America to hit 82 Unemployment I have the data to back it up_transcript.csv

text,start,duration
all right so there's a bit to unpack,0.659,4.081
here but before we get started I want to,2.76,4.26
uh profess this whole thing with take it,4.74,5.04
with a gigantic grain of salt uh my,7.02,5.1
methods are highly dubious and my data,9.78,6.54
quality is uh abysmal let's say but it's,12.12,5.34
an interesting thought experiment so,16.32,2.58
let's dive in,17.46,2.579
um all right so we'll start with the,18.9,3.48
conclusion and then work backwards,20.039,6.48
so right now at least May 2022 so just,22.38,6.719
over a year ago 13 months ago America,26.519,7.081
had 147 million employees ish 148,29.099,8.841
million now based on the current,33.6,8.279
capabilities of AI what this includes,37.94,5.74
all machine automation robotics,41.879,3.981
artificial intelligence,43.68,5.76
my estimate is that we could automate,45.86,8.14
away almost 25 million jobs today,49.44,8.88
now that requires the deployment of new,54.0,6.3
products and services so those products,58.32,4.559
and services do not exist yet but the,60.3,5.34
underpinning technology does which means,62.879,5.28
that the only thing preventing us from,65.64,5.1
from getting that employment is just,68.159,4.921
inertia the time that it takes to,70.74,4.559
develop the products and software,73.08,4.74
the time to get it approved and the time,75.299,4.14
to implement it and then make those,77.82,5.7
changes so as of right now almost 25,79.439,6.421
million jobs are vulnerable to AI with,83.52,5.7
the capabilities of GPT 3.5 turbo and,85.86,6.24
gpt4 and then of course all the other,89.22,5.52
open source uh and and Commercial,92.1,5.339
competitors that are near peer,94.74,7.14
adversaries now that being said if you,97.439,7.561
look at the jobs that uh based on a,101.88,5.599
job's intrinsic requirement for humans,105.0,5.579
uh basically you look at the the kinds,107.479,4.661
of jobs that you're going to pay to have,110.579,4.08
a person doing regardless of what the AI,112.14,4.92
is capable of this is these are the jobs,114.659,3.78
that are going to be vulnerable just by,117.06,3.199
virtue of the fact that you don't really,118.439,5.46
necessarily need a human doing it the,120.259,6.341
numbers become a little bit scarier and,123.899,4.92
uh my estimate this is worst case,126.6,6.42
scenario is that in the near future 77,128.819,6.901
million jobs out of the 140 seven,133.02,5.1
million jobs are vulnerable to be to,135.72,3.9
being automated away,138.12,4.8
basically this calculation is a rough,139.62,6.06
estimate on market demand how much do,142.92,4.92
you actually want a human doing that,145.68,3.72
particular job,147.84,3.3
which means that in the worst case,149.4,5.4
scenario we go from 148 million employed,151.14,7.319
to 70 million employed that is more than,154.8,6.6
50 percent lost so I have been,158.459,5.28
repeatedly saying you know we could face,161.4,5.1
70 unemployment 80 and other people say,163.739,4.86
80 unemployment here's the numbers to,166.5,3.959
back it up all right now,168.599,3.481
since I got your attention let me tell,170.459,3.901
you how I did this and again remember,172.08,4.799
this is incredibly dubious so take it,174.36,4.5
with a huge grain of salt but basically,176.879,5.301
what I did was I took the,178.86,6.239
gpt40613 model,182.18,5.139
and I gave it instructions I said you,185.099,5.341
are a BLS chat bot and you're trying to,187.319,5.581
assess the risk of having jobs automated,190.44,3.42
away,192.9,4.619
uh yada yada yada the tldr is that I,193.86,8.58
asked it to uh judge jobs or occupations,197.519,7.381
based on job activities and I had it,202.44,5.28
break it down into percentages uh based,204.9,4.919
on three categories so the first,207.72,4.92
category is is dexterity so this is high,209.819,5.881
friction uh in in physical space that,212.64,6.0
requires compliance uh manual dexterity,215.7,4.98
uh kinetic intelligence that sort of,218.64,4.679
stuff because yes we do have a few,220.68,5.04
robots that can do this but there are as,223.319,4.321
of right now no general purpose robots,225.72,4.379
that are capable of operating anywhere,227.64,4.679
near human level in terms of dexterity,230.099,4.441
there are no general purpose surgical,232.319,3.901
robots there are no general purpose,234.54,5.52
labor robots uh just doesn't exist,236.22,6.18
now that being said you can robotically,240.06,4.319
automate like a skid steer or a tractor,242.4,4.08
or something that's fine that's not what,244.379,3.78
I'm talking about,246.48,3.6
um I'm talking about what a human can do,248.159,4.381
with your hands and body and then the,250.08,4.379
second category is solo work so this is,252.54,3.539
work that can be done in front of a,254.459,4.68
computer or otherwise electronically by,256.079,5.88
yourself uh you know on your phone on a,259.139,4.981
computer that sort of thing and then the,261.959,4.141
third category of job activities is,264.12,3.78
group stuff so this is communication,266.1,4.44
collaboration face-to-face time with,267.9,3.9
other humans,270.54,3.5
and so those three categories break down,271.8,5.82
and and kind of Judge the that's part of,274.04,6.34
what I use to judge the vulnerability of,277.62,5.94
a job so if it if it has low dexterity,280.38,5.58
it is much at much greater risk of being,283.56,4.859
automated away uh just with today's,285.96,6.12
technology uh if it is mostly solo work,288.419,5.701
it is much higher risk of being,292.08,4.32
automated away and if it's a high group,294.12,4.079
activity then it's a much lower risk,296.4,4.079
because that indicates that the human,298.199,4.381
com the human connection component is a,300.479,4.201
big part of it and then finally the,302.58,4.14
vulnerability score is a standalone,304.68,6.86
score which is used to uh basically uh,306.72,7.74
assess how how much you want a human,311.54,5.379
doing that job what is the intrinsic uh,314.46,4.32
need for that job and if the number is,316.919,3.78
lower if that means it's less vulnerable,318.78,4.62
which means you're more likely to really,320.699,4.861
want to have a human in that job and,323.4,4.44
you're willing to pay for that whereas,325.56,4.44
if it's a high vulnerability score then,327.84,3.78
that means that there's no intrinsic,330.0,4.5
economic or personal or whatever there's,331.62,5.28
no intrinsic demand to have a human in,334.5,4.1
that job if you could automate it away,336.9,5.28
then you might as well and so this is,338.6,6.099
like machine shop workers right you,342.18,3.84
don't need a whole heck of a lot of,344.699,3.78
empathy or communication skills and I,346.02,3.72
can hear all the machinists saying yeah,348.479,2.421
like hell,349.74,4.26
because machine is a machinists actually,350.9,4.72
do have to do a tremendous amount of,354.0,4.74
communication that being said the actual,355.62,5.46
Hands-On machine stuff that that part,358.74,4.08
could be automated away,361.08,3.839
if of course the machines become more,362.82,3.9
dexterous right because dexterity is a,364.919,3.06
huge component of that so that's why I,366.72,2.88
broke it down into these four categories,367.979,5.521
of vulnerability and I had chat GPT just,369.6,6.06
kind of do these rough estimates,373.5,5.46
and it did a pretty good job using gpt4,375.66,7.02
so I ran I downloaded the data from BLS,378.96,7.739
and I excluded the detail in Broad ones,382.68,5.82
because they're they're they're very,386.699,4.141
very highly specific and I noticed that,388.5,4.8
it was like okay the job duties really,390.84,4.919
didn't change so I only focused on the,393.3,4.56
minor and major categories which ended,395.759,3.66
up being,397.86,6.3
um let's see that was 116 categories so,399.419,8.4
that's still 116 categories uh that it,404.16,5.7
was used to estimate,407.819,4.021
um in terms of vulnerability and I've,409.86,3.779
got it all color coded so I'll tell you,411.84,5.52
how to interpret the data in a second,413.639,5.34
um here's the here's the thing so the,417.36,4.2
first thing uh the first sorry good,418.979,3.901
grief I'm going too fast here's the,421.56,4.259
repository the first step of this is to,422.88,5.819
break it down uh it just it's very very,425.819,4.861
simple you open the data,428.699,5.641
the CSV data pipe it in you get the,430.68,6.84
result it pipes it back out in Json and,434.34,5.1
I can show you an example of the debug,437.52,4.88
here so I just it captures everything,439.44,5.46
occupation code description and,442.4,4.72
employment so that's what it gives it so,444.9,3.66
that it understands and of course chat,447.12,3.18
GPT already knows what all the,448.56,3.72
occupation codes actually mean because,450.3,4.2
it's baked into the training data and so,452.28,4.5
if you go and talk to like chat gbt and,454.5,4.58
say like hey what is BLS occupation code,456.78,5.34
17-1000 mean give me some examples and,459.08,4.54
it'll it can give you a good explanation,462.12,6.66
now one thing that I had it do was to in,463.62,9.18
the output it actually tells me why it,468.78,5.759
made the judgments that it did so this,472.8,3.78
is where explainability comes in,474.539,5.28
so uh here's an example of the final,476.58,6.08
output for uh code,479.819,5.1
41-3000 sales representatives and,482.66,5.379
services total current employment is 2.1,484.919,6.601
million uh dexterity rather low you know,488.039,5.701
you got to call some people,491.52,4.079
um you know a significant portion of,493.74,3.359
their work can be done solo on a,495.599,3.421
computer such as managing customer,497.099,3.841
databases and orders which make it,499.02,4.019
susceptible to automation however a,500.94,3.96
large part of their job about 60 percent,503.039,3.301
involves group collaboration and,504.9,2.699
communication such as face-to-face,506.34,3.479
meetings negotiations which is difficult,507.599,4.38
to automate the intrinsic technological,509.819,4.441
vulnerability is estimated at 40 so it's,511.979,5.221
a relatively low vulnerability uh job,514.26,4.92
because yes there are some things that,517.2,5.339
you can automate away but in general you,519.18,5.099
want to be talking to a human sales rep,522.539,4.081
it to close the deal,524.279,5.161
so in this case uh the the numbers kind,526.62,5.159
of all come together to say okay uh,529.44,5.22
let's see we uh this estimated that we,531.779,4.801
might lose about twenty six thousand,534.66,4.14
jobs,536.58,6.06
um or no sorry uh 255 000 jobs uh,538.8,6.9
leaving 1.86 million instead of the 2.1,542.64,5.16
million because again if you can,545.7,4.68
automate if you can automate away a,547.8,5.88
third or you know a quarter or a half of,550.38,5.04
a job that reduces the demand for people,553.68,4.38
doing that and if you have tools that,555.42,4.32
can accelerate it and make it easier,558.06,4.5
then you uh then of course you have less,559.74,4.86
demand for someone in that role now that,562.56,3.36
being said this vulnerability score,564.6,3.419
means that in general you still want to,565.92,4.32
have a human there even if you have less,568.019,4.44
demand for humans overall aggregate,570.24,3.539
demand so think of it in terms of,572.459,2.82
aggregates,573.779,6.721
okay so uh that is how I achieved that,575.279,7.921
and um let's see I kind of lost my train,580.5,4.14
of thought okay I guess we can go over,583.2,3.0
the code it's all pretty straightforward,584.64,3.54
I've got it documented,586.2,4.319
um it it dumps it all out into the yaml,588.18,4.44
files here so you can look at all,590.519,4.401
however many there are,592.62,4.68
116 so you can look at the reasoning,594.92,4.24
that it used for each one and you can,597.3,3.42
you can monkey around with this if you,599.16,3.84
want uh you can rerun it all the code is,600.72,5.34
there to regenerate this data but then I,603.0,7.8
got it all into this and so this is uh,606.06,6.0
the total data and I've got it color,610.8,4.86
coded so I have total number of jobs,612.06,6.18
lost and this is color coded so that the,615.66,4.739
the higher the number the darker or the,618.24,4.74
more red it is and of course it's not,620.399,4.201
proportionate so I added a percentage,622.98,5.039
lost and so this is this column here the,624.6,5.22
jobs lost in percentage loss this is,628.019,4.921
most likely based on some really rough,629.82,7.26
numbers uh based on what AI what I know,632.94,6.0
AI is capable of today and so remember,637.08,4.5
here's my credibility I was an I.T,638.94,4.74
automation engineer I've deployed,641.58,4.499
servers and software and cloud and,643.68,5.399
everything to automate uh and and,646.079,5.581
support businesses and and I've,649.079,3.961
integrated all these systems I know what,651.66,3.419
it takes to run these systems and to do,653.04,4.739
the job of like a DBA and business,655.079,5.041
analyst and you know Financial you know,657.779,4.74
comptroller I I talked to everyone when,660.12,4.92
I was in a corporate job so,662.519,4.801
that being said remember like I'm,665.04,3.78
basically throwing wet spaghetti at the,667.32,5.459
wall so this is a educated guess uh as,668.82,6.18
to percentage of jobs that could be,672.779,4.261
automated away obviously I'm less,675.0,4.2
familiar with uh blue collar jobs with,677.04,4.14
manual labor jobs I have done them in,679.2,4.5
the past uh I've I've worked nights in,681.18,4.68
in stores and warehouses so I kind of,683.7,4.68
have a feel for that that being said,685.86,4.08
um you know I have poured concrete but,688.38,4.62
not professionally so you know take some,689.94,5.04
of that with a grain of salt now percent,693.0,3.72
loss you see some of the higher ones,694.98,3.299
computer and mathematical operations,696.72,3.84
okay yeah that makes sense,698.279,3.961
um stuff with a low percentage loss,700.56,3.18
occupational therapy and physical,702.24,4.2
therapists assistance and AIDS this is,703.74,6.18
at a very very very low risk of being,706.44,7.38
automated away because it is one it's a,709.92,7.5
highly dexterous job and you also very,713.82,6.24
much want a human I've dated a physical,717.42,5.539
therapist before and it is an intensely,720.06,5.279
emotional job and it has to do with,722.959,4.841
communication verbal and non-verbal,725.339,5.461
communication there's tremendous amount,727.8,4.979
of human connection involved in that so,730.8,4.32
I totally agree with the result here,732.779,5.041
where you know health support and stuff,735.12,4.98
so all these numbers are jobs that are,737.82,4.86
at very very low vulnerability,740.1,5.28
uh for being automated away,742.68,4.26
um and then you see oh wait here's a big,745.38,4.8
chunk here so Communications uh clerks,746.94,6.12
information uh handling material,750.18,5.7
recording secretaries uh other office,753.06,5.88
and admin all at high very high risk of,755.88,4.62
getting automated away because again,758.94,3.3
these are things that you primarily do,760.5,3.079
in front of a computer,762.24,4.5
agricultural workers this is uh you know,763.579,5.021
eight and a half percent because again,766.74,3.96
you know some of it will be automated,768.6,4.919
away and you see most of them are around,770.7,6.0
kind of like the 10 to 12 percent range,773.519,5.281
um you know with a few few categories,776.7,4.56
being much much more vulnerable to,778.8,3.659
automation,781.26,5.28
now that being said there is uh I have a,782.459,5.701
second sheet and in the second sheet,786.54,3.359
what I did was I removed all the major,788.16,3.479
categories,789.899,3.981
um so you see like it's,791.639,5.181
11-10011-2000 so I removed,793.88,5.86
11-000 so that we would have a finer,796.82,6.819
grain view to look at the total data for,799.74,6.839
each of the the the minor categories,803.639,5.161
and so here,806.579,4.141
um it's it's the same same pattern,808.8,3.42
obviously I just removed some of the,810.72,3.059
stuff so that we'd have more accurate in,812.22,3.059
terms of total numbers,813.779,5.101
now what I did was I uh you know I,815.279,5.701
estimated total jobs lost that could be,818.88,4.44
automated away right now so that's right,820.98,5.039
here uh so that's that's how I arrived,823.32,5.28
at that data is okay based on all these,826.019,5.101
vulnerabilities scores and stuff what is,828.6,4.799
the what is the maximum jobs loss that,831.12,4.56
we could anticipate here,833.399,4.5
uh and then of course you just take that,835.68,4.8
away from you know total remaining etc,837.899,3.661
etc,840.48,5.28
simple simple arithmetic now over here I,841.56,6.18
said okay let's ignore what Ai and,845.76,4.44
Robotics are capable of today and let's,847.74,6.0
just look at the uh the aggregate demand,850.2,6.42
for humans in a particular job and so,853.74,4.8
for instance in computer occupations,856.62,4.68
there's not really a strong need to have,858.54,4.62
humans doing that to do most of those,861.3,4.5
jobs uh you know if the computer works,863.16,6.0
nobody cares uh as as an example and as,865.8,5.7
a former I.T specialist I can tell you,869.16,5.88
that my Humanity didn't really matter uh,871.5,5.399
it was as long as as long as the lights,875.04,3.72
were on and the you know the registers,876.899,3.961
were running nobody cares,878.76,5.22
uh drafters engineering techs a little,880.86,4.86
bit less demand,883.98,3.299
um let's see,885.72,4.14
there's some yeah so what are these,887.279,5.161
uh Communications operation operators,889.86,4.919
clerks these are at super high risk,892.44,4.259
agricultural workers so this is this is,894.779,3.3
one of the ones that's kind of a paradox,896.699,4.44
because on the one hand right now it's,898.079,6.661
at a very low risk because it requires a,901.139,6.541
tremendous amount of dexterity but,904.74,4.74
as long as the food gets on the table it,907.68,3.779
doesn't matter who put it there so in,909.48,4.14
terms of intrinsic vulnerability it is a,911.459,4.44
very high intrinsic vulnerability so,913.62,3.18
that's why I've got all these color,915.899,3.0
coded here,916.8,5.099
um let's see uh yep so there that's that,918.899,5.101
and so what I used there was to say like,921.899,3.841
what is the maximum possible attrition,924.0,4.92
we could see based on you know okay if,925.74,5.219
we can get rid of 20 of Executives,928.92,3.3
that's so that's basically what this,930.959,3.781
means is that the aggregate demand for,932.22,5.52
uh Executives for top Executives goes,934.74,5.58
down by 20 because we can automate away,937.74,5.279
a lot of it and you know make the things,940.32,4.92
easier and faster and so on and so forth,943.019,4.081
so that means we could have an attrition,945.24,4.38
of seven hundred thousand Executives,947.1,5.539
leaving us with 2.9 Million Executives,949.62,5.94
uh and you know but then you go down and,952.639,4.241
because this is Imagining the future,955.56,2.7
where it's like okay,956.88,3.24
let's take the Farm Workers the,958.26,5.28
agricultural workers uh for example and,960.12,7.079
so right now we have uh looks like we've,963.54,7.08
got a total employment of 390 000.,967.199,6.121
um but there's only so much demand for,970.62,5.64
that so imagine we put AI in every John,973.32,6.36
Deere tractor every combine every Hopper,976.26,6.18
system and you basically have you know,979.68,5.04
Farm entire Farms with no humans that's,982.44,4.5
what this .85 means so we'd have an,984.72,5.52
attrition rate of 85 percent leaving,986.94,7.019
only 58 000 farmers in America total now,990.24,5.339
of course this does not include seasonal,993.959,4.94
labor so that's uh slightly different,995.579,5.7
construction workers this is another,998.899,4.36
example where,1001.279,3.781
um the it's kind of it's kind of a mixed,1003.259,3.901
bag so the attrition rate here would be,1005.06,4.32
expected to be about 40 percent but then,1007.16,4.14
again like this is a super highly,1009.38,4.259
dexterous thing with with that is,1011.3,5.64
primarily uh collaborative work so this,1013.639,5.041
is going to be a very very very,1016.94,3.24
difficult one to automate away because,1018.68,4.26
it's very high risks if a robot makes a,1020.18,4.56
mistake an I-beam gets dropped on,1022.94,3.899
someone and they get killed right and so,1024.74,3.42
it actually you'll probably have a,1026.839,4.441
threshold where it'll be safer and,1028.16,5.1
cheaper to just take humans off the job,1031.28,5.639
site entirely now that being said we are,1033.26,5.46
already seeing that a little bit with,1036.919,5.101
like the 3D printing home printers where,1038.72,5.16
it's like a robotic Gantry that just,1042.02,3.419
sits over there it's got its pile of,1043.88,3.12
material and it just builds the house,1045.439,3.961
with no humans involved at all and so,1047.0,4.02
that's what we have to look out for is,1049.4,3.72
that in some of these cases there's,1051.02,3.779
going to be this saltatory leap where it,1053.12,3.36
goes from you know four and a half,1054.799,4.141
million employees to zero very quickly,1056.48,4.319
because as these new technologies rolled,1058.94,3.359
out you're going to replace them all,1060.799,3.541
wholesale there is some things are not,1062.299,3.181
going to lend themselves to being,1064.34,3.48
gradualistically replaced truckers are,1065.48,4.02
going to be the same thing once we,1067.82,4.14
figure out how to automate trucks truck,1069.5,4.14
driving it's just not going to make,1071.96,3.9
sense to have truck drivers now that,1073.64,4.26
being said that's a very high threshold,1075.86,3.96
but once to reach that threshold it's a,1077.9,3.54
step change so that's what I mean by,1079.82,3.54
saltatory leaps that's that's a step,1081.44,3.96
change it's a more common term versus,1083.36,3.66
gradualistic where you kind of replace,1085.4,3.2
people slowly,1087.02,3.96
in some cases where you replace people,1088.6,4.3
slowly that could be like I.T staff,1090.98,2.939
right,1092.9,3.659
as a former automation engineer I can,1093.919,4.26
tell you that I have personally reduced,1096.559,4.441
head count in infrastructure departments,1098.179,5.161
by automating away jobs which reduce the,1101.0,4.919
demand but it was over time right it was,1103.34,4.079
like okay well we can do more with less,1105.919,3.301
let me automate a little bit more and,1107.419,3.421
then we can do more with less and so on,1109.22,3.0
and so forth and then eventually it was,1110.84,3.18
basically just me running the whole,1112.22,3.48
department by myself as an automation,1114.02,4.08
engineer not everything lends itself to,1115.7,4.8
that but that being said the amount of,1118.1,4.14
logic that goes into making decisions,1120.5,4.5
even financial decisions or executive,1122.24,4.86
decisions or HR decisions a lot of this,1125.0,4.1
is now within the realm of possibility,1127.1,6.54
of GPT Technologies so when I say that,1129.1,7.0
we could we could get rid of almost 25,1133.64,5.22
million jobs right now,1136.1,5.28
that's a number that I will stand behind,1138.86,4.439
again it's going to take time because,1141.38,4.32
there's inertia but then in terms of,1143.299,5.281
total risk of jobs that might go away,1145.7,6.0
for good looking at 77 million jobs in,1148.58,5.7
America leaving us with only 70 million,1151.7,4.44
jobs and we have a population of what,1154.28,4.5
350 million or 400 million right now so,1156.14,4.32
that's an employment rate that is very,1158.78,3.54
very low that's a very high unemployment,1160.46,5.219
rate if if that comes to pass and again,1162.32,5.88
some of these things are dependent upon,1165.679,4.5
robotic advancements that are actively,1168.2,4.38
being worked on construction robots,1170.179,4.86
surgical robots general purpose domestic,1172.58,5.76
robots so that the aggregate demand for,1175.039,6.0
human labor is going to fall off a,1178.34,5.76
freaking Cliff that being said like okay,1181.039,6.781
you know what happens with when you have,1184.1,6.42
70 million people which is less than a,1187.82,5.16
third of uh the Americans out there,1190.52,4.38
let's see what is it,1192.98,3.72
um I don't remember the population but,1194.9,3.5
let's say we have 380,1196.7,5.64
million Americans right now and,1198.4,7.24
we end up with only 70 million jobs,1202.34,6.86
um oh wait uh,1205.64,3.56
where's the invert there we go so that,1209.6,6.24
is a that is only 18 of our current,1212.0,5.82
employment so that means we'd be having,1215.84,5.64
an unemployment rate of about 82 percent,1217.82,7.739
um yeah so we go if if we end up with an,1221.48,7.199
unemployment rate of 82 percent uh that,1225.559,5.281
is not good uh based on current,1228.679,3.961
paradigms and so when people say oh well,1230.84,3.78
you know Dave you keep advocating for,1232.64,3.24
Universal basic income and that's,1234.62,3.9
socialism well if one out of if only one,1235.88,4.44
out of five people have a job,1238.52,4.68
we got to do something right if there,1240.32,4.5
it's that simple,1243.2,3.24
um now one thing that I wanted to point,1244.82,3.359
out is that people are like well where's,1246.44,3.0
the money going to come from where does,1248.179,3.781
money already come from money is created,1249.44,4.92
through uh through minting money and,1251.96,4.8
giving loans to central banks and who do,1254.36,4.08
the central banks give money to they,1256.76,2.94
lend it out to other Banks and,1258.44,3.359
corporations that's where money comes,1259.7,3.78
down from it doesn't come from the,1261.799,4.141
bottom up money is created at the center,1263.48,4.26
of the economic system and then it,1265.94,4.619
percolates outwards so when I when I,1267.74,5.16
mention in another video recently that,1270.559,4.74
uh B2B transactions are going to be what,1272.9,3.96
drives the economy that's what I'm,1275.299,4.981
referring to uh because you know if a,1276.86,5.34
company lends money to another company,1280.28,4.44
or bank or whatever and then they have,1282.2,4.859
goods and products and services the,1284.72,4.319
aggregate consumer demand is only part,1287.059,4.681
of that uh part of that equation uh,1289.039,4.14
because if if you have a lights out,1291.74,2.58
company that's selling goods and,1293.179,2.701
services to another lights out company,1294.32,3.96
the economy is going to be red hot even,1295.88,5.039
though there's no humans involved so,1298.28,5.3
that's why I mentioned in my in my uh,1300.919,4.801
macroeconomics or sorry post labor,1303.58,4.12
economics video that we're going to need,1305.72,3.24
new kpi,1307.7,2.7
and we're also going to need to,1308.96,4.079
redistribute that because right now the,1310.4,4.139
only way that money gets into the hands,1313.039,4.62
of consumers is through either the the,1314.539,5.461
very few uh entitlement programs that we,1317.659,5.88
do have uh which are relatively weak or,1320.0,5.94
through uh employment through money,1323.539,5.701
coming by force of extraction downwards,1325.94,4.979
uh through the through the grapevine,1329.24,4.5
from the central banks to the regional,1330.919,4.861
Banks to the corporations and then,1333.74,5.04
finally to us but if we if we reverse,1335.78,5.46
that if instead so this is basically,1338.78,5.1
what Universal basic income is to me if,1341.24,6.24
instead of lending money to from uh from,1343.88,5.64
you know the Federal Reserve to the,1347.48,4.26
central banks to the regional Banks what,1349.52,4.92
if we instead uh take about half of that,1351.74,4.86
and just give it instead of lending it,1354.44,4.08
out to Banks we just give it to,1356.6,3.54
Consumers,1358.52,3.36
and you just you just flip the the,1360.14,5.1
system uh on its head and so then the,1361.88,5.58
total amount of economic change is,1365.24,3.72
pretty much identical you're not,1367.46,2.94
printing any more money than you already,1368.96,2.459
were,1370.4,4.2
anyways this is all very uh getting very,1371.419,6.421
abstract point is uh there's a lot of,1374.6,4.86
change coming and here's some numbers to,1377.84,4.64
back it up thanks for watching,1379.46,3.02