level you can adopt it almost
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instantly overnight um some of these
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Technologies do take longer for larger
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organizations to adopt just because
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there's a lot of inertia in those
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organizations so these are a few
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examples of the fact that we have had
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dropin Technologies um that allowed for
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that kind of that almost saltatory leap
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that very Square wave um of adoption not
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all of it goes that way so um before we
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uh get a little bit further I want to
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talk uh also about examples of where
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full automation does tend to be superior
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so if you can achieve full automation if
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you can achieve that full automation
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Cliff that is going to be more desirable
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so one example is um autopilots so
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originally autopilots would basically
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just maintain your altitude and speed
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and a little bit more but now uh as
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airplanes are more and more
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sophisticated autopilots basically there
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are stories of Pilots just going to
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sleep with the autopilot on uh meaning
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that they run 100% of the aircraft um
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another example is pharmaceutical
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production so these by the way these are
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all numbers that were surfaced using
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deep research um so yeah maybe I should
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start including the links to those deep
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research articles as uh as as evidence
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anyways let me know what you think in
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the comments so uh having supported and
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worked with and consulted for and talked
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to people in the Pharmaceuticals
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Industries um the pharmaceutical
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industry is one of the most heavily
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automated Industries out there
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particularly with the actual production
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of drugs um and when you look at what
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when we talk about lights out
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manufacturing lights out manufacturing
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basically means no humans need to be
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present or even observing um and so that
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that took the effect rate from 0.1% to
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0.001% um so in in other words keeping
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human supervisors was actually a net
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negative it was actually better to get
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humans completely out of the loop um
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here's another example is automated
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Harvesters so John Deere so these are
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like the big combines that you see like
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that you know go over fields and harvest
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everything um they're fully autonomous
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combines uh reduced yield loss from 15%
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to 2.3% by eliminating operator fatigue
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and operator errors basically you know
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humans make mistakes and if you're
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driving a tractor for 10 hours a day you
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get kind of bored so on and so forth you
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get the idea we don't need to go through
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every single example but these are some
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these you know between uh autopilots
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pharmaceutical manufacturing and uh
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harvesting you can see that there are
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several examples across several
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different domains where full automation
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is actually preferable if you can
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achieve it so moving on um now one
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question you might might be wondering is
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like okay well why why why is the
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automation Cliff preferable um if if you
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know you could just gradually uh
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Implement things number one is the uh
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performance degradation of handoffs so
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you know you see videos of people you
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know driving their Tesla and it's like
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oh you know you're distracted and then
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you have to intervene um that's one
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example now what I will say is as a
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counter example to that is that the is
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that splitting your cognitive attention
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with using tools like deep research it's
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like oh here go do go do a research uh
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topic for me briefly and then I'll come
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back in 5 minutes and that actually
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gives your brain a CH a chance to rest
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there's actually a brief story that I
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have where um uh at a software company I
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worked at gosh 2012 2011 2012 so that
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was a long time ago um I built out their
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their uh virtual infrastructure and we
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built more build servers for them and
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their build process went from 24 hours
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to 2 hours and they were like Dave can
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you take those servers out they were
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kind of joking but they're like can you
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remove those servers because you know uh
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we we we now have less time to actually
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you know work on our after action
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reports we're used to we're used to the
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build process taking 24 hours so then we
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have a full day to keep working um I was
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like I'm not going to do that like you
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you wanted me to make things faster I
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made things faster by a factor of 12
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deal with it so anyways um you have
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trust issues workload problems
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monitoring partial automation can uh
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increase the C itive load which that's
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particularly true if you're monitoring
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different automation stations um so that
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can and and if information is coming at
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you faster that will tire tire you out
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much much more quickly uh and those
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sorts of things so in many cases if it
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is possible then you want to use the
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full automation Cliff you want to go
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straight from the current way of doing
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things to the new way of doing things
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without much um without much
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interstitial time another reason is
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because you don't want to keep
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Reinventing the wheel um that was
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something that I include in this slide
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but basically every time you have to
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reinvent the infrastructure or Implement
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new infrastructure that handles you know
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human affordances and partial Automation
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and then you have to do it again to get
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to full automation often it's better to
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just wait and then implement the full
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automation all at
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once um and so this is this is talking a
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little bit more this slide is we talk a
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little bit more about how um in reality
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usually full automation is just not an
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option so this is one of the things that
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we're going to be talking about with the
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rise of agents and robots so um also my
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dog is under the desk so if I seem
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distracted I'm petting my dog um she'll
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make a guest appearance one day um so
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first and foremost uh is the economic
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barriers so full end in automation can
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often be very expensive and as many of
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you have pointed out in the comments and
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on Twitter and other places the first
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90% is usually actually really easy it's
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the last mile of automation that is
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really hard and that's where 90 to 99%
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of of your automation effort will go
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into is uh what was it one of you said
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something like you know when in in the
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space of automation you realize that
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everything is edge cases and that's
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that's not a bad way of thinking about
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it is because yes 90 to 99% of what
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you're doing is routine robust uh or not
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not robust um uh routine but or brain
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dead simple I don't know what word I was
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thinking of um but it's it's uh it's
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it's very repetitive maybe that's the
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word I was thinking of it's routine and
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repetitive but then you do need that
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level of high level adaptation for every
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single exception every single edge case
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and those sorts of things and that is
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the technical complexity where it's like
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some things it's just too complex to
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automate unless or until you get to a
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general purpose general intelligence
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whether it's a computer using agent or a
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robot um which is going to be more
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cognitively flexible than a human then
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the technical complexity is no longer a
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barrier that has honestly been the
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biggest barrier to automation up to this
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point but with the rise of generative AI
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language models and cognitive
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architectures that's no longer going to
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be a barrier um risk management resource
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constraints you can imagine how all of
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these things play out but really it's
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the economics and the technical
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complexity are the two biggest barriers
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or constraints to full automation um but
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as robots you know become more
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ubiquitous and become more intelligent
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and as computer using agents also become
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more ubiquitous more robust and more
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intelligent those barriers are going to
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disappear very quickly um so speak
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speaking of barriers and adoption rates
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one of the things that I have been
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pointing out to people is that
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technology adoption rates have been
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accelerating so the automobile took a
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long time to reach a point of saturation
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oh and by the way this graph is a little
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bit dated because you know the internet
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has been around for more than 10 years
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now um so the data in this graph is a
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little bit dated but you get the point
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where the television once it got cheap
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enough it took off really fast
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electricity took off really fast but
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these are things that took you know
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decades to a century to get fully
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adopted but then mobile phones PCS
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internet everything is getting adopted
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much much faster here you're talking
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about adoption curves that are in
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measured in the 10 to 20 years um now
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that uh that the internet has reached a
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certain level of saturation anything
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that can be delivered on the internet
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gets adopted much much faster and that
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includes artificial intelligence such as
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uh chat Bots and those sorts of things
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robots require a lot of infrastructure
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to be built out um then you have to ship
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the robots and those sorts of things so
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because there's a physical layer to the
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robots there's going to be a little bit
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more friction but on the other hand
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robots that are in humanoid shape are a
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perfect dropin technology so uh before
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we move on I want to point out my uh sub
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uh not substack my link tree real quick
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which has my substack um it's got my
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patreon my school Community this is my
00:13:19
learning community I update uh two to
00:13:21
three lessons per week over there on
00:13:22
patreon we have an exclusive Discord I'm
00:13:24
also on substack Twitter um I also just
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added my SoundCloud to uh to my link
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tree which is where I put all my AI
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generated music um it's not for everyone
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but I listen to my own music a lot um so
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if you're into psychedelic Space Rock
00:13:38
I've got a lot of it up there um also
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I'm on GitHub and Spotify and a few
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other things so go check it out all
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right back to the
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show now um what this slide is talking
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about like where are we actually trying
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to automate things so I I what I did was
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I had deep research say go find the
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problems that people are trying to
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automate today right right now with
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generative Ai and Robotics so here are
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the examples that it came up with number
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one is contact centers so we've all by
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now probably heard some of the stories
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where call centers have had some of
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their their Staffing reduced by 90%
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there's also been some stories of
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they've had to rehire Some Humans for
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all those edge cases that we were
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talking about but at the same time a lot
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of those call centers that have switched
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to fully or mostly AI um the cat scores
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also go up and cat is customer
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satisfaction so that's that's MBA jargon
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for how happy are your customers in many
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cases if you go to full automation the
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customers get happier because then uh
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there the quality of their service goes
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up and they have more faith in your
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service or your company or your product
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um so however with that being said you
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know if a call center can only get rid
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of 90% of its people but it still needs
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10 for those edge cases that's not full
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automation um and furthermore there's
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plenty of other kinds of call centers
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that you just cannot fully automate away
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yet that is still a very high Target
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taret because that's what we would call
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low hanging fruit uh another example is
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retail checkout um so in uh for instance
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if you've ever gone to those self
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checkouts um those self checkouts
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sometimes they break or so on and so
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forth sometimes theft also goes up
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because it's like you have a self
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checkout but then you have like a human
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supervising and then but the human gets
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bored and stuff still gets stolen and
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yada yada yada so then you need more
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computer vision for the security and
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yeah and so you end up with all these
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other what about what about what about
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uh kinds of things that make full
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automation of the checkout uh a little
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bit harder uh Warehouse robotics uh this
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is another example so you've probably
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seen some of the videos of like the
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Amazon robots where it's like it there
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Amazon has warehouses that are not human
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navigable anymore um at the same time
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sometimes those systems still get gummed
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up because of a complex emergent
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behavior that happens when you have
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hundreds and hundreds of uh item
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fetching robots and they get you know
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all uh jammed up I don't mean physically
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jammed up I mean you know like the
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traffic gets congested and so on and so
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forth so these are these are current
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challenges that we have not yet solved
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and it's like okay well if we can't
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fully automate call centers and Retail
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checkout and warehouses then clearly
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like a lot of jobs are still safe
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however keep in mind that as robots get
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more intelligent every every step of
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intelligence they that they gain and
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this also includes computer using agents
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that dramatically expands what they can
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do without human intervention so so
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you're going to see some of these leaps
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some of these um some of these sigmoid
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curves or these step functions where
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you're going to have new abilities that
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are going to just say oh all that stuff
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that we couldn't automate a year ago we
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can automate all of it now and I have
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seen that personally back in my back in
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my corporate days I've also seen it in
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some of the clients that I've consulted
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for where there are things that you can
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automate today that a lot of people
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don't even believe that you can automate
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and that's one of the reasons that I
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make these videos is to say hey the
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thing that you think that you can't
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automate maybe you actually can
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so moving on um now I've talked about
00:17:02
humanoid robots on this channel quite a
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bit but I want to talk about how this is
00:17:05
really the ultimate drop in solution so
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one of the key things is that humanoid
00:17:09
robots can operate in human spaces using
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human tools human vehicles and uh pretty
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much everything else so if you have a
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human robot that is as smart as or
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honestly if you put you know gp4 or gp5
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in it or you know clae 4 whatever
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whatever model comes out then it's going
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to be smarter than the vast majority of
00:17:29
humans already then if you have watched
00:17:32
the Boston Dynamics videos where those
00:17:34
robots are far more agile than humans
00:17:36
they can do standing back flips I cannot
00:17:38
do a standing backflip so they're
00:17:40
stronger they're smarter they're faster
00:17:42
they're going to have more dexterity
00:17:43
than humans that means that it is a
00:17:45
perfect drop in solution which means
00:17:47
that basically any job that a human does
00:17:49
with their hands and eyes and body sorry
00:17:51
hit the microphone um these robots will
00:17:54
be able to do very soon and those that
00:17:58
general purpose form function means that
00:17:59
it can even sit in front of a computer
00:18:01
and use a keyboard and mouse if it needs
00:18:03
to um but we can use computer using
00:18:06
agents for that so you can just remove
00:18:07
the whole robot entirely um so this this
00:18:11
represents a full automation solution
00:18:14
and this is what I mean by the
00:18:15
automation Cliff once you start shipping
00:18:18
you know super intelligent super strong
00:18:19
super dextrous super agile robots it's
00:18:22
like game over for 90% of human jobs
00:18:25
next is the computer using agents so the
00:18:27
computer using agents are what you've
00:18:28
seen like um uh operator and repet and
00:18:32
all those other different tools out
00:18:34
there um what you need to think of and I
00:18:37
still have people saying Dave why don't
00:18:38
we just focus on apis and so for those
00:18:41
that aren't familiar an API is an
00:18:42
application programming interface which
00:18:44
is basically allows one computer program
00:18:46
to call and talk directly to another
00:18:48
computer program with without any other
00:18:50
user interface but keyboard video Mouse
00:18:53
KVM is the universal API furthermore
00:18:57
think about how the vast majority of
00:18:59
what humans do also my dogs are
00:19:00
wrestling in the background so if you do
00:19:01
hear that I apologize um so the vast
00:19:05
majority of human knowledge work is done
00:19:07
with KVM keyboard video Mouse if you can
00:19:10
do it with KVM and an operator can do it
00:19:12
with KVM that's a universal UI that's a
00:19:15
universal interface that you don't need
00:19:17
any other infrastructure for you don't
00:19:19
need custom apis you don't need custom
00:19:21
API discoveries that is the API the KVM
00:19:25
is the universal API and so what that
00:19:27
means is is that instead of even having
00:19:30
a robot using the computer you just drop
00:19:32
that agent onto any computer or servers
00:19:34
and they can be virtual servers by the
00:19:35
way and you have literally the
00:19:38
equivalent of hundreds thousands
00:19:40
millions of of employees all using you
00:19:43
know their own own laptop screen
00:19:45
basically but on a virtual server in the
00:19:47
cloud somewhere um that's really what
00:19:50
we're heading towards and the roll out
00:19:51
of this so this is this ties back to
00:19:53
that um what I said about you know the
00:19:55
adoption of cloud services um it's going
00:19:58
over the Internet so that means it's
00:20:00
really really fast to roll out um now
00:20:04
here's my personal timeline so this is
00:20:07
the automation wave optimistic timeline
00:20:10
um and this is based on the kind of
00:20:13
seven-year time Horizon and the seven
00:20:16
years is basically about how long it
00:20:18
took for companies to adopt
00:20:20
virtualization uh which was my area of
00:20:22
specialty as well as Cloud software uh
00:20:24
or software as a service which was uh
00:20:26
adjacent to what I was doing and when
00:20:28
you think that computer using agents are
00:20:30
basically virtualization and Cloud
00:20:33
software and it took seven years to
00:20:35
adopt those then we're looking at about
00:20:37
seven years for full commercial adoption
00:20:40
from this year because this year is when
00:20:41
we're first uh deploying agents so
00:20:44
initial launch is 2025 computer using
00:20:47
agents begin deployment um and not and
00:20:49
digital knowledge work and humanoid
00:20:51
robot uh humanoid robots are being
00:20:53
ramped up this year as well Mass
00:20:55
adoption happens 2026 and 2027 um so
00:20:59
this is when Fortune 500 companies are
00:21:00
going to really start using both
00:21:02
computer using agents and humanoid
00:21:04
robots um in Mass there are Fortune 500
00:21:07
companies already using Tesla Optimus
00:21:09
and other robots just want to point that
00:21:11
out I think BMW was the first car
00:21:13
company that started using them other
00:21:15
than Tesla of course um then so that's
00:21:18
the that's the uh early early Mass
00:21:21
adoption and then you're going to have
00:21:23
full integration happening in 2028 to
00:21:25
2030 and then you're going to have the
00:21:28
the fin laggards the the the
00:21:31
optimization happening in the 2031 to
00:21:33
2032 range and then by 2033 you're going
00:21:37
to have offices full of robots and
00:21:40
computer using agents and all that fun
00:21:42
stuff that's my personal prediction is
00:21:43
that we're looking at seven years until
00:21:47
you know knowledge work as we know it is
00:21:49
over and done with in every industry um
00:21:52
now I want to use this graph so this
00:21:54
this graph is the adoption curve um so
00:21:57
this is like a very similar
00:21:59
version to the other adoption curve that
00:22:01
I showed you so this is this is a linear
00:22:04
adoption curve which is just at what
00:22:06
point does the technology become
00:22:07
saturated but another way to look at the
00:22:09
adoption curve is this which is at what
00:22:12
point does each um each type of company
00:22:14
adopted so right now or or I guess 2024
00:22:18
and earlier were the innovators so these
00:22:20
are all the people that you know watched
00:22:22
my YouTube channel since 2022 2023 these
00:22:25
are the people that have been
00:22:26
experimenting with cognitive
00:22:27
architectures and agents
00:22:29
since you know before chat GPT came out
00:22:31
or when chat GPT first came out think
00:22:33
about back to the era of baby AGI and
00:22:35
those sorts of things that was the
00:22:37
innovators so that was the bleeding edge
00:22:38
innovators that was the first
00:22:40
2.5% this year and 2026 are going to be
00:22:43
the early adopter so this is where uh
00:22:47
this is where all the first movers are
00:22:48
saying okay there's actual commercial
00:22:50
value here let's pull the trigger then
00:22:53
20 2027 to 2028 is going to be the early
00:22:57
majority this is where you know your
00:23:00
your mom and pop shops may maybe maybe
00:23:02
not you know your bakery but what I mean
00:23:04
is you know your average run of the mill
00:23:06
companies are going to start adopting
00:23:08
some of these Technologies you know U I
00:23:11
know lawyers and law firms that are
00:23:12
already using some of these AI tools um
00:23:15
uh but they're they're still kind of the
00:23:16
early adopters so then the majority of
00:23:18
law firms and doctor's offices and those
00:23:20
sorts of things will start adopting then
00:23:22
and then you'll have the uh the group of
00:23:24
people in the late majority so these are
00:23:26
the more Skeptics these are the more uh
00:23:28
mortar kind of stores so like you know I
00:23:31
would imagine that like Home Depot
00:23:33
they'll probably be a little bit later
00:23:34
to adopt these just because that
00:23:36
business model hasn't changed in more
00:23:38
than a century you know it's like you
00:23:40
have hardware and tools and you sell
00:23:42
Hardwares and tools to real people in
00:23:45
front of you um so some businesses some
00:23:48
Industries are going to be a little bit
00:23:49
more resistant to it um rather than
00:23:52
people that are going to be more on the
00:23:53
front end now heavy Industries like
00:23:55
Mining and construction they will
00:23:57
probably be in the early majority if I
00:23:59
had to guess just because human labor is
00:24:01
really expensive and loss of life and
00:24:04
injury is also really expensive but if a
00:24:06
robot gets crushed under a rockfall
00:24:08
that's just a tax write off you can't
00:24:10
write off human lives uh sorry that's
00:24:12
not how it works and then 2030 plus this
00:24:15
is going to be as the rest of the world
00:24:17
catches up so this is my preferred
00:24:20
timeline now I asked deep research to
00:24:24
take all of this into account and make
00:24:26
its own timeline and it gave a much more
00:24:28
conservative timeline so it's its
00:24:31
timeline based on historical evidence
00:24:32
and those longer adoption curves which
00:24:34
we saw earlier says that the initial
00:24:36
wave will be 2025 to 2030 um so I I I
00:24:42
need to emphasize this is not my
00:24:43
personal timeline I'm just showing you
00:24:45
what the AI said as a as a more
00:24:47
conservative or realistic timeline so
00:24:50
2025 to 2030 this is when we're going to
00:24:52
see digital knowledge work uh get
00:24:54
replaced the early majority won't be
00:24:56
till 2030 to 2035 again
00:24:59
I don't believe
00:25:00
that um service integration so this is
00:25:03
where you start to see kind of the the
00:25:04
early uh early and late majority so some
00:25:07
of the more uh resistive s uh uh uh
00:25:10
Industries so like healthc care is a
00:25:12
very resistant industry education very
00:25:13
resistant industry you're it doesn't
00:25:16
expect that we're going to see full
00:25:17
automation there for the next 10 to 15
00:25:20
years again I this will not age well um
00:25:24
and then by then there will be enough
00:25:26
regulatory pressure on States and
00:25:28
federal governments to say okay we need
00:25:30
to do things differently and that's 15
00:25:32
to 20 years out now keep in mind that
00:25:34
2045 is like Singularity so if teachers
00:25:37
unions are still preventing AI in the
00:25:39
classroom when Singularity hits oh boy
00:25:41
are they in for a roote Awakening
00:25:43
anyways like I said I this this timeline
00:25:47
is way too conservative for me but I I
00:25:49
felt like just for the sake of argument
00:25:51
I had to put this is what the AI thinks
00:25:52
the timeline is going to be um now what
00:25:56
I do predict is that as computer using
00:25:58
agents and robots ramp up in terms of
00:26:00
intelligence and ubiquity we are going
00:26:02
to see total Workforce automation as we
00:26:04
understand it today now we can talk
00:26:05
about post labor economics there's there
00:26:07
will be some kinds of jobs like
00:26:08
influencers I hope will stick around um
00:26:11
entertainers will probably stick around
00:26:13
like musicians and stuff there might be
00:26:15
entirely new classes of jobs there
00:26:16
probably will but the vast majority of
00:26:19
economic uh activity will not be done by
00:26:21
humans in the near future so you look at
00:26:24
Medical Precision superhuman surgical
00:26:27
robots with perfect Steady Hand hands or
00:26:28
multiple hands um combined with computer
00:26:31
using agents that are constantly
00:26:32
researching the best medical procedures
00:26:35
you will not have a human doctor you
00:26:37
will not want a human doctor in this
00:26:39
potential world next is construction a
00:26:41
lot of people say oh well I'm a boiler
00:26:43
maker or I'm a welder and Y yada yada
00:26:45
and my job safe no it isn't um consider
00:26:48
that robot that industrial robots
00:26:50
already do better Precision welds than
00:26:52
humans do the only difference between
00:26:54
like those Factory line welders and a
00:26:57
human welder is is that the human is in
00:26:58
a form factor that is more mobile um
00:27:01
that's not an advantage in the long run
00:27:03
electricians plumbers construction
00:27:05
workers uh welders you guys like you're
00:27:09
on notice I'm I'm telling you I'm I'm
00:27:10
trying to warn you ahead of time um that
00:27:13
that job is probably going away next is
00:27:16
emergency response so this is everything
00:27:18
from um uh emergency medical technicians
00:27:21
to Firefighters to even police um or or
00:27:25
whatever like all kinds of emergency
00:27:27
respons you take the human out of the
00:27:29
loop they you know you have machines
00:27:31
that are immune to smoke heat biological
00:27:33
radi radiological chemical attacks
00:27:35
whatever like you know there was um
00:27:38
there was a movie called surrogates
00:27:40
which was a really cool movie it didn't
00:27:42
make that bit much at the box office but
00:27:43
it's a Bruce Willis movie and one of the
00:27:45
scenes in that movie was really cool
00:27:47
where there's like a bunch of soldiers
00:27:48
and they're like all kids like playing
00:27:50
VR but they're piloting little humanoid
00:27:52
robots across a battlefield um and it's
00:27:54
just like oh you know robot gets you
00:27:56
know nuked and you know the person's
00:27:58
like ah darn and they you know spawn up
00:28:01
into another robot and to them it's just
00:28:02
a game um science science and
00:28:05
engineering that would you know I don't
00:28:08
think I have to really sell this for my
00:28:09
audience because you guys are like
00:28:10
paying attention to The Cutting Edge of
00:28:11
like Alpha fold and all that fun stuff
00:28:13
but you know we have like somewhere
00:28:16
between eight and 25 million scientists
00:28:19
uh you know phds uh or doctorates
00:28:22
globally right now we're going to have
00:28:23
the equivalent of billions or trillions
00:28:25
here real soon um and so therefore the
00:28:27
vast majority of scientific research
00:28:28
will be automated you combine Those
00:28:30
computer using agents those digital
00:28:32
agents um or those narrow AIS with
00:28:34
robots and you won't even need humans in
00:28:36
the loop if you don't want it now
00:28:38
obviously you still want humans saying
00:28:39
hey you know hey Mr Robot maybe stop
00:28:41
making VX gas we don't want you to make
00:28:43
that because that's really dangerous for
00:28:45
us but you get the idea and then finally
00:28:48
um you know uh government um
00:28:51
particularly if AI is provisioned uh of
00:28:53
the people for the people and by the
00:28:54
people um and the AI is is is directly
00:28:57
accountable to the people then what role
00:29:00
does elected politicians play
00:29:02
anymore I don't know so anyways thanks
00:29:05
for watching I hope you got a lot out of
00:29:06
this cheers
