October 1, 2025 • 21 mins
This week, we're doing something special and sharing an episode from another podcast we love: The Humans of AI by our friends at Writer. We're huge fans of their work, and you might remember Writer's CEO, May Habib, from the inaugural episode of our own show.
From The Humans of AI:
Learn how Melisa Russak, lead research scientist at WRITER, stumbled upon fundamental machine learning algorithms, completely unaware of existing research — twice. Her story reveals the power of approaching problems with fresh eyes and the innovative breakthroughs that can occur when constraints become catalysts for creativity.
Melisa explores the intersection of curiosity-driven research, accidental discovery, and systematic innovation, offering valuable insights into how WRITER is pushing the boundaries of enterprise AI. Tune in to learn how her journey from a math teacher in China to a pioneer in AI research illuminates the future of technological advancement.
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Transcript
Episode Transcript
Available transcripts are automatically generated. Complete accuracy is not guaranteed.
(00:00):
Hey everyone, Chain of Thought host Connor Bronson here.
We are switching things up a bitthis week and showcasing an
incredible episode from another podcast that we think you'll
love, Humans of AI Podcast, which is produced by our friends
over at Ryder. As some of you may know, we're
big fans of the work they're doing.
We actually had their CEO and Cofounder Mei Habib on our
(00:21):
inaugural episode of Chain of Thought last year, and we
definitely encourage you to diveinto our back catalog to check
out that episode as well. The episode we're sharing today
is a fascinating conversation with one of writers, AI research
scientist Melissa Rossa. Her story, the powerful reminder
that sometimes the biggest breakthroughs come from the
perfect combination of curiosityand naivete.
(00:42):
Because as Melissa discovered, sometimes the best way to
innovate is to have no idea whatyou're doing.
It's a thought provoking lesson and if you enjoy it, we highly
recommend adding Humans of AI toyour podcast feed.
We'll be back next week with ourregularly scheduled programming
as we're joined by Aishwarya Srinivasan, but for now, I'll
let the team over at Humans of AI take it away.
(01:10):
Of course, at this point you have no notion of OEI, right?
You never came across machine learning this phrase.
You see text, right? Text is simple, I mean like
relatively simple. But you also see pictures,
right? So this is kind of what we were
trying to achieve. That's Melissa Russack, and what
(01:37):
she's describing, trying to figure out how to make computers
understand pictures and words. Well, that's machine learning,
except she didn't know that yet.She was just a math teacher in
China, tinkering with a problem that fascinated her, completely
unaware that she was reinventingalgorithms that had been studied
(01:58):
for decades. Which raises a question that's
been nagging at me since I firstheard Melissa's story.
How many times do we independently arrive at the same
ideas? How often do we think we're
being original when we're actually walking a path that's
already been carved? I'm Laura Weaver.
(02:20):
Welcome to HUMANS of AI. Today, we're telling the story
of a woman who accidentally became a machine learning
pioneer twice. And what that tells us about the
nature of discovery itself. Our story begins not in Silicon
Valley or MIT, but in Chengdu, the bustling heart of Szechuan
(02:43):
province, where ancient temples cast shadows on glass towers,
and where a young woman named Melissa was studying
mathematics, the very language that powers artificial
intelligence. But here's the thing.
She was restless. Mathematics is it's purely
abstract. So I said in mathematics I was
(03:04):
discovering that a part of me isnot developing like in
mathematics. I have a feeling like there is
this thing like you can spend 10hours on thinking about the math
problem and have no results. You can go to to Chinese and you
can spend those 10 hours learning Chinese Suns, right?
So you're going to see the progress.
What she was describing was the mathematician's dilemma.
(03:25):
Working in a realm of pure abstraction, where breakthroughs
can take years, where you might spend decades on a problem that
leads nowhere. She craved something more
tangible, more immediate. She wanted to see her learning
compound visibly, day by day. So she made what might seem like
(03:45):
an irrational choice. She decided to study Chinese
alongside math, not because it made practical sense, but
because it felt like the missingpiece.
So I just chose something maximally different from
mathematics, and that was Chinese.
I need to admit that Chinese because it's challenging, right?
(04:05):
It's a completely different system, like everything you know
from, you know, phonetic system.So you don't have an alphabet,
right? Completely out of your comfort
zone. What Melissa didn't realize then
was that she was training herself in something that would
become essential to her future work.
Pattern recognition across completely different systems.
The human brain that can switch between abstract mathematical
(04:28):
proofs and tonal Chinese characters.
That's exactly the kind of brainthat can see patterns in data
that others might miss. After graduation, she became a
high school math teacher, and itwas there, watching her students
struggle with handwritten Chinese characters, that
something clicked. I had a hobby like this is the
(04:49):
time when I discovered programming, so I started from
Action Script and Flash because that was how can I facilitate my
students to learn faster, to learn better, to memorize
better. She wanted to build something
that could recognize these handwritten characters
automatically. A classification system, she
called it. Here's the thing that gives me
chills about this story. Melissa was essentially trying
(05:12):
to solve the same problem that researchers at universities
around the world were tackling with sophisticated machine
learning algorithms. Except she had no idea that's
what she was doing. Of course, at this point you
have no now show in the eye, right?
You never came across machine learning this race.
She was like someone trying to reinvent the wheel, not knowing
(05:36):
that wheels exist. Except in her case, she actually
succeeded. So this is when he all started.
I think that was the my first data science project, my first
data science job that really pushed me into into discovery.
Only later did she discover thatwhat she had built was
essentially K means clustering, A fundamental machine learning
(05:59):
algorithm that had been around for decades.
Of course, like you come up withthe algorithm that is not
perfect and then you discover that actually people are working
already more than 100 years on this problem and there are
improvements to this. So it's like you upgrade.
So that was amazing. She didn't just stumble onto 1
established algorithm, she did it again.
(06:31):
Melissa's second accidental invention happened when she
started wondering about memory and self knowledge.
She wanted to build software that could track her Internet
activity and tell her something about herself that she didn't
already know. Maybe you had this problem like
when you wake up in the morning and you have this empty blank
page right? Like you're asking questions
like wanted to do today, right? Even before the 1st coffee.
(06:54):
What I wanted to achieve today, Who am I?
To solve this, she needed to figure out how to represent both
text and images in a way that a computer could understand and
compare. She needed to group similar
content together to find patterns.
And of course, if you try to do that, you naturally come into
(07:14):
machine learning. This is like the machine
learning because you see text, right?
Text is simple, I mean like relatively simple.
But you also see pictures, right?
So how do you represent pictures?
What she was describing, learning good representations of
data, is what we now call embeddings, and then clustering
that data to find patterns. Well, that's the foundation of
(07:36):
modern AI systems. That's what powers everything
from recommendation algorithms to large language models.
Right now, of course, like afterthose years in machine learning,
I would say like it's all about embeddings, right?
You just need to learn good embeddings.
So you need to have a good encoder model.
But back then she was just a person with curious question
about self knowledge, working with a team of linguists trying
(07:58):
to build something that had never been built before.
We actually spent a lot of time trying to design those features
ourselves, those embeddings, andtrying to come up, you know, to
the first system. She was reinventing neural
networks, embeddings, clusteringalgorithms, the entire
foundation of modern AI because she needed them to answer a
(08:19):
simple question, Who am I? There's something happening in
Melissa's story that goes beyondjust the coincidence of
rediscovering algorithms. She stumbled onto something
(08:41):
fundamental about how discovery works.
Actually, this is a piece of advice that I that I give.
Like if you have a topic, beforeyou start working on a topic,
think about how you would frame it yourself.
Because once you start reading papers, you will converge to
what they actually how they framed this problem.
And it's very difficult to escape from that box once you're
(09:03):
in the. Box This is the paradox of
knowledge. The more we know about how
others have solved a problem, the harder it becomes to see new
solutions. Melissa's accidental discoveries
happened precisely because she didn't know the right way to
think about these problems. But there's something even
(09:25):
deeper here. Melissa's current project, the
one she's working on now, takes this question of accidental
discovery into even more philosophical territory.
I did have a even a Wilder idea.So imagine that you collect all
artifacts that you see, all of pictures, like even sound,
everything that you can collect,even conversation with other
(09:46):
human beings. So this is your input, right as
a human being. And then try to train an LLM on
this input and try to ask the LLM what's my next action.
She wants to train an AI on everything she experiences,
every conversation, every image,every piece of text, and then
see if it can predict what she'll do next.
(10:09):
So I would love to train an alarm on the entire input, and
then I would love to check to what extent I'm random, to what
extent it can predict what I want to do next, like checking
do I have free will? Think about what she's proposing
here. If an AI trained on all of your
experiences can predict your next action, what does that say
(10:30):
about free will? Are we just very sophisticated
algorithms ourselves, following patterns we're not even aware
of? Usually how it's framed is you
have free will if your next action is not fully determined
by your history, like the entireinput that you get.
It's the ultimate version of heroriginal question.
(10:51):
Who am I? Taken to Its logical extreme,
and it connects directly back toher accidental discoveries.
If our thoughts and innovations are just the inevitable result
of our inputs and experiences, then maybe Melissa's accidental
algorithms weren't accidents at all.
Maybe they were the only possible outcome of her
(11:14):
particular combination of mathematical training,
linguistic curiosity, and teaching experience.
Building enterprise grade AI shouldn't be complicated, it
should just work the right way. At writer.com, we don't do
(11:38):
everything. We do one thing.
We build enterprise AI that unites business and IT business
teams. Build your own AI agents, no
code required. IT teams manage just one
platform, not a plethora of point solutions. ryder.com
creates AI tools that are safe, scalable, and smarter every time
you use them. That's why Accenture, Qualcomm,
(12:01):
Vanguard, and hundreds more aren't just doing enterprise AI
the right way, they're doing it the righter way.
Book your demo at ryder.com/demo.
At Ryder, where Melissa now works as a research scientist,
(12:24):
she's discovered something remarkable.
The same conditions that led to her accidental breakthroughs in
China are happening again, but this time it's by design.
Excellent thing about this placeis constraints at the very
beginning. And in writer what we emphasize,
we don't use customer data. So how do I develop a system
(12:45):
without data? And at first you can be angry,
right? I can't, you know, create a
solution and then you think about this of this is an
excellent constraint, right? Most AI companies today are
built on hoarding vast amounts of user data.
Ryder took the opposite approach.
No customer data, period. To most people in the industry,
(13:07):
this would seem like trying to build a car without wheels.
But for someone like Melissa, who had already reinvented
algorithms from scratch twice, this wasn't a limitation.
It was an invitation to innovate.
So actually the first model thatwe created was to generate the
data. So we trained a model to
(13:28):
generate the data and I think that was an amazing idea because
I would never come up with this if not given those constraints.
She's describing something that would become a cornerstone of
modern AI development, syntheticdata generation.
But this was years before it became mainstream, before
everyone was talking about it. Once again, necessity had LED
(13:49):
her to reinvent the cutting edge.
And there's something else happening at Writer that's
different from the rest of the industry.
What's interesting about writer is nobody told me how to do
that. They told me you will find a
way. Just take your time, look
around, you will find a way. This isn't how most tech
companies operate. Usually there are Rd. maps,
(14:12):
established methodologies, best practices copied from other
companies. But Ryder was betting on
something else, that the same curiosity driven approach that
led to Melissa's accidental discoveries could be channelled
into systematic innovation. When I joined had two people or
three people in the NLP team. Right now we have several teams
doing NLP and I remember my first task was training a model
(14:35):
for grammar. It was not obvious what an LM
can actually do the job well. I considered T5A large model
just to give you the scale rightnow.
That model was less than 1 billion parameters.
What she's describing is the difference between research and
engineering. Most companies in 2020 were
focused on implementing existingsolutions at scale.
(14:59):
Reiter was asking deeper questions.
What if we could build enterprise AI systems that
actually understand how businesses communicate?
What if we could make AI that doesn't just follow patterns,
but understands context and intent?
Everyone in the industry is relying on benchmarks, that's
true, but the benchmark is like a cherry picked use case.
(15:22):
There is one piece that is missing in all of that testing
and it's very time consuming because it's going to a human
being, asking the human being, could you please use that model
in production? Do you like actually talking to
the model? She's talking about something
they call the Vibes Test, and itreveals something profound about
how writer approaches AI development.
(15:45):
So we always say that the Vibes test is the most important.
After you satisfy all of those benchmarks, you go and do device
check. While other companies chase
benchmark scores, writers askingdoes this AI actually work for
real people doing real work? It's the difference between
optimizing for test scores and optimizing for human experience.
(16:08):
This approach has led to breakthroughs that go far beyond
what typical enterprise AI can do.
They're not just building chat bots or document generators.
They're building AI that understands the nuances of how
different industries communicate, that can adapt to
companies specific writing styles, that can actually
improve how people think and work with language.
(16:31):
Sky is the limit, right? Even with the current
technology, we can go very, veryfar, so it's only about the
imagination. This isn't just one person's
success story. Writer has created an
environment where this kind of innovative thinking can
flourish, where constraints become catalysts, where the
impossible becomes just another interesting problem to solve.
(17:00):
There's a larger pattern here that goes beyond Melissa's
individual story. Throughout history, some of our
greatest discoveries have come not from experts following
established paths, but from outsiders approaching problems
with fresh eyes, from Kepler discovering planetary motion
while trying to find the music of the spheres to Darwin
(17:21):
developing evolution while studying to be a clergyman.
But what writer has figured out is how to systematically create
the conditions for this kind of breakthrough thinking.
The thing about quantization anddistillation, every model is
different, right? That's why we do this extensive
testing, because we want to knowthe characteristic of the model,
how to design a system on top ofthe model.
(17:44):
What she's describing is deep, fundamental research into how AI
systems actually behave. Not just how they perform on
tests, but how they think, how they fail, how they can be
improved. This is the kind of work that
pushes the entire field forward,and it's paying off.
While other companies are racingto implement the latest trending
(18:06):
model, Writer is building AI systems that work reliably in
the messy reality of enterprise environments.
So if you have a start up right and you rely on API and then
suddenly the API provider changes the model, your entire
business is in pieces, right? Because everything that you
created so far on top of it, it stops existing.
(18:27):
Writers approach understanding their models deeply, controlling
their own infrastructure, optimizing for real world
performance rather than benchmark scores.
This isn't just good engineering, it's the foundation
for the next generation of enterprise AI.
But here's the twist. Now that Melissa knows about
machine learning, now that she'sworking with a team of world
(18:50):
class researchers, can she stillhave those accidental
discoveries? Or has knowledge become a
constraint? Those tokens that happened
before are very important for the current token.
If you change anything in past tokens, you would diverge from
the trajectory that you're not right now.
So there is a possibility that you would not end up getting the
(19:11):
token that you have right now. So I've been thinking about my
token right now and I'm really happy where I am.
She's using the language of AI tokens and trajectories to
describe her own life. And maybe that's the point at
writer she's not just applying her curiosity to solve
individual problems. She's part of a team that's
rethinking what enterprise AI can be.
(19:33):
The accidental algorithms of herpast have become intentional
innovations. And for other researchers and
engineers who are tired of chasing the latest hype cycle,
who want to work on problems that actually matter, who
believe that the most interesting breakthroughs happen
at the intersection of constraints and creativity,
(19:55):
well, Reiter has proven that there's another way to build the
future. Melissa Rusack is a research
scientist at Writer, where her team has grown from two people
to multiple specialized groups, all working on the kinds of
fundamental problems that push the entire field forward.
(20:18):
The next time you find yourself approaching a problem without
knowing the right way to solve it, remember Melissa's story.
Sometimes not knowing is exactlywhat you need to see something
new. And sometimes, if you're lucky,
you might find yourself in a place that rewards that kind of
thinking. Thanks so much to Melissa Rusack
(20:42):
for sharing your story. I'm Laura Weaver and this has
been Humans of AI. Thank you to everyone for tuning
in this week, and a huge thank you to the Humans of AI team
over at Rider. If you enjoyed this conversation
with Melissa, make sure you go check out Humans of AI wherever
you get your podcasts. And don't forget to check out
(21:03):
our own back catalog for our interview with Writer CEO Mei
Habib. Thanks for listening and we'll
see you next week.
Hey everyone, Chain of Thought host Connor Bronson here.
We are switching things up a bitthis week and showcasing an
incredible episode from another podcast that we think you'll
love, Humans of AI Podcast, which is produced by our friends
over at Ryder. As some of you may know, we're
big fans of the work they're doing.
We actually had their CEO and Cofounder Mei Habib on our
(00:21):
inaugural episode of Chain of Thought last year, and we
definitely encourage you to diveinto our back catalog to check
out that episode as well. The episode we're sharing today
is a fascinating conversation with one of writers, AI research
scientist Melissa Rossa. Her story, the powerful reminder
that sometimes the biggest breakthroughs come from the
perfect combination of curiosityand naivete.
(00:42):
Because as Melissa discovered, sometimes the best way to
innovate is to have no idea whatyou're doing.
It's a thought provoking lesson and if you enjoy it, we highly
recommend adding Humans of AI toyour podcast feed.
We'll be back next week with ourregularly scheduled programming
as we're joined by Aishwarya Srinivasan, but for now, I'll
let the team over at Humans of AI take it away.
(01:10):
Of course, at this point you have no notion of OEI, right?
You never came across machine learning this phrase.
You see text, right? Text is simple, I mean like
relatively simple. But you also see pictures,
right? So this is kind of what we were
trying to achieve. That's Melissa Russack, and what
(01:37):
she's describing, trying to figure out how to make computers
understand pictures and words. Well, that's machine learning,
except she didn't know that yet.She was just a math teacher in
China, tinkering with a problem that fascinated her, completely
unaware that she was reinventingalgorithms that had been studied
(01:58):
for decades. Which raises a question that's
been nagging at me since I firstheard Melissa's story.
How many times do we independently arrive at the same
ideas? How often do we think we're
being original when we're actually walking a path that's
already been carved? I'm Laura Weaver.
(02:20):
Welcome to HUMANS of AI. Today, we're telling the story
of a woman who accidentally became a machine learning
pioneer twice. And what that tells us about the
nature of discovery itself. Our story begins not in Silicon
Valley or MIT, but in Chengdu, the bustling heart of Szechuan
(02:43):
province, where ancient temples cast shadows on glass towers,
and where a young woman named Melissa was studying
mathematics, the very language that powers artificial
intelligence. But here's the thing.
She was restless. Mathematics is it's purely
abstract. So I said in mathematics I was
(03:04):
discovering that a part of me isnot developing like in
mathematics. I have a feeling like there is
this thing like you can spend 10hours on thinking about the math
problem and have no results. You can go to to Chinese and you
can spend those 10 hours learning Chinese Suns, right?
So you're going to see the progress.
What she was describing was the mathematician's dilemma.
(03:25):
Working in a realm of pure abstraction, where breakthroughs
can take years, where you might spend decades on a problem that
leads nowhere. She craved something more
tangible, more immediate. She wanted to see her learning
compound visibly, day by day. So she made what might seem like
(03:45):
an irrational choice. She decided to study Chinese
alongside math, not because it made practical sense, but
because it felt like the missingpiece.
So I just chose something maximally different from
mathematics, and that was Chinese.
I need to admit that Chinese because it's challenging, right?
(04:05):
It's a completely different system, like everything you know
from, you know, phonetic system.So you don't have an alphabet,
right? Completely out of your comfort
zone. What Melissa didn't realize then
was that she was training herself in something that would
become essential to her future work.
Pattern recognition across completely different systems.
The human brain that can switch between abstract mathematical
(04:28):
proofs and tonal Chinese characters.
That's exactly the kind of brainthat can see patterns in data
that others might miss. After graduation, she became a
high school math teacher, and itwas there, watching her students
struggle with handwritten Chinese characters, that
something clicked. I had a hobby like this is the
(04:49):
time when I discovered programming, so I started from
Action Script and Flash because that was how can I facilitate my
students to learn faster, to learn better, to memorize
better. She wanted to build something
that could recognize these handwritten characters
automatically. A classification system, she
called it. Here's the thing that gives me
chills about this story. Melissa was essentially trying
(05:12):
to solve the same problem that researchers at universities
around the world were tackling with sophisticated machine
learning algorithms. Except she had no idea that's
what she was doing. Of course, at this point you
have no now show in the eye, right?
You never came across machine learning this race.
She was like someone trying to reinvent the wheel, not knowing
(05:36):
that wheels exist. Except in her case, she actually
succeeded. So this is when he all started.
I think that was the my first data science project, my first
data science job that really pushed me into into discovery.
Only later did she discover thatwhat she had built was
essentially K means clustering, A fundamental machine learning
(05:59):
algorithm that had been around for decades.
Of course, like you come up withthe algorithm that is not
perfect and then you discover that actually people are working
already more than 100 years on this problem and there are
improvements to this. So it's like you upgrade.
So that was amazing. She didn't just stumble onto 1
established algorithm, she did it again.
(06:31):
Melissa's second accidental invention happened when she
started wondering about memory and self knowledge.
She wanted to build software that could track her Internet
activity and tell her something about herself that she didn't
already know. Maybe you had this problem like
when you wake up in the morning and you have this empty blank
page right? Like you're asking questions
like wanted to do today, right? Even before the 1st coffee.
(06:54):
What I wanted to achieve today, Who am I?
To solve this, she needed to figure out how to represent both
text and images in a way that a computer could understand and
compare. She needed to group similar
content together to find patterns.
And of course, if you try to do that, you naturally come into
(07:14):
machine learning. This is like the machine
learning because you see text, right?
Text is simple, I mean like relatively simple.
But you also see pictures, right?
So how do you represent pictures?
What she was describing, learning good representations of
data, is what we now call embeddings, and then clustering
that data to find patterns. Well, that's the foundation of
(07:36):
modern AI systems. That's what powers everything
from recommendation algorithms to large language models.
Right now, of course, like afterthose years in machine learning,
I would say like it's all about embeddings, right?
You just need to learn good embeddings.
So you need to have a good encoder model.
But back then she was just a person with curious question
about self knowledge, working with a team of linguists trying
(07:58):
to build something that had never been built before.
We actually spent a lot of time trying to design those features
ourselves, those embeddings, andtrying to come up, you know, to
the first system. She was reinventing neural
networks, embeddings, clusteringalgorithms, the entire
foundation of modern AI because she needed them to answer a
(08:19):
simple question, Who am I? There's something happening in
Melissa's story that goes beyondjust the coincidence of
rediscovering algorithms. She stumbled onto something
(08:41):
fundamental about how discovery works.
Actually, this is a piece of advice that I that I give.
Like if you have a topic, beforeyou start working on a topic,
think about how you would frame it yourself.
Because once you start reading papers, you will converge to
what they actually how they framed this problem.
And it's very difficult to escape from that box once you're
(09:03):
in the. Box This is the paradox of
knowledge. The more we know about how
others have solved a problem, the harder it becomes to see new
solutions. Melissa's accidental discoveries
happened precisely because she didn't know the right way to
think about these problems. But there's something even
(09:25):
deeper here. Melissa's current project, the
one she's working on now, takes this question of accidental
discovery into even more philosophical territory.
I did have a even a Wilder idea.So imagine that you collect all
artifacts that you see, all of pictures, like even sound,
everything that you can collect,even conversation with other
(09:46):
human beings. So this is your input, right as
a human being. And then try to train an LLM on
this input and try to ask the LLM what's my next action.
She wants to train an AI on everything she experiences,
every conversation, every image,every piece of text, and then
see if it can predict what she'll do next.
(10:09):
So I would love to train an alarm on the entire input, and
then I would love to check to what extent I'm random, to what
extent it can predict what I want to do next, like checking
do I have free will? Think about what she's proposing
here. If an AI trained on all of your
experiences can predict your next action, what does that say
(10:30):
about free will? Are we just very sophisticated
algorithms ourselves, following patterns we're not even aware
of? Usually how it's framed is you
have free will if your next action is not fully determined
by your history, like the entireinput that you get.
It's the ultimate version of heroriginal question.
(10:51):
Who am I? Taken to Its logical extreme,
and it connects directly back toher accidental discoveries.
If our thoughts and innovations are just the inevitable result
of our inputs and experiences, then maybe Melissa's accidental
algorithms weren't accidents at all.
Maybe they were the only possible outcome of her
(11:14):
particular combination of mathematical training,
linguistic curiosity, and teaching experience.
Building enterprise grade AI shouldn't be complicated, it
should just work the right way. At writer.com, we don't do
(11:38):
everything. We do one thing.
We build enterprise AI that unites business and IT business
teams. Build your own AI agents, no
code required. IT teams manage just one
platform, not a plethora of point solutions. ryder.com
creates AI tools that are safe, scalable, and smarter every time
you use them. That's why Accenture, Qualcomm,
(12:01):
Vanguard, and hundreds more aren't just doing enterprise AI
the right way, they're doing it the righter way.
Book your demo at ryder.com/demo.
At Ryder, where Melissa now works as a research scientist,
(12:24):
she's discovered something remarkable.
The same conditions that led to her accidental breakthroughs in
China are happening again, but this time it's by design.
Excellent thing about this placeis constraints at the very
beginning. And in writer what we emphasize,
we don't use customer data. So how do I develop a system
(12:45):
without data? And at first you can be angry,
right? I can't, you know, create a
solution and then you think about this of this is an
excellent constraint, right? Most AI companies today are
built on hoarding vast amounts of user data.
Ryder took the opposite approach.
No customer data, period. To most people in the industry,
(13:07):
this would seem like trying to build a car without wheels.
But for someone like Melissa, who had already reinvented
algorithms from scratch twice, this wasn't a limitation.
It was an invitation to innovate.
So actually the first model thatwe created was to generate the
data. So we trained a model to
(13:28):
generate the data and I think that was an amazing idea because
I would never come up with this if not given those constraints.
She's describing something that would become a cornerstone of
modern AI development, syntheticdata generation.
But this was years before it became mainstream, before
everyone was talking about it. Once again, necessity had LED
(13:49):
her to reinvent the cutting edge.
And there's something else happening at Writer that's
different from the rest of the industry.
What's interesting about writer is nobody told me how to do
that. They told me you will find a
way. Just take your time, look
around, you will find a way. This isn't how most tech
companies operate. Usually there are Rd. maps,
(14:12):
established methodologies, best practices copied from other
companies. But Ryder was betting on
something else, that the same curiosity driven approach that
led to Melissa's accidental discoveries could be channelled
into systematic innovation. When I joined had two people or
three people in the NLP team. Right now we have several teams
doing NLP and I remember my first task was training a model
(14:35):
for grammar. It was not obvious what an LM
can actually do the job well. I considered T5A large model
just to give you the scale rightnow.
That model was less than 1 billion parameters.
What she's describing is the difference between research and
engineering. Most companies in 2020 were
focused on implementing existingsolutions at scale.
(14:59):
Reiter was asking deeper questions.
What if we could build enterprise AI systems that
actually understand how businesses communicate?
What if we could make AI that doesn't just follow patterns,
but understands context and intent?
Everyone in the industry is relying on benchmarks, that's
true, but the benchmark is like a cherry picked use case.
(15:22):
There is one piece that is missing in all of that testing
and it's very time consuming because it's going to a human
being, asking the human being, could you please use that model
in production? Do you like actually talking to
the model? She's talking about something
they call the Vibes Test, and itreveals something profound about
how writer approaches AI development.
(15:45):
So we always say that the Vibes test is the most important.
After you satisfy all of those benchmarks, you go and do device
check. While other companies chase
benchmark scores, writers askingdoes this AI actually work for
real people doing real work? It's the difference between
optimizing for test scores and optimizing for human experience.
(16:08):
This approach has led to breakthroughs that go far beyond
what typical enterprise AI can do.
They're not just building chat bots or document generators.
They're building AI that understands the nuances of how
different industries communicate, that can adapt to
companies specific writing styles, that can actually
improve how people think and work with language.
(16:31):
Sky is the limit, right? Even with the current
technology, we can go very, veryfar, so it's only about the
imagination. This isn't just one person's
success story. Writer has created an
environment where this kind of innovative thinking can
flourish, where constraints become catalysts, where the
impossible becomes just another interesting problem to solve.
(17:00):
There's a larger pattern here that goes beyond Melissa's
individual story. Throughout history, some of our
greatest discoveries have come not from experts following
established paths, but from outsiders approaching problems
with fresh eyes, from Kepler discovering planetary motion
while trying to find the music of the spheres to Darwin
(17:21):
developing evolution while studying to be a clergyman.
But what writer has figured out is how to systematically create
the conditions for this kind of breakthrough thinking.
The thing about quantization anddistillation, every model is
different, right? That's why we do this extensive
testing, because we want to knowthe characteristic of the model,
how to design a system on top ofthe model.
(17:44):
What she's describing is deep, fundamental research into how AI
systems actually behave. Not just how they perform on
tests, but how they think, how they fail, how they can be
improved. This is the kind of work that
pushes the entire field forward,and it's paying off.
While other companies are racingto implement the latest trending
(18:06):
model, Writer is building AI systems that work reliably in
the messy reality of enterprise environments.
So if you have a start up right and you rely on API and then
suddenly the API provider changes the model, your entire
business is in pieces, right? Because everything that you
created so far on top of it, it stops existing.
(18:27):
Writers approach understanding their models deeply, controlling
their own infrastructure, optimizing for real world
performance rather than benchmark scores.
This isn't just good engineering, it's the foundation
for the next generation of enterprise AI.
But here's the twist. Now that Melissa knows about
machine learning, now that she'sworking with a team of world
(18:50):
class researchers, can she stillhave those accidental
discoveries? Or has knowledge become a
constraint? Those tokens that happened
before are very important for the current token.
If you change anything in past tokens, you would diverge from
the trajectory that you're not right now.
So there is a possibility that you would not end up getting the
(19:11):
token that you have right now. So I've been thinking about my
token right now and I'm really happy where I am.
She's using the language of AI tokens and trajectories to
describe her own life. And maybe that's the point at
writer she's not just applying her curiosity to solve
individual problems. She's part of a team that's
rethinking what enterprise AI can be.
(19:33):
The accidental algorithms of herpast have become intentional
innovations. And for other researchers and
engineers who are tired of chasing the latest hype cycle,
who want to work on problems that actually matter, who
believe that the most interesting breakthroughs happen
at the intersection of constraints and creativity,
(19:55):
well, Reiter has proven that there's another way to build the
future. Melissa Rusack is a research
scientist at Writer, where her team has grown from two people
to multiple specialized groups, all working on the kinds of
fundamental problems that push the entire field forward.
(20:18):
The next time you find yourself approaching a problem without
knowing the right way to solve it, remember Melissa's story.
Sometimes not knowing is exactlywhat you need to see something
new. And sometimes, if you're lucky,
you might find yourself in a place that rewards that kind of
thinking. Thanks so much to Melissa Rusack
(20:42):
for sharing your story. I'm Laura Weaver and this has
been Humans of AI. Thank you to everyone for tuning
in this week, and a huge thank you to the Humans of AI team
over at Rider. If you enjoyed this conversation
with Melissa, make sure you go check out Humans of AI wherever
you get your podcasts. And don't forget to check out
(21:03):
our own back catalog for our interview with Writer CEO Mei
Habib. Thanks for listening and we'll
see you next week.
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