Why Tech is Targeting the $15 Billion Mattress Market
On the surface, the sleep industry appears to be a relatively undesirable space for a startup.
Beds and mattresses are heavy and bulky, and sales are traditionally based on a tactile experience that consumers have with products in physical stores. Holding inventory is expensive and risky, and shipping is a nightmare.
Sure, people are willing to shop online for almost everything these days – but when up to 40% of life is spent lying on a bed, isn’t that a product that should be tested out before a purchase decision is made?
Despite the conventional wisdom to the contrary, the $15 billion mattress industry has seen the entrance of several ambitious startup companies, and they are starting to put a dent in market share.
Today’s infographic from Online Mattress Review tells the story of how disruption is occurring in this unlikely space – and it all starts with big changes to the business model to make online mattress sales more palatable for both the company and the consumers.
An Updated Model
Here are a few key ways online mattress companies, like Casper or Purple, have changed up their value proposition to customers to make life easier for themselves:
By offering a money-back guarantee of up to 100 days, online mattress companies give customers plenty of time to test their product. This reduces the chance of buyer’s remorse.
Going all-in on memory foam
Memory foam, as well as other mattress types that can be compressed down in size, allow for fast and easy shipping. Consumers can take a box the size of a filing cabinet and easily navigate it around corners and doorframes in a household setting.
Fun, relationship-based marketing
To appeal to the millennial market, Casper has taken on some quirky initiatives, such as creating Insomniabot-3000 (a chatbot for people who can’t sleep), and a Labor Day Mattress “Sail” boat cruise.
In 2016, the market share for online mattress sales was 5%, and it’s expected that the number for 2017 could be at least double that.
While tech startups and the sleep industry may seem like strange bedfellows at first, it’s clear that consumers are embracing the chance to get in bed with the idea.
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.
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?
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.
|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.
|Perceptron Mark I||1957–58||695,000|
|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.
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