When VisiCalc first came out, you could explain
it or show it to people, okay? And if you showed
it to a computer person, they look at it and
say, what's so special here? I can write a program
that does that and I can do better and stuff
like that. You show it to a regular person, a
normal person, they say, computers can do anything.
They can forecast the weather. What's so special
about that? But if you showed it to people who
actually did that stuff, They would start shaking
and say, here's my credit card. Please take it.
Give me that. Give me that. How did the best
machine learning practitioners get involved in
the field? What challenges have they faced? What
has helped them flourish? Let's ask them. Welcome
to Learning from Machine Learning. I'm your host,
Seth Levine. Hello and welcome to Learning from
Machine Learning. On this episode, we have a
very special guest. Dan Bricklin, the co -creator
of VisiCalc, the first electronic spreadsheet
and really the first killer app that helped launch
the PC revolution. Dan has lived through every
major computing platform shift from mainframes
to today's AI. Few people have a better perspective
on what separates transformative technology from
hype. Dan, welcome to the show. I'm glad to be
here. Thanks a lot, sir. It's such a pleasure
to have you. Let's get right into it. So what
initially attracted you to computer science,
computers in general? Well, as a child, this
is back in the 50s and 60s. I was, you know,
I love technology and, you know, mechanical stuff
and electrical, electronic stuff and whatever.
And I would read Popular Electronics magazine.
That was my one of my favorites. So a computer
seemed like I didn't know what it was really,
you know, how they worked or how to make them.
But I knew that pretty cool stuff. And I knew
some of the electronics of how to do whatever.
But so I was interested in that. The first time
I actually saw a computer is I was quite young.
I was in early and grade school and I visited
my grandfather, editor of a small newspaper,
and I visited him at work and they had a punch
card machine. IBM punch card machine they use
it I guess for circulation for subscriptions
and stuff and I was really taken by that how
it could sort do the sorting and I actually built
for a science fair project I built a rudimentary
punch card sorter that you could Crank it and
you know as the thing went through it It was
able to tell and the magnet would open lift something
that would go here or there depending on it So
that that sort of got me wanting to get into
it Um, and so that attracted me. Uh, I was always,
uh, viewed as a techie into space and things
like that. Very cool. So then, yeah, we'll fast
forward. You will see you did, uh, you started
at MIT and well, yeah, but before, but I got
into computers early. I mean, I got into computers,
uh, when I was like, uh, what, 15. My cousin
came home from his school and they had just gotten
access to a time sharing system. They were one
of the few schools in the whole Philadelphia
area who were able to do a version of Fortran
called QuickTran using a remote terminal, type
terminal. And I got to go over and learn. He
brought the manual home and I studied the manual
and learned Fortran, enough Fortran too, and
started programming. Like one of the first programs.
that I wrote was we had learned in school patterns
of English. We learned about noun, verb, whatever
and that a sentence had this. So I put in all
these types of sentences and I put in a list
of nouns and verbs and it would make up... It
would make up sentences and stuff that were technically
correct. I thought that was pretty cool. That's
very cool. So that's one of the first things.
And then I built a graphing program. I think
the thing that did graphing, you know, on the
printer, the typewriter output. Then I would
beg, steal or borrow computer time to be able
to improve my stuff wherever I could find access
to computing. In those days, it was very hard
for like a high school or anybody to get access
to computers. But I learned how to be able to
find where... There were ones that I could get
access to. They're usually, you know, like the
Bureau of Public Education in Philadelphia. Right.
I was like the only one who would go convince
them to let me go down and use their 1401 computers.
Yeah. I mean, it's so cool because it was like
right as it was really becoming a thing. So not
very many people had access to it. So you were.
Well, yeah, as these are all mainframes. Right.
I mean, I in late. Let's see. In later high school,
I took a course, a summer course at the University
of Pennsylvania, where the National Science Foundation
had a programming computer course. And they had
this 1130 computer that was brand new. And I
was able to program that and learn computer stuff
at that course at the University of Pennsylvania,
even though I was a high school student. And
then I got a job at the person who ran that course.
worked at the Wharton School at the University
of Pennsylvania and worked in computational services.
And I got an afternoon job in high school to
be able to work there. And I had access, suddenly
I had access to computers through the University
of Pennsylvania. Very cool. As a high school
kid. Nice. Yeah. So I was at the engineering
school at University of Pennsylvania. So every
day when I would go through one of the halls,
I'd pass the ENIAC machine. Is that, is that?
Yeah. So I guess, yeah. Well, whatever. It was
down the hall from where I took my course. I
knew that there was nothing to look at or anything.
Yeah. Now it's just there behind glass and you
can kind of look at it. So they built a shrine.
So they didn't have a shrine. They didn't have
the shrine. I don't remember there being a shrine.
I didn't see it. I just knew that it was somewhere.
I was at the Moore School, which is where it
was done. Yeah, that's where I took a lot of
my classes. It's kind of cool to be connected
to the past. I like looking at the history of
stuff. So it's nice to be connected to the past.
Yeah, definitely. Tell us a little bit like a
mainframe computer. Like, so does it have all
of the same things? You know, how different?
Those days? Well, in those days, I mean, they're
very, very powerful for those days. Today they're,
you know, my watch is more powerful. Right. Microwave
oven is probably as powerful. They were in these
big, you know, the cabinets that look like big
refrigerators and multiple of them, and each
one had different parts in that. Uh, now we might
have, you know, half a chip or a fraction of
a chip to be that whole thing. So there was the
CPU, maybe a couple of them. There were tape
drives, you know, that, uh, were used for storage
for longer term storage. And there were disk
drives, you know, and stuff like that. And it
produced a lot of heat and they had lots of big
cables for lots of power and stuff. So the floors
were raised. They had, so you could get, you
could pull a, a tile off and get underneath and.
you know put the wires down there between them
so you wouldn't trip but also they could take
air conditioning through it to get the heat off
and the air conditioners are running you know
you hear the noise when you go in the there was
a computer room and you didn't go in there because
you don't want to get any dust in there right
or anything like that that might mess up the
card readers and stuff so you know you did things
outside and Plus, you don't want to give normal
people, regular people access to that room. They
might mess something up. So that was what mainframes
were like. They had CPUs, they had short -term
storage and long -term storage and non -volatile
storage and they did all the same. They had computer
languages and you could connect terminals to
them of various sorts to do visual stuff or printed
output with paper printers that could print 10
pages a minute or something like that. You know,
and that's how checks were printed and and reports
and stuff So it's a lot of what we have today,
but just it was bigger Just if you take today
literally within the last few days for most for
any normal person getting it You have the new
Apple has a new phone where all the electronics
are basically are where the camera is a little
strip where the camera is is the whole thing
of the phone because they've managed to rise
the whole phone down to the cameras and everything
fit in a little strip and the rest is battery
and screen but the basic idea of storage computation
you know uh and display and input in and those
are all different parts software that runs on
them it's a stored program computer that that
is you know, from an architectural viewpoint,
it's very similar. Right. It's pretty amazing
to have seen the entire evolution and being one
of the people that was accessing those mainframes
to now see how that has all evolved into today.
So some of the work that you did helped propel
the PC, you know, the personal computer revolution.
You worked on a word processor pretty early on,
right? Yes. Yeah, very early on, I was at Digital
Equipment Corporation after college. At the end
of college, I worked on a system that used a
language called APL that regular people, but
mainly engineers and people like that, could
type in a simple program and get the results
out of it. But then when I went to Digital Equipment
Corporation, which was a large mini computer
manufacturer, I worked in computerized typesetting.
and worked with editing for editing the articles
that went through for connecting to wire service
stuff. That's data that's coming in from outside.
And then I ended up in their first word processor.
It was a computerized word processor, a document
oriented one. In those days, there were two types
of electronic with screen based word processing.
There were just a few companies that made any.
of those and one of them was was page oriented
kind of like page maker or um powerpoint or something
you laid out one page and then you laid out the
next page and you position things within that
as you type document oriented is what we're is
more like word is what we're familiar with today.
Ours used just one long thing of text and it
broke it up into pages and cared about layout
horizontally with tabs and decimal tabs that
line up just right and how do you do that and
I worked on that which was to be used by typists.
Then hopefully by managers and executives, but
they didn't type in those days, but they would
give things off to typists So that's I worked
on that that software ended up on all sorts of
different hardware It started out with a computer
in the deck inside of a desk. The computer was
a big and the screen Part of the desk eventually
they sold it as a one -piece unit for about ten
years as the deck mate Word processor that started
as a deck WPS eight system. So and it looked
like this. Here is an ad for it. This is a typewriter.
This was our system. That was on top of the desk,
you know, and but you can see the desk with the
and the floppy disks, the eight inch floppy disks
that went in it. So and it could do output like
this. This is the functional specification for
it. for this thing written in itself, in an early
version of itself. So I got to, that was word
wrapping, being able, and I wrote software that
did some of that stuff. There was a team of about
four of us doing the software for that word processor.
Before that, I had worked on an editing system
for newspapers. I was familiar with some newspaper
systems that were specifically for editing. You
know two -dimensional layouts for giving the
commands to a type setting system for doing the
ads in a newspaper I got to see those that people
had there was an interactive system called the
Harris 2200 that you can't find too much about
but was a groundbreaking machine that was basically
page maker and hardware and That I was quite
taken by and that influenced visit calc in a
way Yeah, so getting into VisiCalc. So, you know,
VisiCalc, I mean, widely regarded as fueling,
you know, the rapid growth of the personal computing
industry. You know, Steve Jobs went as far as
to say that VisiCalc is what propelled Apple
II to the success it achieved. He says that in
an interview in like the 1990s. Because it was
only available for a year on the Apple II. Because
software takes a long time. Yeah, where do you
want to start with visit calc? It's it's referenced
everywhere right like in prediction machines
The book it talks about like that's like one
of the first steps towards realizing the power
of automation You know change the way that it
was interactive computing people were not used
to interacting with a computer other than to
put their card in and get money out of a machine,
right? That was the main way that people interacted
with a computer. Here was a thing, a word processor,
you know, maybe they had tried a word processor,
but here is a thing that, you know, did things
that they would have to do by hand they were
supposed to, but it could do it so much faster
and open up all sorts of worlds. So that That
really affected things quite a bit. It let people
say, oh, this is what an interactive computer
is. It's not just games. They did have access.
They were playing Pong and stuff like that, Atari
stuff. And so, oh, it can be useful in things
that are better than anything else. Even a mainframe
doesn't do anything. This interactive, me give
and taking with it and getting the output that
I needed and it's custom to what I need. that
just like a word processor. There were programs
before word processors that would construct a
will, would ask you questions and then write
a will. But a word processor lets you write anything.
You know, that's a general purpose tool. Well,
a spreadsheet was a general purpose tool for
numbers, like a word processor was a general
purpose tool for paragraphs or prose. For numbers
and for also text, people often used it only
for text to keep lists. And since you could lay
things out any way you wanted, it was not strict
columns and rows that all the rows are the same,
and all the columns have the same one. No, it
wasn't like that. You could put anything anywhere,
but you could take advantage of it. You could
very quickly create things because it had just
the right tools to let you do things very fast,
because it was optimized for keystroke, minimum
keystrokes, because I came from type setting
where you're paid by the keystroke so I learned
how to optimize for quick and I was competing
against paper with a calculator and a pencil
and paper and it had to be faster the first time
you used it because otherwise you wouldn't use
the recalculation if it was quicker you say oh
I only need this once I'll do it by hand right
so so that I think that said people wow Computers
can be useful in so many ways. What other things
can they do? This is interactivity. What you
see is what you get. The thing is you could build
something and build on it and build on it and
give it to somebody else. And then all sorts
of other software start coming out. Yeah. So
you talk about it... You have like a some sort
of formula about figuring out if something can
be like a killer app. You were kind of referencing
it before, like, so you're comparing, like, what
was it before, right? Like people were trying
to do spreadsheets existed, right? It just was
the idea that you could use a spreadsheet on,
you know, on your machine, you could change one
number, and then that would lead to cascading
of everything changing, you could put in custom
formulas, you could do all this stuff. I guess,
as you were making it, did you know, like, did
you know the magnitude of what you were creating?
Well, of course it should be. Whenever you do
something, it's going to be great. You know,
it usually isn't. Yes. I knew it would be very
useful, but people were not adopting computers
at the rate we expected. Word processing was
not taking off at that point. Right. Back in
1979, it was not going like that. Normal people
did not get it. It wasn't until the inexpensive.
computers like the Osborne and the Kaypro, you
know, that a journalist could buy their own computer
to be able to take home and be able with a word
processor at all and write things. And suddenly
people could do their novels on a word processor.
That wasn't until a few years later. And that
was because PCs became popular. PCs became popular
because I think one of them is people involved
in money like the spreadsheet. They said, oh,
these PCs are useful. And then when they were
good for word processing, the journalist said,
oh, PCs can be useful. And then people, their
kids went away to college, and the only way to
communicate with them was by instant messaging
and email. Oh, computers can be useful when connected
to a dial -in remote stuff. And then the internet.
Every time there is some new capability from
a hardware viewpoint, or hardware and software
combination, that is much, much, at least 100
times better than what was before. Like, think
the mouse is so much better than arrow keys,
right? In terms of precise positioning quickly.
Every time the laser printer compared to the
typewriter output, you know. It was a ball, remember,
the Selectrix. I don't know if people know that.
But in the line printers, the laser printer could
put 300 dots per inch each direction and do pictures
and everything. It was so much better than what
came before. That opens up new capabilities.
So you then say, with this new capability that
is two orders of magnitude, 100 times or more
than what I had before, what does this open up
and what is it good for? This is good for something
I used to do or I needed to do, but didn't do.
And that then lets us move forward. Portability
makes a big difference. It's a big difference
to be able to find, you know, to find messages
in a fraction of a second compared to have to
dial up, you know, to AOL or something and log
in to read your emails and stuff like that. Or,
or, but that was so much better than waiting
for the mail to come, you know, the snail mail.
and each of those things, the fax machine, it
opened up so many things, right? Because you
could send a document, anything drawn or typed
or whatever, you could send it immediately to
other people. Now, what was interesting is about
putting up with things that aren't so good. We
worked really hard in word processing to be letter
perfect, they called it. The output, you wanted
it to look as good as a good typewriter. Okay,
really really good. Then we had the laser printer
that could produce fonts of all sorts, perfectly
sharp, indistinguishable from the old stuff.
Then what do we do? We said, oh, we'll stick
it in the fax machine that turns it down to 150
dots per inch and you know, whatever and it's
only black and white and no no gray and You know,
we'll send it that way We'll take our litter
perfect thing that we work so hard and turn it
into this barely readable thing on paper That
was kind of wet and you know faded whatever but
we could get it immediately right and you know
And we put up with that when we first had cell
phones Can you hear me now? Can you hear me now?
It was, you know, was, but. And they were enormous.
They were huge. They were huge. But at least
you could carry it with you. Right. And so that's,
you know, you look at when you have a new capability,
it opens up some things and you're willing to
put up with it not being as good. A lot of this
is described by Clay Christensen. May he rest
in peace. And the innovator's dilemma. Yes. So
about that whole thing about when you have something
that disruptive technology. So we can look at
that for any new technology we have today. And
how is it better or not from what we had before?
And what is it good for? What is it not good
for? And what doesn't it matter? Right, right.
This sort of trade off when there's something
like some new capability, but then there's this
trade off between quality and then port, like
portability. I think about music also, right?
It's like the quality of music by changing it
and having it be into an mp3, you're losing a
lot of quality. For those that can hear that,
not all of us. The thing is that the portability
and the fact that you could carry in your pocket
hundreds of songs, you know, to listen anywhere.
But the quality was pretty you know compared
to a transistor radio You know listening on the
radio waiting to hear whatever happened to be
able this tinny little speaker when you had those
really good earphones that The Walkman when I
when Sony did the Walkman they did these really
good earphones with these special magnets and
whatever So it actually sounded better then it
probably would be if you used your expensive
stereo in a noisy room so and it doesn't matter
because you're you're portable you can do it
when walking or running or when you're commuting
so yeah this this trade -off of uh and then eventually
they get better where the sound quality you have
with the latest little earphones wherever running
off of you know directly off your watch or your
phone or whatever are like concert quality compared
to our old speakers that we had you know in our
dorm rooms or something like that years before
for an expensive stereo. So it does catch up
that's the innovator that's exactly Clay Christensen's
about how eventually the toy becomes fixes the
differences between it and eventually becomes
better because of the volume and stuff like that.
So going into VisiCalc and creating something
like that, what were, I guess, a couple of questions.
Like, so when you're creating this general purpose
tool, first, it's hard to think of all of the
possibilities that you could, you know, that
you could hit, right? But I guess could you talk
about... I had no clue. I mean, I knew about
a lot because I was at business school learning
about all different aspects of business. So I
was exposed to many uses. Right. but it was a
fraction of the uses that it actually became
used for, even in the first year. Right, right.
Yeah, your story about... So you have a great
TED Talk that I encourage all listeners to listen
to about the history of VisiCalc, but you mentioned
there that you used the spreadsheet basically
to help you with your case studies and to do
projections and to do that... those sort of things,
which personally was, is kind of cool for me
because one of my first case studies was, was
yours, was your case. I mean, they're just, it's,
there's so much echoing of the past. Yeah. So
it's, it's, it's, it's a pleasure to be. And
it was highly interact. VisiCalc is highly interactive.
I worked on the word processor with somebody
in the name of Jack Gilmore, may he rest in peace,
who was on the word processor. He developed what
is arguably one of the first video games. Well,
it is one of the first, but it really could be
the first video games in 1951. Oh, wow. You know,
and, you know, on the whirlwind computer. So,
you know, this moving along and from person to
person to person is kind of cool. Yeah, it's
it. Yeah, it's it's it's really cool. So while
you were developing it at like, what were the
challenges that you were facing? Were there like?
I mean, I guess the technical challenges of trying
to fit it. I know you mentioned that there was
a particular upgrade in the Apple II that made
it possible, I think, or there was some additional
memory or something that allowed it. Oh, well,
I mean, there was stuff. The thing is that, well,
the Apple II was good enough, but the challenges
were the Apple II had a very small screen in
terms of characters. 25 rows of 40 characters
wide on the screen for character based. And it
had two arrow keys. That's it. It had paddles
you could play a game with, but I was gonna use
that like a mouse, but I gave up on that very
early on. Like the... No, no, no. The type you
turn. Oh, okay. The turning one, yeah. I should
go get it. I have one. An old Apple II with it.
It was for playing punk. Right. You know, that
was that type of stuff. Or Space Invaders, which
is what everybody wanted to play, which was a
game on the Apple II. So the challenge was you're
working with data that probably... You know,
you're going to want a lot of it. So luckily
from my word processing background, I was used
to scrolling and dealing with scrolling, but
how do you deal with scrolling on such a small
screen? So we had to do things like what we now
call locked pains. We call them titles where
you would have, you know, the name, some kind
of maybe a row names or something. And as you
scroll, you know, they would stay on the screen.
Right. and I'll let you have two windows so you
could look at two places at once. You could do
this and see the results of a sum somewhere else.
And if you did two scrolling, they would be synchronized.
So we decided to put that software in. Help,
we wanted to have a help system, but there was
no space to put that much information. The only
help we had is you typed a slash and it showed
you what characters you could type for the commands
to put in. And slash IR is insert row, which
I think still if you type slash IR into some
versions of Excel, it still inserts a row. Those
letters that we showed on the screen were the
same letters that we were comparing what you
type to, to see if, you know, what to do next.
I mean, to save memory. So memory, how do you
store the numbers? First of all, we had to make
it that if you saved it and bring it back again,
it would still fit. So if you used up every byte
of memory available on your computer, there was
no putting it out to disk or any of that paging
stuff that we knew from our other day. So you
had to build it so that it would be repeatable.
And therefore you needed certain things to be
very quickly garbage collected using fixed size,
whatever. So what was the largest number that
we would store? Because we needed to know that.
and Bob and I talked about it, Bob Frankston,
who wrote most of the code, and we'd said, okay,
let's imagine that people would use this to calculate
the budget of the United States of America in
dollars. That's enough significant digits before
we go to scientific notation, where it's powers,
you know. And sure enough, It ended up being
used for that a few years later. But we had to
make decisions like that. How many columns, how
many rows, max, because, and when do we allocate
memory to be fast versus how do we do it so that
you could actually have a spreadsheet that could
be this wide or could be this high, but it couldn't
be both at the same time because there wasn't
enough memory for it. We only had, you know,
I don't know, like 30, you know, 30K thousand
bytes of memory for your whole spreadsheet. I
mean, we started on a system that was 32K bytes
for the whole system. That was the program, the
operating system, the screen buffer of what was
going to be on the screen, and your data. We
could fit a few K of data for that. Most people
had 48K, which was the biggest version then that
we could use if you... bought a special card
to run some other program that sold this card,
you got another 16K of memory and we could use
that card to give you more memory and people
would spend hundreds of dollars on this card
and throw away the software that came with it
in order to get the extra memory for bigger spreadsheets.
So we were very constrained. We had a program,
Bob had a program really, really well to be able
to scroll and, you know, display. turn it all
into the screen. You had to turn it into the
screen dynamically as you scrolled. That's a
lot of programming. It has to be fast enough
for the repeat key. What we didn't know is that
the repeat key was done with hardware that isn't
based on time, but was based on an RC circuit,
which means it varied from computer to computer.
Our computer, you could scroll and stop, and
it would stop. But some, if you stopped, it still
had a few extra keys and kept going. But it had
to be that fast. That was a real challenge to
make it fit in memory, do all the things that
we wanted it to do. And how do we decide what
not to do? We wanted a great help system. We
wanted more functions. We wanted, you know, I
mean, you're just, you know, it's deciding what
not to do is a very tough thing. And we had to
make it easy to understand, you know, to learn.
And people learn by using a reference card and
a manual. They either read the manual or they
looked at the reference card or both that tell
them, if you push this key, it does that. And
we had to make that as simple as possible and
we succeeded. Yeah. And useful. Yeah, for sure.
I mean, it's still the most used, one of the
most used software. So VisiCalc was the precursor
to Lotus 123, precursor to Excel. And then Google
Docs and dozens and dozens and dozens of other
spreadsheets that have been built. Right. Linux
has their own open source version as well. You
know, what's funny is, you know, designing software,
often the competitor is the status quo. So you
created something that's like the status quo,
like any... No, the status quo was paper and
pencil and a calculator. I'm saying that it became
now that that has... No, it was a status quo.
It was pretty easy to beat us out if you were
on a more powerful computer because you had more
columns. You had graphing ability on the screen.
You had lots more memory. And if you happen to
know what people wanted, things we left out like
commas in numbers or the thousand separators,
the columns being different widths and some other
things. And there were certain things we left
out because they were hard to program or we weren't
willing to make certain compromises that other
products like 123 did do and did very well because
they had those. And then when there was new hardware,
so Excel could take advantage of fonts. Just
think what multi -plan from Microsoft put in
and then Excel used, which was the ability to
have a single line between each cell, to have
a grid visible. That's a huge advance for many
applications. And they were able to do that because
the hardware, initially of the Macintosh, the
early Macintosh that they did, let you do that.
So there are all these advances that you could
put in new products. With Excel, you could run
it under Windows. And it came bundled with your
word processor with Office. And so whenever you
bought your computer, it came with it. So that
was a reason that people would go with that.
So 123 was just much better for the IBM PC. It
was tuned so much for it. was so tuned to the
IBM PC, which had just come out, was now available
from a major manufacturer. People now knew PCs
were useful. So they were finally all buying
them. So Jeff Moore's, you know, inside the tornado
and about the early adopters now going to the
early majority where there's a huge uptick. And
IBM PC was the computer of the time. And the
spreadsheet of the time was one, two, three.
And it came even with a piece of plastic, molded
plastic that went over the keyboard's function
keys to label them. So you knew where help was
and you knew where this was and all that because
you had physical labels on this piece of plastic
tied to the hardware so much. So why not buy
that? And so what did 123 cost like? for $500,
the computer with the printer and all that cost
you several thousand, like three, four, five
thousand dollars perhaps for your computer with
a printer and all the things that you, the screens
and the stuff that you wanted. So the software,
who cares? But you bought it for the software,
which is what it means to be a killer app. That
it's worth buying the hardware for that software.
Yeah. There you go. But thanks for tying that
back in. Yeah. So the idea of having that killer
app. The matter is when you already have the
hardware. Right. So like when Netscape came in
with a nice browser, the idea of that type of
a browser, there had been browsers before, but
they weren't integrated with screen, with pictures
and stuff the way that it was. There were just
a few and it was available for free. All you
needed was a personal computer running Windows.
and you needed to have that computer on a desk
and you wanted it connected to the local area
network to be able or somehow connected to what
could be the internet or whatever. You needed
that hardware all in place. And companies had
that because of Lotus Notes, a product that Lotus
did that was really good for email and workflow
and all sorts of stuff. And it was one of the
premier applications for Windows to help Windows
being developed. And so the hardware was already
there. You didn't need a killer app. All you
needed was the good app. You don't have to buy
the hardware if it was already there. And then,
um, when Napster came around, you know, you don't
need to buy anything. Right. When, now when you
needed a music player is when the MP3 stuff,
you know, and Napster and whatever came in, that
was, you sort of needed that. Uh, if you wanted
portable music. But when, you know, when Facebook
came in, they already, students already had computers.
They already were connected to whatever. So they,
they didn't have to be a killer app that way.
They just needed to be a useful app. Right. Almost
like use the system that's in place. to then
kind of to move things forward. Once you have
a fertile environment, if you want a fertile
environment, meaning it's a thing where it's
a good place to do different types of applications
and stuff that lends itself to innovating in
different areas. So the PC was a fertile platform
of sorts. And once you have that, you can do
things. for that. And we're seeing that today
with AI, where people are using AI systems that
other build as the fertile platform in which
to build something else, now that I have something
that can take inputs and then do outputs in English.
In terms of win something, it's hard for adoption
versus easy for adoption. Right. Now we have
adoption in... Just a thing I saw that David
Smith wrote. David Smith wrote a product called
Widget Smith. which is on the Apple iPhone that
lets you put widgets, customize them. And he
writes about it, how he was building it originally
for the watch and then he was into customization
and Apple made it possible to do that. And then
he came out with it and it started selling like
normal. Then somebody put up something on TikTok
where they said, boy, you can customize exactly
what I want. Customize my screen to be exactly
what I want. Within hours he became the number
one app for a week or for two weeks on the Apple
iPhone Right. He sold millions and millions.
I think what do you say a hundred million copies
or something didn't sell them all But he you
know, they're free. It was for free, but you
can buy the the upgrades You know, I mean within
within days or within hours today is quite different
Right how fast things how fast you can go up
and how fast you can go down. Yeah That's for
sure. I wonder, are there any new criteria that
you add in to making a killer app today? I haven't
thought about it much, but I think the whole
thing of whenever there's a new capability, look
at what is it good for and better than anything
before by not a little bit, but by a lot, 100
times better type of thing. Right. Now that you
have that, what does that enable You know, so
let's take machine learning. Yeah, okay the old
machine learning It was used for visual stuff
to recognize things and really kind of cool on
the production line You know, you could see is
this a good one or a bad one and you could then
automatically You know do some quality control
and stuff Well, what happens if you can put it
on a small enough ship to be able to detect people
by eyes? Being able to say that's a face All
you have to do is figure. Well, that's a really
tough thing to find faces. OK, but we figured
out machine learning and stuff. They figured
out how to do that. And they put it into cameras.
And we take this for granted. The fact that a
camera can figure out it's a face and then do
the autofocus there, not just the dot in the
middle, wherever you point it used to be wherever
you pointed it. That was the cool thing. Right.
Where you pointed it was where it focused this.
It's able to say, oh, I see a face. I see two
faces. Let's try to figure that out. What was
it good for? It was good for that. It was good
for lots of other things. So what is it good
for and that we could do that we couldn't do
before that's worth it, that pays for itself
almost immediately? I say within two weeks type
of thing. When you got desktop publishing, a
laser printer, something like PageMaker or later
Word and stuff like that, being able to... get
your output and, you know, do a newsletter or
whatever. The old days, you had to type it up,
send it out to a typeset, you know, to a person
who would typeset it and then print it to the
printer and come back. It paid for itself the
first time you used it. There was a two -week
payback for a lot of these things. VisiCalc paid
for the Apple II. You know, there were certain...
Programs you could use to do financial forecasting
not as easy to use and whatever but for your
particular application You could use a time -sharing
system six hundred six thousand dollars you a
month. Well for six thousand dollars That's much
more than I to get the top end. You'd get an
Apple to a nice monitor Sony's Trinitron probably
or something that I could use to watch TV and
a printer the best printer that I could get at
the time, you know, a daisy wheel printer or
something. So it pays for itself the first time
I use it in a couple of weeks. No -brainer. I
mean, it's obvious. None of this one -year payback,
two -year payback. So, you know, cell phones,
the first time you're stuck somewhere and can't,
with a flat tire that you can't change, in the
middle of nowhere, and you find out you could
have had a cell phone you know when your mother
does that you buy her a cell phone right away
i did that you know she had a flat tire and you
know whatever i said mom you're getting a cell
phone right this is many many years ago um when
but or late enough for cell phones so that's
um you know when is it obvious that it pays for
itself the first time you really need it or something
so those that sort of gets into today's stuff
um and separately is user interface. Why was
my spreadsheet implementation versus other calculating
things that came out? Why was mine so successful?
Was it just the marketing? Was it just the cool
name? Was it our implementation? Was it, you
know, I mean, there were other, you know, was
it because it was on the Apple II? I mean, why,
what particularly, what was the things that made
it, you know, but a lot of it, is user interface
and stuff, and what were they good for, and is
it fun? In the early days, when I first did computerized
typesetting, in our office we had a typesetter,
thing used chemicals and stuff, whatever that
you could use as an output, like a printer. And
out would come this paper, little shiny paper,
that you could do fonts. you know, Times Roman
and then Italic and all that stuff and different
point sizes. And the first things we did were
look like ransom notes. These are our status
reports, you know, where we could have just made
it look like a typewriter, but no, we wanted
to do all that. And sure enough, when the first
word processors came out that did fonts with
laser printers, what did everybody do? They went
overboard. They use it for everything, you know,
and they made fonts like, you know, and it was
the most garish thing. And now we've sort of
settled down. Right. The same thing. It's the
hammer. Once you have a hammer, everything looks
like a nail. I'll use I love this new thing.
Let's use it for this. Let's use it. I don't
know what it's really good for, but I'll use
it for everything. Right. With today's stuff,
what's it good for? You know, I can ask it a
question, it gives me an answer. That's good.
Well, it turns out it's not very good for certain
answers. Right. And it's really good for certain
other answers, you know, and certain type of
questions. And understanding that is important.
Yeah, 100%. And I think the challenge right now,
I mean, you've seen all of the cycles, you know...
Lots of cycles. It goes back, you know, back
to the Stone Age. Right. The AI winters now...
Look at my wheel. Now we're in this, people argue
where we are in terms of the, you know, the hype
cycle for AI right now. I think we're really
high. I think some people are getting into that
trough of disillusionment also a little bit,
but I wonder for you seeing it from your perspective,
how do you separate kind of the hype of AI and
the reality of it? Well, a lot of people hyping
it don't know what it is or what its limitations
are. Right. What I used to say, let's see, when
this account first came out, you could explain
it or show it to people, okay? And if you showed
it to a computer person, they look at it and
say, what's so special here? I can write a program
that does that, you know, and I can do better
and stuff like that. You show it to a regular
person, a normal person, they say, computers
can do anything. They can forecast the weather.
They can tell you whatever they do. What's so
special about that? But if you showed it to people
who actually did that stuff, they would start
shaking and say, here's my credit card, please
take it, give me that, give me that, give me
that. So the same thing's happening with a lot
of stuff with like AI today, is people look at
it and they say, they don't really understand
its limitations or what's what, but it looks
like it's real smart, like a person, it can do
things I can't, and I can ask a question because
of an answer. And it sounds like a person doing
that who is that confident. must be right and
it's a computer you know and it's trained one
on the whole web and the web must have everything
there must be everything on the web well we know
that the not everything is on the web and not
everybody puts stuff on the web so it's not trained
on everything and the people who are training
it to check it don't always know the right answer
right um etc for me what i need in my application
stuff i just saw with today or something that
a lot of training was on certain populations
of medical stuff is in the literature because
they only did the studies on certain people they
could get to. Right. But that's normal people.
I mean, you should understand the, you know,
well, maybe from my experience, you know, it's
different that the training now is based on that
as that's what it is. There's an old joke. about
the policeman sees this drunk and he's looking
around on the ground and he says, what's the
matter? And he said, well, I lost my wallet at
23rd and Walnut. He said, well, oh, well, we're
at 19th and Walnut. Why are you looking here?
He said, well, the light's better. And so that
is that when we have the data, we use that, whatever
we can get. Whatever is easy to measure is the
thing that we measure even though that may not
be a good measure but there are people who don't
know that who just assume that it must be all
-knowing and It's better than me. So therefore
it's good. So so we learn people who really use
it learn to use the tool, like those that learn
to use the spreadsheet, those that learn to use
the word processor, those that learn how to drive
a car versus, if you don't know how to drive
a car, you're gonna crash and you might kill
yourself. Once you've learned how to drive a
car and stuff like that, then you can do also,
and if you're really good, you might be a race
car driver or something and can do it, but you
have to learn to do that. dancing, you know,
learning how to dance. You know, some people
are naturals and she goes, look at one other
person. The same thing about these things. We
have to learn how to use them and what they're
good for. Unfortunately, they keep changing.
So I become a prompt master. I'm really good
at prompting for GPT -4 for three. And I know
all of what is good and I know the way to do
it and how to, you know, and then of course it's
changed. and suddenly my skills may not be as
useful, things that I worked really, and maybe
there are different ways to convince it for this,
and that's just with one type of input, with
one type of output, and it's changing so fast.
That's not a recipe to depend upon it. it's great
for advancing. Are you going to depend, let your
life depend upon it? Reporters would ask lots
of questions and learn lots of stuff about an
air, it's something and report about it. But
if you knew that particular thing, you were at
the event and you knew whatever, you knew what
they wrote isn't exactly right. Close, but not
necessarily exactly right. Especially if they
weren't, you know, had been in that area and
whatever. There's the same type of stuff here
that we have something that, yes, it's pretty
good. Is it perfect? Well, right now we're learning
like encoding. We're doing vibe coding, whatever.
You have to check what it does. Yeah. Well, if
you don't know how to check, you know, if you
don't know the type of failures that you might
run, and we don't even know the type of mistakes
that it might be prone to, that we have to look
for what type of failures and what uses will
be used for. And it keeps changing. So what it
was good for, how do we know if it's still good
at the thing that I want to do? It's going to
take time. to settle these things out. Is it
still better than not using it? For some things,
it is. And some people who know how to use it,
like any tool, can get great advantage. Anybody
who had a spreadsheet, suddenly, in the early
days of the Apple II, and nobody else knew they
had it or what it was, thought they were... these
geniuses you know they unbelieve you did you
know what people would do is they would get a
job quote it for this many hours of money go
away and then eventually when they needed to
do it spend the 15 minutes use the spreadsheet
to do the answer and then come up with it and
wow whatever i got my money's worth um you know
we're we're in that that world for certain things
right so we'll see um and what was the best way
my idea of uh using the paddles was not a good
idea on the Apple II. The mouse is good for certain
things on the spreadsheet, but the arrow keys
are good for others. Using the keyboard is better
than reading into voice input for a spreadsheet
with numbers and words and all that. You can
tell by anybody's texting who texts you voice.
You know whether, do you want to depend on your
doctor using that type of stuff where the difference
of voice recognition is life and death? You may
want different type of inputs with the right
type of feedback. The feedback that we get from
today's systems sometimes is, well, I can read
it and I can tell where it's good or bad, or
something that doesn't matter because anything
that gets me in the ballpark. In other words,
I'm trying to get I'm trying to get within the
ballpark. I don't have to get to the seat. I
need to be able to get to the ballpark. So therefore,
anything that gets me to the ballpark or nearby
most of the time is a help. And you have to understand
that. I mean, can you imagine if with today's
systems at the error rate in terms of even small
or big, you're using it to get to your seat in
a ballpark or anything else. You think a lot
of us would end up in the same seat by mistake
and not in our others with, you know, I mean,
we're talking about in a stadium, if you have
0 .0001 mistakes, you're still going to have
some people who are in the wrong seats. So you
have to have the error correction and stuff.
So I don't know how we're going to do that. I
know that there's a real challenge with the interfaces
to these different models that are using the
technology that the LLMs use. and the type of
training and whatever, what is the best use of
this technology for what type of things? I'm
really excited by new hardware that's coming
out that will be able to do it apparently. Some
of the new phones and stuff, the particular chips
in them are powerful enough and coming with the
right software to be able to do certain things
even on... in your hand and what's that gonna
open up? We're not gonna just be talking. You're
not gonna wanna talk to it. You're in a noisy
environment. You're gonna wanna be able to do
other ways of interfacing. We're learning. Remember
with a lot of the visual stuff, it uses hand
motion. Well, is that really the best way or
is it better to have a controller? We now move
back to controllers. Remember the iPad? Right,
the touch screen. Yeah, with no pen. Well, now
you sort of must have a pen to be able to use
some of the stuff on the iPad for some of the
applications. But we wouldn't have a pen when
we can do touch. But it turns out that the interface
is better when you have the sharp point. and
the ability that people have, but you also need
others for those that can't use their hands that
way. There's accessibility. There's where I'm
using it, noisy environments. Where I can't,
how do I present things to you so you know what
to check? Write me a 10 ,000 word treatise on
such and such. Well, how am I gonna check if
it's right? How do I know where to check? I mean,
it's the teacher will just use an LLM to tell
you if it's right or not. No, I'm just, you know,
and what's that trained on, you know, what, you
know, what class is it then? And do you trust
the person who trained the train, you know, to
train those systems? There are people you can
read about the people. They get some pretty good
people to train these systems, you know. to say
no, you know, no, LLM, you got it wrong, you
know, whatever, and it fixes it and stuff. They
still do weird stuff, you know, weird stuff.
I think it's that's one of the flaws, actually,
that we're using human preferences. So it's.
Trained to output things that humans prefer and
that's sometimes just off of like what you see
and what you look like and sometimes the formatting
of an answer can cause you to like it more than
some other answer and the fact how Factual it
really is isn't always checked. There's so many
there's so many challenges for it We're so early,
but it's interesting to see the way the pendulum
swings It's interesting to see hat what the new
interfaces are gonna are gonna be like for it.
Yeah Um, you raised so many good, do we need
new hardware? Do we knew? Well, first of all,
we knew, we knew we needed new hardware to do
the calculation. So therefore they've been putting
the neural engines and all that, and all these
hardware. That's okay. So we've done that, that
at least, but is there other hardware? Like we
needed bitmap displays, meaning you could, you
could address every dot on the screen in order
to be able to do, you know, uh, mouse and stuff
like that. What other hardware, what interfaces,
input -output devices, do we need for the particular
applications for which these things are useful
for? Like if you don't have an autofocus motor,
what good is being able to recognize where the
person isn't focusing, right? It'll be interesting
to see how those co -evolve with each other as
these new capabilities, you know, arise and emerge
and then there's going to be new hardware. I
don't think we know yet what the right interface
is exactly to take advantage of all of these
things. But it is interesting for each of these
things, which interfaces for each of these things.
Right. Is it going to be right for we still know
people still buy laptops even though they have
just as powerful phones. Why? Well, there must
be something about that laptop that is better
for them that they're willing to spend a thousand
bucks. or a few hundred to 2000 bucks for that
laptop versus their phone that they're also spending
hundreds of dollars or a thousand dollars for.
And so we have to sort of understand that whole
thing. Yeah, yeah. You raise so many interesting
points, so many things to think about. It's hard
to be an engineer because there's so many questions.
And how do you decide, now's the time for me
to bet on this? This combination, is it too early
or is it too late? Am I choosing the wrong combinations,
the wrong application? And then there's the old
market fit, do people care? And a lot we don't
know. That's why it was interesting to see David
Smith, what he wrote about how he... He didn't
know that there were I mean he cared about fonts
particular you choosing this font and this color
and this type of little layout whatever the style
for a clock or the showing the weather because
you cared about He cared about that, but he didn't
know that there were a lot of people that did
care He thought most people didn't because on
other products they didn't care But it turns
out on your home screen. There are people who
view the home screen like they view the case,
right? and they view their clothes or something
like that, that that does matter to them. And
that's how they express themselves. He happened
to hit the right combination that others did
not. They also had some stuff, but they didn't
allow you to do the chartreuse that you wanted
or something like that that made all the difference
in the world to some people. And that was good
for them. That's true of a lot of you don't know
the space. You're not that type of surgeon. You
may not know the needs for that type of surgery.
If you're not that type of business person, you
may not know the needs of that. Oh, the person
using it works in this environment, physical
environment. What must that hardware have? They
only have one hand to do because the other hand
is busy. So they have to do one -handed operation.
There are just so many things. That you and you
don't always know it and they have to go right
for you to be successful. Yeah, it's a good point
I mean, yeah taking into account When you're
trying to design anything right, you know who
your end user is what what environment they're
gonna be using you're Offering in the context
that they're in the experience that they've had
in trying to create something that can kind of
address You know all of those things. That's
why I think that Robots, you know humanoidish
robots might be interesting in that So much of
our society in the world around us is built around
a person within a certain range with one or two
hands, you know, with this many fingers and whatever,
and with this ability to move, et cetera, and
this strength, you know, and speed that therefore...
maybe those input -output devices, which is the
humanoid robot, may be appropriate to fit in
situations that we couldn't automate without
specialized machines. It's a real challenge,
but what are the physical configurations that
are appropriate for our physical world? You know,
the fact that a cell phone fits in your pocket
matters when the first scientific, really good
scientific calculator came out, the Hewlett -Packard
calculator, HP 35. The story is that Packard,
Hewlett -Packard came to his engineers and said,
I want something that fits in my pocket, my shirt
pocket. They took out their measurement and they
measured his pocket. And sure enough, it could
fit. It was, you know, a certain thing that he
could fit in his pocket because he needed to
be able to carry it around, you know. Um, before
that we had slide rules and we had special strap,
leather straps that fit them on our, on our belts.
And that wasn't for regular people. This you
wanted to, and that size calculator became dominant.
Right. And same thing with the phone. It fits
in the pocket ones that were too big, weren't
right at first. Right. Once they, once the, um,
like the Nokia, uh, small cell phones came out
that you could fit in a purse. and you can fit
in your pocket. That fitting in a purse and fitting
in a pocket make a difference. What's really
nice about the new iPhone Air, it's really, really
thin and fits in your purse a lot better, among
other things, which is packed. You put your phone
right. Right. I mean, going back to what we were
talking about earlier in terms of like how the
pendulum swings back and forth. Yeah, like cell
phones were smaller at one point in some ways,
and they now have gotten bigger and now they're
going back to being a little bit smaller and
thinner. And then there's other tradeoffs. Now
we're doing folding together. Right now there's
the back to the flip. And then there was a time
when who would ever have a full touch screen.
And then there's other tradeoffs in terms of
when you make something so small yet so interactive.
right, there's trade -offs in terms of battery
life, right, and things like that, and then you
have to weigh, you know, those trade -offs, so
that goes into... What's the sweet spot? We don't
know what the sweet spot's going to be, and luckily,
lots of people are trying, and we're seeing what
sticks. Right, right. Which I guess is just the
exciting thing about technology and evolution.
Always been. Yeah. you know, look what happened
with automobiles, you know, and stuff, you know,
automatic transmission, you know, and stuff like
that, you know, well, anti -lock brakes and whatever.
And then something's caught on and then it became
standard. You know, I don't know how, how to
drive a, you know, an older car that doesn't
have the backup checking and a backup camera
and whatever, you know. And then we become so
used to it, you know, then you start to become
so used to it. Yeah. Which, which is. That's
why I'm really scared in my car that doesn't
have those. You know, it has a backup camera,
but one of my cars doesn't have the sensors.
Right. And now we're so used to hearing that
sensor when you turn the blinker on that, you
know, that if someone's in your thing, I, you
just always need to check, especially as you
get your neck back and all. So coming towards
the end, I have two questions that I like to
ask. What advice would you give your younger
self? Maybe when you were kind of just say starting
your career. Oh the career advice as opposed
to personal advice Well, you could do both but
personal advice is yes. She's the right one Okay,
so that's you know, okay that's very important
finding the right that's very important very
important a finding a partner is is You know
important and so what advice what would I want
to do the different? I mean because I I really
like how things turned out I mean, things could
have turned out different where I could have
made lots and lots of money, which I did not
because of various business questions and stuff
like that. On the other hand, I like my life
and it's better than I expected it to be. So
I don't know what advice that would change what
I would have done except that, let's see, I mean,
choosing the right, you know, right horses to
get on. I mean, that's the, this, which technology
one to ride, when, how to make that decision.
But I still don't know. I mean, all the things
I've told you. Some of it comes from what I've
said way. I was saying back then that I learned
then that's kind of that's kind of tough You
know family matters enjoy Enjoy the good times
because they may be the good times There's movie
as good as it gets there are some things that
are as good as it gets enjoy them when you have
them When you have the really good time enjoy
that don't feel bad that it could be or whatever.
I've had some really Special things happen in
my life, you know and been at unusual situations
and no longer have that So the more that hopefully
I enjoyed them at the time that I could enjoy
them, that's good. Right. There's like a great
quote that you reminded me of, of it's like about
the good old days. Right. And sometimes you don't
know, like I wish there was a way to know you're
in the good old days before you've actually left
them. It's from the office. It's a good quote
that I recently came across again. Now, many,
many years ago, early in the days of Apple, Steve
Jobs came to speak at the Harvard Business School
to an entrepreneurship club or something. And
I went to go visit. I was a graduate at the time.
I had graduated at the time. So I went back and
I raised my hand and said, hey Steve, you know,
where are you, what are you expecting for the
future? Where are you trying to go? And Steve
said, it's the journey. He was not looking for
where he was. It was the journey. And that, you
know, kind of, oh. You know, I had asked the
same thing Gates at some point and, you know,
Gates had, I'd asked him about some stuff and
he talked about how he was going to give money
away. And he sure did, you know, that, you know,
this was his success. Look, we'll look in the
future and if he's successful, he'll, uh, you
know, he'll, he'll get back that way. So that
was the thing about enjoy the journey. I always,
when I did accompany What I did was when I was
paying people and sending stuff I said I want
to do it that when you look back if we if we
fail I want you not to Feel bad about that. You
wasted your time here. I wanted you to say, you
know, I wish I hadn't You know, I want to make
sure that I paid you enough that it isn't costing
you to to become poor or something like that
I wouldn't that was now that may not be the best
business But I was a feeling I had that I wanted
you want to do it so that you won't regret That
you were doing that stuff because of what doesn't
if it doesn't pan out from that viewpoint so
do things that's the thing of doing it what you
love and You can't always follow that. You don't
always have that option. But if you can that's
pretty good Yeah, it reminds me of your earlier
remark. Life's a journey, not a destination.
We might be seeking all of these things, but
it's really about the journey that gets us to
where we are. I'm afraid that this last question
is too close to that last one, but I always ask
it, so I have to. What has a career in tech taught
you about life? I love tech, and I love that.
And I'm happy that I did it. So I'm happy I chose
that and that I'm still doing it. I'm still programming.
You know, I'm still writing and I like, I'm still
programming a lot by hand. You know, cause I
like, it's like people who like to paint. They're
not Picasso. They're not Rembrandt, but they
like to paint. Even though they're not, you know,
they're people like to play basketball. They're
not LeBron, but they still like to play. You
don't have to be the best. So that's, that's
one of the things about, you know, about your
career. But you also should think about what
you're good at and what you like. You know, there's
these retrospective I saw last night about Robert
Redford and about what he went through figuring
out what his calling was in life, where, you
know, he realized he was in arts and he wanted,
he thought he was going to be a painter. to france
and stuff and he thought he was going to be a
painter but then he ended up you know in school
for for drama and stuff like that and a teacher
who told him you're you're good to stay in that
you know type of stuff and then he did he ended
up on stage and then he ended up in one film
and then he ended up behind the camera you know
as a director and figuring out what and then
then of course Sundance in terms of giving it
back in terms of his love of The outdoor of of
outdoors and stuff like that. That's an interesting
thing to think about Look at people's paths how
they got where they got especially ones that
seem happy with the path that they took and Why
and so that's a good reason to read biographies
of various people that have done stuff to talk
to people Who would not have written a biography
or there being a biography of them? To find out
and about that somebody contacted me that I knew
from, from high school and said, Dan, I hear
that you took pictures back in the old days and
you have pictures of the play I was in. And so
that's, you know, yeah. Um, and you still have
them. I said, yeah, I scanned them for one of
the other people who was his kids, didn't believe
that he was ever in a play and I was the lead.
And so sure enough, so I had the pictures and
this turned out to be a major memory in her life.
I was into photography. And I had pictures, I
had great pictures of her. And I found out something
I didn't know. She was a shy person and she had
a crush on a guy. And she got Maria and he was
Tony. And they, boy, I have pictures. You got
to live out a fantasy. You get to live out all
sorts of fantasies in life sometimes that you
don't expect. I mean, they're not married and
they haven't, you know, they're friends, but
they, you know, they're different parts of the
world, whatever. But looking at that piece of
her life. And that turned out to be a major,
in hindsight for the two of them, that play turned
out to be, in high school, turned out to be a
major good point along the way. I'm sure in terms
of other things in their lives, they've had some
other high points. I mean, I'm a grandparent
and there are just such incredible high points
you can get as a grandparent, and stuff like
that. And being a parent and the same thing,
So enjoy those and they will happen and don't
belittle them when you have them. So that's,
that's advice that I have, including in your
career. You know, I mean, even if you're going
to get fired at some point, you may have, you
got to speak in front of a huge crowd and they
clapped because they liked what you said and
you liked that feeling. Right. Maybe, I don't
know. Or you have a product, you built a product
and people actually use it. And then you get
I did a product where you draw on the screen,
but you can draw big and it shrinks it down.
You could mark up PDFs, okay? So on an Apple,
on an iPad, even though you didn't have to write
real small. So I hear from this mother who said,
thank God, I really love your product. My daughter
has cerebral palsy and now she can do her homework
herself because she's able to draw big. on the
homework and she doesn't have to tell me what
to do. Thank you. I mean, that was like, I still
remember, it was years ago. I still remember
that moment. So you never know. There's a line
in the play rent, you know, about helping other
people and stuff like, I mean, and you know,
this thing about the relationship with other
people, you know, is part of what it's all about.
So enjoy those. If you do, there may be other
things you enjoy more, enjoy them. Ice cream.
I think it's really good advice and it reminds
people basically, like, you never know what the
consequences of your actions are, but if you're
kind of doing it and you have the right intentions
and you're doing things for the right reasons,
you could make an unbelievable, you know, you
can have an unbelievable impact on other people.
I think that, yeah, that's really beautiful.
The speech that I give that the TED Talk is a
piece of, I've had people years later say that
I was inspired by that and I went and did this
thing totally different and whatever, and this
product that we all know about is there because,
thank you. Yeah, it's amazing. Yeah, that's incredible.
Or, thank you for the spreadsheet. I was helping
this other person learn the spreadsheet because
they needed it, and now they're my husband. Thank
you very much. Yeah, and you never know. You
never know. You never know what could happen
because, yeah, because doing one particular thing
could then lead to unbelievable impacts and unbelievable
consequences. For others or for you. Yeah. Yeah.
I think that's a really good place to wrap up.
Dan, this was such a pleasure. Same here. I appreciate
it. I love your questions, the areas where you're
going after and stuff like that, and knowing
your background makes it even more so in terms
of your technical background and stuff. It was
really, really a pleasure to have you on. Thank
you for all of the incredible work that you've
done. Yeah, your TED Talk is inspiring. It's
inspired me. Your case study was one of those
first ones. Reading that and, you know, connecting
with you is a real honor and a real pleasure.
I really appreciate you taking the time to chat.
Let me pick in your mind. My pleasure, too. Thank
you, Dan. the first electronic spreadsheet, and
the killer app that helped launch the PC revolution.
We explored what five decades of platform shifts
teach us about today's AI moment. Dan's framework
is simple but powerful. Breakthrough innovations
must be a hundred times better, not incrementally
better. The same questions he asks about spreadsheets
and word processors apply to AI today. What is
this genuinely better at? What does it enable?
What trade -offs will people accept? Does it
pay for itself immediately? Most importantly,
Dan reminded us that we never fully know the
impact of what we build. Whether it's a mother
whose daughter with cerebral palsy can finally
do her own homework, or a couple who met learning
spreadsheets. The moments worth remembering aren't
the product launches or exits. They're the unexpected
times. when your work changes someone's life
in ways you never imagined. Thank you for listening.
Be sure to subscribe and share with a friend
or colleague. Until next time, keep on learning.