Technology

How to Take the First Steps in Scaling Your Business

Published

5 years ago

December 18, 2018

Jeff Desjardins

How to Take the First Steps in Scaling Your Business

Most entrepreneurs are hungry to bring their company to the next level.

Whether they operate a family-run business or a rapidly evolving tech startup, there is always another milestone in sight. Business owners want to their companies to make an impact with their customers and communities, and they want to keep honing their craft.

But with 27.9 million small businesses in the United States alone, there is no shortage of competition for the same pieces of the pie.

How can you take steps in scaling your business, and do what your competitors are not willing to do?

Roadblocks to Scale

Today’s infographic comes to us from Brunner Consulting, and it breaks down common roadblocks to scaling as well as potential solutions to the problem of decision fatigue.

To begin, we’ll look at a poll of U.S. small business owners, which gives perspective on the challenges most often faced by companies with fewer than 10 employees:

Profitability (50%)
Hiring new employees (48%)
Growing revenue (41%)
Cash flow (38%)

Unless a business has deep pocketbooks or is venture-backed, there are several obstacles here that may prevent companies from scaling successfully.

A lack of profitability is an obvious limitation, but it’s also clear that revenue growth, cash flow, and adding new employees can be growing pains that may derail any long-term plans.

Decision Fatigue

Why is scaling your business so challenging?

It’s because most types of businesses are not really scalable to begin with. The only sustainable way to scale for most companies is to grow revenue while decreasing operating costs, and for many traditional small businesses (i.e. bakeries, restaurants, hardware stores, consulting, etc.) this can be incredibly difficult.

Even if you come up with a scalable business model, there is yet another obstacle that can prevent your from growing the right way: decision fatigue.

In a growing and evolving company, entrepreneurs can’t do everything – and when they try to make every big and small decision, it affects the quality of those decisions. It can lead to being unnecessarily risk averse, maintaining the status quo, or even avoiding decisions altogether.

Scaling Your Business: First Steps

For a business to grow, there has to be more than one decision-maker.

There are two main routes to this:

1. Delegate Responsibility
In a typical small business, employees find and diagnose problems, while owners focus on solving them. However, by delegating these day-to-day decisions to employees, it frees up owners to work on the big picture items that can fuel growth.

2. Play to Your Strengths
Entrepreneurs can’t do it all, so it’s best to play to your strengths. To do this, outsource business departments that are outside of your wheelhouse. Often those may include things like bookkeeping, marketing, customer service, or website design.

Decentralizing decision-making is one of the first steps in scaling your business – and no matter how you do this, it frees you to focus on the big problems.

Related Topics:scale cash flow hiring scaling your business startups entrepreneurship

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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

Published

4 days ago

September 18, 2023

Pallavi Rao

A cropped version of the time series chart showing the creation of machine learning systems on the x-axis and the amount of AI computation they used on the y-axis measured in FLOPs.

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.

ℹ️ FLOPs are often used as a metric to measure the computational performance of computer hardware. The higher the FLOP count, the higher computation, the more powerful the system.

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.