A talk about how we scaled our engineering team at Gorgias, cognitive load theory for organizing teams, when to split a team, what to look for in great engineering managers and a few other topics.

Ten years ago, on a Friday night, in a small studio at the edge of Paris, I wrote the first lines of code that started the Gorgias adventure.

Why was I coding a customer service solution? I volunteered to help with customer service at Gymglish to understand our customers and support process better. I still believe that doing customer service makes you at least a better engineer. Everyone should do it. Antoine and Ben (our first investors) encouraged me to improve things, and I had tried a few helpdesks, but they were expensive and complicated for Gymglish.

Enter Gorgias. The idea was simple: Write faster on the internet with templates.

Ionut helped me with initial iterations. Soon after, I met with our now-CEO, Romain, and the small project became a bigger idea, then a startup with customers, and eventually, a fast-growing company.

In late 2015, we got accepted to accelerators in Numa, in Paris, then joined Techstars NYC, where Alex Iskold was our sponsor and investor.

Soon after, we raised our seed round from CRV, with Murat Bicer leading the round. Next, we moved to San Francisco, where we managed to find product-market fit over the next 18 months with Louis, Martin, and Jean-Elie.

Once we got to around $10,000 MRR, Aasif joined, and Jason Lemkin from SaaStr provided additional funding when we most needed it. Jason has guided us since then, investing in all the following rounds!

Today, we are proud to serve around 12,000 ecommerce merchants, improving the customer experience for hundreds of millions of shoppers. I’m extremely fortunate to work side-by-side with 250+ talented and hard-working Gorgians to accomplish these amazing things.

Looking back, it’s clear that this was a series of winning lottery tickets that cannot be replicated. Gorgias exists because many people (including many I didn’t mention) gave Romain and me a chance.

This is how startups are made. Day by day, year by year, with lots of luck, leaps of faith, and hard work from many people.

I’m writing this for those who took the first step and entered the entrepreneurial arena. I tip my hat to you, my brothers and sisters! Expect the road to be long and arduous, but nothing worth doing is ever easy.

All big things have small beginnings

…and we’re just getting started.

Big thanks Ramon Berrios & Blaine Bolus for inviting me to talk about Gorgias’s history, it’s mission and what is coming next.

If you’re like me (first time manager in a fast growing software company) you’re likely facing a vast number of organizational issues that you never faced before and they are coming at you faster than you can learn how to deal with them.

Thankfully there are smarter, more experienced people out there that figured out a lot of topics that you can copy, claim credit and become the leader that you always dreamed out to be and your company desperately needs!

My list of books I wish I knew about before scaling Gorgias

If it’s not immediately obvious, the book selection and the order below is opinionated. I’m starting with the leadership and culture books first, then going into management fundamentals, engineering manager career orientation, hiring engineers, operational best practices, writing strategy and finally scaling teams and productivity.

First fundamentals, then tactics.

Turn The Ship Around!

My #1 recommended professional book. Manager or not, if you want to figure out what is a great leader I recommended reading it!
My personal take on the whole leader-follower dichotomy is that it’s abused in our industry. Being a follower: bad! Being a leader: good!
Great leaders figured out when to follow and empower people in your team and when to lead. The art of moving between these modes is what separates noobs from experienced and effective leaders. This is how trust is built and how you encourage growth, ownership and motivation in people.
IMO the best part about this book is that’s it’s not boring! If you read enough management and self-help books you know what I’m talking about.

No Rules Rules

It’s about the (in)famous Netflix culture. The core idea is that an exceptional company increases talent density and establishes a culture of freedom and responsibility.
Allow me to put it less diplomatically in steps:

• First you aggressively fire anyone that is not a “top performer”.
• Then you pay top of the market salaries to those who are left and recruit the “best” people.
• Remove redundant bureaucracy because you hired the “best” and they hate stupid rules that don’t bring any value.
• Make sure that they know they own their shit, they are expected to take risks and will get fired if they don’t get results on time.

The book is full of anecdotes of the CEO and interviews from the various employees that make the case for the above.
My recommendation is to take the ideas here with a giant grain of salt, it’s likely not something that applies to your seed-stage B2B SaaS startup where cash is in limited supply and you have commission based roles.

Why do I recommend this book then?
Because I think it helps to think about how you should treat product, engineering and design roles. Why it’s worth paying the top dollar to get the best possible engineers. Why your designers should not have to jump through ridiculous hoops to get their best work done and why your product people should be empowered to take risky but calculated decisions.
I think it applies to marketing and other “scalable” roles, but that’s not my place to comment.

The Manager’s Path

This is a great “operating” manual for any engineering manager. Starts with coaching and managing ICs, managing teams, teams of teams and goes all the way to answering the question of what the hell is the difference between VPE and CTO? If you’re looking for orientation about various roles and growth paths in the engineering management I couldn’t think of a better book. It’s easy to pick up from any point in the book. You’ll also find a career path/ladder section. It’s a great reference that I always have on my desk.

Smart and Gets Things Done

One could argue that it’s somewhat dated now, but I think it’s still great if you want to follow a simple rule when hiring:

Is the candidate smart and can they get things done?

Today I would add cultural fit: it’s important because you cannot change someone’s values or personality. The point the book is making is that you should not hire academics that are smart, but never get anything finished. Nor should you hire people who work a lot, but make dubious decisions and constant mistakes.

This book forced me to answer the same two questions after every interview: Are they smart? Can they get things done?
Not sure? Not a good idea to hire.

Accelerate

Data-driven approach to best practices in engineering. Can you measure good engineering? The main point the book is trying to make is that you can avoid sacrificing quality and focusing on the right things you can increase velocity of your organization. What are those metrics?
Introducing DORA metrics - read more here.

• Deployment frequency: How often a software team pushes changes to production
• Change lead time: The time it takes to get committed code to run in production
• Change failure rate: The share of incidents, rollbacks, and failures out of all deployments
• Time to restore service: The time it takes to restore service in production after an incident

Should you use these metrics and draw immediate conclusion if you team is amazing or sucks? Metrics without the context are a huge danger, but can trigger valuable investigations and gaining more understanding.
I recommend supplementing this book with Swarmia. I have no affiliation with them. Just a fan.

Team Topologies

For later stage scaling teams and how to think about it. The core concepts of the book are cognitive load, Conway’s law and the “Inverse Conway Maneuver” which translates to: if you need a system with X components then you should have X teams.
From Jacob Kaplan-Moss’s blog which goes in more detail:
The main thesis of the book is to engage in “team-first thinking”:
We consider the team to be the smallest entity of delivery within the organization. Therefore, an organization should never assign work to individuals; only to teams. In all aspects of software design, delivery, and operation, we start with the team.

It covers four common patterns for teams:

• Stream-aligned teams, that are aligned to a single delivery stream, such as a product or service (what others might call a “product team” or a “feature team”).
• Enabling teams, specialists in a particular domain that guide stream-aligned teams
• Complicated-subsystem teams that maintain a particularly complex subsystem, such as an ML model
• Platform teams that provide internal services like deployment platforms or data services

Again, you can read more here.

An Elegant Puzzle

This one is last because I think it’s better for bigger orgs. It starts with organizations, how to size teams, the types of malfunctions and how to fix them.
Then it gets into processes and various rituals that are common in scaling orgs.
Finally gets into some work principles and culture ending in hiring and career growth.
I couldn’t pinpoint a single thing about why I like this book. It addresses a lot of issues that I’m having right now at Gorgias.

That’s it for now! In the future I will update the above list by adding or removing. I’ll try to keep the list to less than 10.

Word of advice:

Supplement your book reading by meeting leaders in your space, getting an executive coach, listening carefully to your team and customers and mentoring people. There are many ways to learn, books, podcasts and blogs posts is just one way. Arguably not the best way.

The neocortex: the organ that occupies ~80% volume of the brain while consuming ~20 Watts. In his book, A Thousand Brains: A New Theory of Intelligence by Jeff Hawkins the author tries to illuminate how the cognitive sausage is made by looking at a lot of neuroscience clues and trying to piece them together into a new theory of intelligence.

I’ll try to summarize and make sense of his theory here, the way I understood it, and hope it creates excitement for others who are thinking of reading the book. I highly recommend reading the book yourself and I must warn you that I’m not a neuroscientist or AI person. I’m a software engineer that has an interest in these topics.

Before I start, please note that the book is split into two sections - I’ll focus on the first section because that’s where my main interests lie. The second section of the book is diving into the future of humanity, AI safety, space exploration, and many more interesting topics. I enjoyed all these, but I admit, I wanted more juicy neocortex meat.

I’ve been a fan of Jeff Hawkins since ~2009 when I read his first book, On Intelligence - his HTM theory and what it implied was inspiring to say the least. It was the first attempt that I heard of that tried to create a theory of how the neocortex works. The second book expands on the HTM and attempts to complete it. Armed with a decade+ of AI and neuroscience knowledge at Numenta he develops a new theory of the mind called A thousand brains.

The simplest and shortest way I can explain it is this:

The neocortex holds thousands of models of “objects” made of sensory input and reference frames. These models are learned through sensing and moving. The inference is done by “voting” between concurrent models.

Note that these are all high-level concepts that are useful to explain how it’s all supposed to work. There are no “objects” or “reference frames” in the brain - there are synapses, dendrites, cortical columns, minicolumns, grid and place cells (or at least growing evidence that they exist), axons, etc… but it’s hard to explain how intelligence works by using the “hardware” components alone. It’s like trying to explain how a combustion engine works using quantum mechanics - it’s possible, but it’s not the right level of abstraction. The first part of the book is defining this abstraction and connecting it with the different “hardware” parts of the brain.

Let’s dive into what each of these concepts means:

Models of objects

An object can be an apple, a person, a math equation, language, democracy, love, etc… These “objects” are all the same to the neocortex. The same means that the same building blocks or algorithm (inside cortical columns) is used to represent them. This is remarkable because we don’t normally think of grouping abstract things such as calculus with an apple. Remarkable as it may be, there’s a lot of evidence supporting this claim (more below).

The brain maintains models of these objects. Not one model per object, but thousands of concurrent models per object. These models are physically distributed across multiple regions of the neocortex into cortical columns. A single cortical column can concurrently hold thousands of models. In the human brain, there are an estimated ~150k cortical columns.

A useful analogy is to think of a sum of models of an object (let’s call it object-model) as a big jigsaw puzzle with 1000 pieces. Individual puzzle pieces are located in separate cortical columns distributed across the neocortex and the sum of them makes the object-model representing “math” for example.

These individual puzzle pieces are confusingly called models as well, hence the object-model term I introduced above. Let’s call this partial model: a piece-model.

The brain creates a view of the world using millions of these object-models distributed across ~150k cortical columns. Visualize millions of jigsaw puzzles randomly stacked one on top of each other in ~150k columns with a huge amount of connections between them: as far as I understand it, this is what the theory of a thousand brains looks like. It’s a mess, but if you look at the neocortex you start to think it’s not that bad.

Note that it doesn’t mean that an object-model has one piece-model in each of the 150k columns. It depends on how complex the object in question is.

Reference frames

Now let’s take a single jigsaw puzzle piece or piece-model from a single cortical column and see how it’s supposed to work. A piece-model is composed of 2 parts:

• Sensory input or features (Ex: color, temperature, sound).
• Reference frames or refs in short.

These refs are like relative cartesian coordinates of different sensory inputs. Let’s bag this concept for now and let’s dive into an example:

Imagine you have an apple in your hand, what does your brain get as input?

• There are your fingertips that have spatial coordinates relative to the rest of your body, the pressure relative to “less pressure”, the temperature difference.
• The weight of the apple felt by your nerves in your arm and shoulder.
• Your visual system observes the color difference, the corners of the apple and the different distances from your body and compared to other objects in your visual field.

The difference of pressure, temperature and distance between your eyes and the apple: these are all refs.

The example above is pretty static and while we can imagine a model of an apple from a 100ms experience of holding it, this is not how the brain builds these refs and sensory input. Instead, the brain learns both features of objects and their locations over time.

A single cortical column learns complete models of objects by integrating features and locations over time.

The brain learns these refs and features by moving the object around and observing over time. Movement appears to be a key factor of learning models.

The what (sensory input) and the where (reference frames) of each model are tightly coupled inside a cortical column. The refs are “implemented” by place and grid cells together with other parts of the cortical column. The evidence that these grid/place or “location” cells exist in the neocortex is not confirmed yet, but there is growing evidence.

One last detail I want to add about movement + perception is that the “movement” neurons that send the movement commands to the old brain (neocortex doesn’t have direct access to muscles) are also part of cortical columns and are mixed with sensory and location neurons. They are distributed everywhere around the neocortex.

Inference

The inference is done by voting between these models.

Inference, model convergence? Deciding if a chicken is a chicken. Whatever you want to call it.

Once the cortex constructs these distributed models what happens if it tries to identify an object?

Voting!

There are long-distance connections between columns, inside the same cortical region (Ex: visual) and between regions (Ex: visual and touch). I think this image of the Rubin vase here can give you a clue about what happens. Your brain tries to decide between 2 different equally valid models. What’s curious is that you can see either a vase or 2 faces, but not both at once.

More evidence to justify the theory

Again, read the book because it’s super interesting! I omitted important things about the brain and neocortex, but here are a couple of things worth mentioning:

• The neocortex looks the same no matter which region you look at, meaning that cortical columns and their structure are similar across regions. It appears Vernon Mountcastle was first to make this observation and if that’s not OG enough he proposed that cortical columns have a common neocortical algorithm.
• The neocortex looks the same, but not for all things. Language regions appear to be concentrated in certain areas and their connectivity is greater than other parts. Density may be higher, that fundamentals look the same.
• The evolution of brains looks additive - more complex organisms have bigger and more complex brains. More of the same is the successful evolutionary strategy.
• The newer the brain the “less specialized” it looks and the “more uniform” it appears and the “more of it” there is. Recall that the neocortex occupies ~80% by volume. That’s a lot of brain and it’s not cheap to run! To think that I’m using it to watch memes on the internet…

What’s next? More questions than answers!

• It appears that the “motivation” or “goals” of the brain is not set in the neocortex, but in the “old” brain and there is a constant “battle” between the two. The “old” brain wants to eat the marshmallow, but the new one has a model of you on a diet. Is this accurate? If yes, then what is the mechanism of this interaction?
• Is it confirmed that grid and place cells exist in the neocortex? It’s a key part of the theory.
• Recursion (around language and other nested concepts) was mentioned, but I’m not sure I understood how it works.
• How do location cells work with perception cells together?
• How does the voting happen technically? I get there are connections between columns, but where does the “winning” of the vote happen?
• How does prediction happen? I think I understood the basics of primed & inhibitory neurons vs un-primed neurons, but how that leads to prediction is unclear.

Closing thoughts

I’m excited about what I learned and eager to follow the reading recommendations at the end of the book. I realize that I may have misinterpreted parts of the book and there are a lot of details - I hope to correct and improve my knowledge as I discover more about the subject. There is a non-zero chance that Jeff’s theory is wrong, but his theory can be tested which is exciting!

Huge respect for having the audacity to attack this fundamentally hard problem. I’d like to express my admiration for Jeff’s persistence, he started this journey the year I was born, in 1986 and he’s still at it! Looking forward to book #3.

Finally I want to leave here a video of Jeff explaining much better what I described in this post.