in Guides, Startups 101

Journey through Jargon: Tech Buzzwords Explained

If you want to break into tech then you’ll need to understand what your future colleagues are talking about. Like any industry, the tech world has its fair share of jargon and buzzwords that are often taken for granted. The goal of this guide is to take you through the core concepts behind popular buzzwords, which should ultimately help you feel more confident as you progress on your journey to a career in tech.

The jargon is grouped into themes, and each piece of jargon may refer to jargon already covered in previous lessons within the guide. You are welcome to jump straight in and view any buzzword that takes your interest, but it will make more sense to follow along in a sequential order.

Finally, this guide is a work in progress so you should expect to see new jargon being added over the coming months.

Let’s get started.


There are competing definitions for the word startup, but within the tech industry a startup is generally considered to be a recently-formed company that intends to grow large, quickly. If we use this definition, a startup may be a tech company, but not all technology companies will be startups by default – they need to be focused on growth.

The term is used pretty loosely in the media and popular culture. It’s not uncommon for large, well-established companies like Uber to be referred to as “startups” even though they are 10+ years old and dominant players in their category.  

“I love the challenge of working at a startup, it’s so exciting.”

“Oh, where do you work?”


[Raised eyebrow]

Founder, co-founder

founder is the person or people (cofounders) who started the company.

It’s quite common to hear tech founders being described as technical founders or non-technical founders. A technical founder is someone who has the skills needed to help build the product – for a software company this will generally mean they can write code. Non-technical founders will generally focus on other areas of the business, like sales, marketing, support and administration. In an ideal scenario a founding team will have a mix of technical and non-technical skills.

“I’m the founder of a new startup that is going to disrupt the bespoke sequin dog-collar industry”

Minimal Viable Product (MVP)

The minimal viable product is a basic version of a software product that is developed to help test the product with real customers/users. The MVP should only contain the features and functionality that are absolutely necessary for testing, and will generally lack the “polish” of a finished app – it should literally have the minimum amount of functionality to be a viable test product for users.

“We’ve had three customers testing our MVP and the feedback so far has been very positive”.

Lean Startup

Lean startup is a methodology for building new businesses. The aim of following the lean startup method is to see whether a proposed business model is viable. The process includes coming up with a test hypothesis (eg: “Users will want to upload and share photos with their friends”), building a minimal viable product (MVP) to test the hypothesis, testing the MVP with users, then using what we learn to make any changes needed before repeating the process until we have a product that resonates with the user/customer. We can break this down into a 3 stage “Build > Measure > Learn” loop.

Hypothesis: “Companies will be willing to pay to have employees communicating in real-time, rather than sending emails back and forward”.
Test: Build a simple text-based chat app as an MVP. Get a sample of real users to test it, and then make adjustments to the app based on their feedback. Test new version with users, get feedback, make adjustments etc.

The term lean startup comes from “The Lean Startup” by Eric Ries, a book that has become a classic within the technology industry.

“We’re following the lean startup method, currently building our MVP and think we should have it ready for initial testing in about 2 weeks”.

Product/Market Fit

A startup is said to have achieved Product/Market Fit when they have created a product that is meeting the needs of customers/users in a large market. As a result, they should be seeing very strong growth for the product (in the early stages this could be 10%+ growth per week).

The famous VC Marc Andresson puts it quite simply:  “Product/market fit means being in a good market with a product that can satisfy that market.”

We often associate the product/market fit concept with the stages of the product funding life cycle. A startup is generally trying to “find” product/market fit during the pre-seed and seed stages (the initial investments they receive when the company is basically just an idea). They then use the money from investors to build a product or prototype and start testing it in the market.

A startup is usually expected to have achieved product/market fit when they go to raise their Series A round of funding. The money from the Series A funding can then be used as additional fuel to help the startup grow and scale as quickly as possible, with the knowledge that strong demand for the product exists.


A startup or tech company is said to “pivot” when they decide to change their core product offering in a fundamental way.

Why would a company pivot? Well, startups are hard, and we don’t always get everything right the first time. It’s very common for founders to have an idea for a product or service but then find that the market isn’t interested in what they have to offer. Rather than continuing to bang our heads against a brick wall, we can pivot and try another idea.

In the tech world we could say that the original idea failed to reach product/market fit, a concept we covered earlier in this guide. It’s very hard to find product/market fit, and most startups won’t get it with their first attempt. That’s where pivoting comes in, as the startup makes changes to their product or business model to try and find a combination that resonates with the market. It may even take more than one pivot to reach product/market fit, and many startups will keep trying until they succeed or run out of funding.

There are hundreds of real-world examples of startups pivoting, but a company I’m sure you’ll recognise is Instagram. Instagram started life as the location-based check-in app Burbn, a popular type of app back in the early 2010s.  The founders tested Burbn with a small set of early users but struggled to get any initial traction. They noticed that while the app itself wasn’t very popular, their test users were using the photo functionality they’d included within the app. They made the decision to pivot, changing the product’s focus from location based check-ins to photo sharing. After making changes to the app they relaunched with the name Instagram. Instagram’s photo sharing was an immediate hit with users, they saw rapid growth and it wasn’t long before they were acquired by Facebook for $1B.

How would we expect to hear “pivot” used in a sentence?

“We’ve been testing our MVP with a small set of initial users but we’re struggling to gain any traction, so we’ve decided to pivot.”

Burn Rates (incl. Gross Burn, Net Burn)

The burn rate is the amount of money a company spends on expenses within a certain time period, usually a month. It’s called the burn rate because we are essentially measuring how quickly the company is burning through their existing capital.

We can split it out into gross burn and net burn. The gross burn rate is the total amount of cash that the company is spending each month. That includes all operating expenses like salaries, rent, server costs and so on. Net burn rate is a little different – it measures how much the company is losing each month, after expenses have been taken from any revenue.

If a startup is pre-revenue (tech jargon for not generating any income), then the difference between gross and net doesn’t really matter. If a startup is bringing in some revenue, even if it  isn’t enough to cover all of the expenses, that will impact the net burn rate and how long they can survive without a new injection of capital, or a boost in revenue.

A new NFT profile pic startup has monthly expenses of $50k, made up of $15k for a cool office in Miami, $30k on salaries and $5k on other expenses. They are “pre-revenue” as they haven’t actually released any NFTs yet, so both their gross and net burn rates will be $50k per month.

Contrast this to a second company, sensible SaaS. They also have monthly expenses of $50k, including $40k spent on salaries and $10k on other expenses. They’re fully remote so they don’t need an office. Sensible SaaS are bringing in $30k per month in revenue so they aren’t losing as much per month. Their gross burn rate will still be $50k (because that’s how much they are spending), but their net burn rate will be $50k of expenses minus $30k of revenue, for a net burn of $20k per month.

Cash Runway

The cash runway, or just runway for short, measures how long the company’s cash is going to last at their current burn rate.

If you’re working at a startup with $1M in the bank and a monthly burn of $150k, then we would calculate runway as $1,000,000 / $150,000 = 6.6 months. Is 6.6 months of runway good or bad? 6 months certainly isn’t long, but how bad it is will depend on some other factors – is the company about to sign terms for a large round of funding? Are they about to onboard some large paying customers that will provide a big bump in their revenue? etc.

“We’ve been working to decrease our burn rate, and with this new round of capital we should have about 18 months of runway ahead of us.”

Total Addressable Market (TAM)

The popular acronym TAM stands for Total Addressable Market, or less commonly, the Total Available Market. In other words, it’s the total revenue available for a product or service if you were able to get 100% share of a particular market.

Startups and tech companies use TAM as a way to estimate the potential size of a particular market. This can help them decide whether they consider an opportunity is large enough for them to move forward. It’s also very popular to refer to TAM during fundraising pitches, where startups will try to convince VCs of the huge potential of their company using the TAM. “The TAM for banking services is hundreds of billions of dollars, so if our crypto savings account for grandmothers can capture just 1% of that market then we’ll be a unicorn”.

Calculating TAM

There are a few ways to try and calculate TAM. One popular method is known as “Top Down”, where we start with the largest estimate possible and then reduce it down using data and assumptions for the specific market. An example would be starting with the total market for all software, then using publicly available research to narrow it down to the smaller market for business to business software, and then again until we have a number for software products targeted specifically to help scheduling at dental practices.

A problem with this top down method is you’re going to be limited by the accuracy of the data you’re using. If the initial market data is old or wrong then your TAM will be as well.

Another way to calculate tam is “Bottom Up”, which takes the opposite approach. You start with a small market subset and then extrapolate from that until you’ve calculated the total population of potential buyers of the product or service. The problem with this approach is that we’re making some strong assumptions of our initial starting point, so if it’s not accurate then neither is the TAM.  

It’s highly unlikely that a company can capture the full total addressable market for a product or service, even if they basically have a monopoly. Consider Google – globally they have around 90% of the search market, but that remaining 10% is still large enough for the likes of Microsoft Bing and DuckDuckGo’s search engines to be worth billions of dollars each.

There are some other potential issues with using TAM, beyond just getting the calculations wrong.

Startups are often trying to build products or services that are cheaper and better than what exists in the market, and this can actually increase the size of the total potential market. What they should be measuring is the size of the problem, rather the size of the current market. A famous example here is Uber. When they launched they were technically taking on the private car hire and limo markets. Industry data at the time valued those markets at approx $4.2B, so many investors passed on Uber since they thought the opportunity was too small. Of course, Uber was going after much more than private car hire – their ridesharing model was incredibly successful and today the worldwide market for Uber and their competitors is valued in the tens of billions of dollars.

Unicorn, Decacorn

A unicorn is the informal name for privately-owned startups with a valuation of one billion dollars ($1B) or higher. It’s commonly attributed to venture capitalist Aileen Lee who popularized it in 2013. As startup valuations have continued to grow the term decacorn has started to be used for even larger “startups” with a valuation of more than ten billion dollars, like Stripe.

“The company raised $150m at a $1.25B valuation, making them the newest members of the unicornclub.


An algorithm is a set of rules that are followed to solve a problem or complete a task. We usually think of algorithms in the context of computing and technology but they exist beyond these worlds – if you follow a recipe to mix a martini then you are using an algorithm.

An algorithm can be broken down into Inputs, the algorithm itself, and Outputs. Using the Martini example, the inputs would be the ingredients that we are using to make the drink – the gin and dry vermouth. The algorithm is the set of rules we’re going to follow to mix the drink, something along the lines of “Measure and pour ingredients into a mixing glass. Stir the ingredients” and so on. Since we know the ingredients and are following an algorithm (our recipe) to make the drink,  we should be able to correctly predict our output – in this case, a dry gin Martini.

Let’s bring things back to technology and computing. A common computing task is to sort a list of data into ascending or descending order. A real world example of this would be taking a list of exam results and sorting them from the highest to lowest grade. This sounds like a straightforward problem, right? There are actually many different sorting algorithms that can be used to sort large lists in efficient ways, with names like bubble sort, simple pancake sort and cocktail shaker sort. Anyway, each sorting algorithm is a variation of taking a list of data as the input, applying a sort algorithm, and then outputting the sorted data as the result. The actual algorithms can be quite complex mathematically so we’re not going to go through them here, but Wikipedia has some good breakdowns if you’re interested in learning more.

We need to keep in mind that while computers are incredibly powerful and capable of processing trillions of calculations per second, they are not intelligent in the same way as humans. They are great at following well laid out instructions, or algorithms, but that’s exactly what they will do – they’ll follow the instructions as they have been programmed. Many things that we would take for granted when talking with a person will need to be explicitly instructed to a computer.

Let’s use the example of the Martini again. The first instruction in our recipe slash algorithm is “Measure and pour ingredients into a mixing glass”. What exactly does measure mean? You’d probably guess (correctly) that we need to carefully measure the amount of liquid for each ingredient we are going to use, and then pour them into a mixing glass. But computers lack the real-world understanding and context to know that. The word “measure” could apply to many things – we could be asking it to find the length of the bottle, or the distance between the bottle and the mixing glass.

This is why it’s so important for programmers to think logically and break everything down into small steps when they are writing code. If they don’t then they are liable to run into bugs and issues when the software executes exactly as it was written, rather than how it was intended. A bad martini isn’t the end of the world, but if you’re working on the machine learning algorithms that power self-driving vehicles then unexpected behavior can have very real, and even fatal, consequences.


FAANG is an acronym that refers to large, high-performing tech companies: Facebook (now Meta), Amazon, Apple, Netflix and Google (now Alphabet).

While it’s still commonly used there are a few issues with this acronym:  Mark Zuckerburg changed Facebook’s company name to Meta so they can focus on the Metaverse, and Google now exists as a company within Alphabet. Also, it’s simply not an accurate representation of the largest or most powerful technology companies. If we were basing the acronym on the current market valuations of the largest tech companies you would expect Netflix and Meta to be replaced with companies like Microsoft and Nvidia.

Generally speaking, if you hear someone talking about FAANG they just mean large, well known tech companies in general rather than the specific companies that make up the acronym.

Big Tech

The name Big Tech usually refers to the group of large tech companies that are seen to hold powerful or politically sensitive positions in the market, like Apple, Alphabet (Google), Amazon, Meta or Microsoft.

You’ll commonly see “Big Tech” used in the media in relation to concepts like monopoly power, antitrust, and “breaking up big tech”, where the market valuation of the company is less important than their perceived impact on society. As an example, at the time of writing Tesla has a larger market capitalization than Meta/Facebook, but Facebook is often in the news with stories about how political groups are using them to subvert democracy, so Facebook is more likely than Tesla to be considered “Big Tech”.

Web 1 & 2

In 1990 an English scientist called Tim Berners-Lee created a hypertext document system called the World Wide Web (WWW). The web allowed users to navigate between documents, or websites, by clicking on hyperlinks. This is all very familiar to us now but it was a huge deal at the time. The original websites were read-only – you could view information but you couldn’t create, edit or interact with it in any way. This early period is now known as Web 1.0.

In the early 2000s we moved to Web 2.0 when new technologies allowed websites to become interactive, taking us from read-only to read-write. This led to an explosion in growth across social and user generated content (UGC) products, from Myspace to the likes of Facebook, Youtube, Instagram and TikTok.

Web 3, Blockchains, Cryptocurrencies, DefFi

Web3 is the name for the “next generation” or iteration of the internet, following on from Web 2.0. It’s commonly associated with concepts like blockchain technology, decentralization and tokeneconomics.

A blockchain is a type of distributed, immutable ledger. We can think of it as a way of storing records of data in a way that prevents the record from being changed after it has been created (so that would be immutable in computing terms).

Copies of the records are stored decentralized, or shared across a huge number of computer systems to help prove their accuracy.

If someone managed to change a record on one system, they would only be changing their local copy. The other distributed versions of the record will still show the original details.

Blockchains are most commonly used in cryptocurrencies like Bitcoin or Etherum.

Decentralized finance (DeFi) that aims to remove intermediaries and brokers from the financial markets, and Non-Fungible Tokens (NFT), non-interchangeable units of data that are stored on a blockchain and can be traded, like the Bored Ape Yacht Club, where people pay are paing $330k for a cartoon image of an ape.

Network Effects, The Cold Start Problem

A network effect is the name for the phenomenon where the value that a person gets out of a product/service is based on the number of people who are using that product. Most of the time people will be referring to positive network effects, where adding people to the product improves the product. As the product continues to improve it becomes more attractive and draws in additional users, which in turn makes it more useful, which brings in more users, and so on. Over time, this positive feedback loop can lead to exponential growth and market dominance.

Social media platforms are great examples of companies with strong network effects. When a news user joins Instagram and starts posting photos and leaving comments, it makes the platform more useful for all the other users. They have new content to view, friends to follow and so on. So more people decide to join Instagram, and they increase its value and utility even further and the cycle repeats.

It’s very hard for startups to compete against incumbents with strong network effects as the size of their network is a huge asset by itself.

The Cold Start Problem

If network effects are so strong then why aren’t all network businesses super successful? They suffer from what Andrew Chen, who used to lead rider growth at Uber, calls the “cold start problem” (he’s also written a very good book that goes into network effects in a lot of detail).

The value of a network with positive network effects increases with every additional user. But by definition this also means at the beginning, when you don’t have many users, the network will not be valuable! Imagine if Uber only had a handful of cars to cover San Francisco. People who open the app will become frustrated when they can’t get a ride in a decent amount of time, so they’ll look for an alternative, which could be another ride sharing app, a taxi, or public transport. The network wouldn’t be offering enough value to keep its early users coming back so it will struggle to gain traction, and won’t benefit from the positive feedback loop that leads to growth.

Solving the cold-start problem is a top priority for the product and growth teams at network businesses. How they do that is quite complex and beyond the scope of this topic, but if you’re interested then you should check out Andrew Chen’s book.

Finishing up

This guide to tech jargon and buzzwords is a work in progress. You can expect to see new jargon being added over the coming months.

Write a Comment