Technology
34 Startup Metrics for Tech Entrepreneurs
Courtesy of: Funders and Founders
34 Startup Metrics That Tech Entrepreneurs Need to Know
Today’s infographic comes from Funders and Founders and information designer Anna Vital, and it lists the important metrics to gauge traction and success of new startups.
Several years ago, a key challenge with launching a new tech startup venture was that there weren’t many precedents to follow.
- How do you scale a company?
- How do you measure growth and costs in a more meaningful way?
- Does the company have real traction?
Of course, there were knowledgeable people in the tech ecosystem that knew these things – for example, venture capitalists and ex-founders that had been successful with previous ventures – but they were tough to gain access to, and many of their theories and best practices weren’t yet widespread.
Fast forward to today, and the practices around new startups are much more established. While this can be a blessing and a curse to new founders, at least a more standardized set of metrics helps to give founders a sense of where their company stands.
Key Startup Metrics, According to VCs
The infographic from Funders and Founders lists 34 startup metrics for founders to know – but which one should be a focus for new ventures?
Here’s what three bigtime VCs have said about the startup metrics that they consider to be most important at early stages:
“Month-over-Month Organic Growth”
For most companies, MoM organic growth is a very useful metric and depending on the base, 20–50% MoM growth can be good. Retention, referral, and churn are all things we look at, too.
– Aileen Lee, Cowboy Ventures
According to Aileen Lee, who originally came up with the “unicorn” term, organic growth is a particularly useful metric.
On the other hand, Bill Gurley of Benchmark says that monitoring conversions is a comprehensive metric that is a good proxy for several key business areas.
“Conversion Rate”
No other metric so holistically captures as many critical aspects of a web site – user design, usability, performance, convenience, ad effectiveness, net promoter score, customer satisfaction – all in a single measurement.
– Bill Gurley, Benchmark
Paul Graham, of Y Combinator fame, says that the metric depends on the stage. If you have revenue, then revenue growth is the metric you want. If you’re not there yet, user growth is a good proxy.
“Revenue Growth or Active Users”
The best thing to measure the growth rate of is revenue. The next best, for startups that aren’t charging initially, is active users. That’s a reasonable proxy for revenue growth because whenever the startup does start trying to make money, their revenues will probably be a constant multiple of active users.
– Paul Graham, VC and co-founder of Y Combinator
It should also be noted that the most relevant metric to you depends on your business model. For example, MRR (Monthly recurring revenue) and churn rates would be particularly important to SaaS (Software-as-a-service) startups, while MAUs (Monthly active users) and organic traffic may be more important measurements for online media companies.
Technology
Charted: The Exponential Growth in AI Computation
In eight decades, artificial intelligence has moved from purview of science fiction to reality. Here’s a quick history of AI computation.
Creator Program
Charted: The Exponential Growth in AI Computation
Electronic computers had barely been around for a decade in the 1940s, before experiments with AI began. Now we have AI models that can write poetry and generate images from textual prompts. But what’s led to such exponential growth in such a short time?
This chart from Our World in Data tracks the history of AI through the amount of computation power used to train an AI model, using data from Epoch AI.
The Three Eras of AI Computation
In the 1950s, American mathematician Claude Shannon trained a robotic mouse called Theseus to navigate a maze and remember its course—the first apparent artificial learning of any kind.
Theseus was built on 40 floating point operations (FLOPs), a unit of measurement used to count the number of basic arithmetic operations (addition, subtraction, multiplication, or division) that a computer or processor can perform in one second.
Computation power, availability of training data, and algorithms are the three main ingredients to AI progress. And for the first few decades of AI advances, compute, which is the computational power needed to train an AI model, grew according to Moore’s Law.
| Period | Era | Compute Doubling |
|---|---|---|
| 1950–2010 | Pre-Deep Learning | 18–24 months |
| 2010–2016 | Deep Learning | 5–7 months |
| 2016–2022 | Large-scale models | 11 months |
Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.
However, at the start of the Deep Learning Era, heralded by AlexNet (an image recognition AI) in 2012, that doubling timeframe shortened considerably to six months, as researchers invested more in computation and processors.
With the emergence of AlphaGo in 2015—a computer program that beat a human professional Go player—researchers have identified a third era: that of the large-scale AI models whose computation needs dwarf all previous AI systems.
Predicting AI Computation Progress
Looking back at the only the last decade itself, compute has grown so tremendously it’s difficult to comprehend.
For example, the compute used to train Minerva, an AI which can solve complex math problems, is nearly 6 million times that which was used to train AlexNet 10 years ago.
Here’s a list of important AI models through history and the amount of compute used to train them.
| AI | Year | FLOPs |
|---|---|---|
| Theseus | 1950 | 40 |
| Perceptron Mark I | 1957–58 | 695,000 |
| Neocognitron | 1980 | 228 million |
| NetTalk | 1987 | 81 billion |
| TD-Gammon | 1992 | 18 trillion |
| NPLM | 2003 | 1.1 petaFLOPs |
| AlexNet | 2012 | 470 petaFLOPs |
| AlphaGo | 2016 | 1.9 million petaFLOPs |
| GPT-3 | 2020 | 314 million petaFLOPs |
| Minerva | 2022 | 2.7 billion petaFLOPs |
Note: One petaFLOP = one quadrillion FLOPs. Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.
The result of this growth in computation, along with the availability of massive data sets and better algorithms, has yielded a lot of AI progress in seemingly very little time. Now AI doesn’t just match, but also beats human performance in many areas.
It’s difficult to say if the same pace of computation growth will be maintained. Large-scale models require increasingly more compute power to train, and if computation doesn’t continue to ramp up it could slow down progress. Exhausting all the data currently available for training AI models could also impede the development and implementation of new models.
However with all the funding poured into AI recently, perhaps more breakthroughs are around the corner—like matching the computation power of the human brain.
Where Does This Data Come From?
Source: “Compute Trends Across Three Eras of Machine Learning” by Sevilla et. al, 2022.
Note: The time estimated to for computation to double can vary depending on different research attempts, including Amodei and Hernandez (2018) and Lyzhov (2021). This article is based on our source’s findings. Please see their full paper for further details. Furthermore, the authors are cognizant of the framing concerns with deeming an AI model “regular-sized” or “large-sized” and said further research is needed in the area.
Methodology: The authors of the paper used two methods to determine the amount of compute used to train AI Models: counting the number of operations and tracking GPU time. Both approaches have drawbacks, namely: a lack of transparency with training processes and severe complexity as ML models grow.
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