Marc Andreessen famously said that “Software is eating the world” and everyone gushed into the room. As much as it was writing on the wall for traditional enterprises; it was great news for the software industry.
Still no one actually understood what he meant.
To make his point he stated this example:
Interestingly, Andreessen also said the following:
I, along with others, have been arguing the other side of the case…We believe that many of the prominent new Internet companies are building real, high-growth, high-margin, highly defensible businesses.Marc Andreessen
(Read the full blog article at his a2z VC fund)
Little did Andreessen envision that the same software industry could be at risk of being eaten.
Fast forward to 2019 and the very same software industry is nervous. Very very nervous!
And the reason is AI.
Especially for those who haven’t bulked up their AI warchest.
Acceleration Wave (2009 – 2019) – When Software Started Eating the World
Andreessen was right.
The companies that embraced software in 2011 are the current market leaders in their respective fields, and the top 5 market capitalization companies worldwide in the second quarter of 2019 are all offering some type of software solutions (ycharts.com).
Concurrently, the period since 2011 has shown an unprecedented growth in the developments in AI. Although several key ideas about AI have been around for long, a number of processes have accelerated their potential use.
First, computing power, in particular for specialized AI chipsets, has vastly increased.
HyperAcceleration Wave (2019 – 2030) – AI Has Started Eating Software
Driven by an increase in efficiency, these new companies use AI to automate and optimize the very core processes of their business. As an example, no less than 148 start-ups are aiming to automate the very costly process of drug development in the pharmaceutical industry according to a recent update on BenchSci.
Likewise, AI start-ups in the transportation sector create value by optimizing shipments, thus vastly reducing the amount of empty or idle transports.
Let’s quickly look at a few example applications of this hyperacceleration wave:
Automating the coding process
by having TabNine autocomplete your code with AI!
It is trained on around 2 million files from code repository GitHub. During training, its goal is to predict each token given the tokens that come before it. To achieve this goal, it learns complex behaviors, such as type inference in dynamically typed languages.
Once Deep TabNine developers realized the parallel between code and natural language processing, they implemented the existing GPT-2 tool which uses the Transformer network architecture.
Getting answers to any question about your medical data
As AI will create the query to get the answer for you!
Here, a group of medical researchers created a tool that you can ask literally any questions on medical data and the AI generates a customized SQL query that is then used to retrieve the relevant data from the database.
It’s called Question-to-SQL generation.
They used RNN (a form of deep learning, an AI on steroids for text analytics) with Attention and Point-Generator Network. For those more inclined to exploring the technical part of this feel free to read their research here and software code here.
So is it time the armies of database administrators (DBAs) to go home?
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These are just a few examples of how AI is increasingly encroaching all parts of software development and eliminating mundane tasks of coding and programming rapidly!
This is due to the motivation to automate the process of numerical analysis, data collection and eventually, processing and relevant code production.
Researchers have higher-than-ever awareness and knowledge to infiltrate each and every problem at all levels with AI-powered software, from day-to-day anecdotes such as: Which kind of cookies shall we recommend to a customer given their shopping preferences?
To large-scale, manufacturer’s dilemma, for example:
How do we automate the production line in an individualized yet systematic manner?
And finally, to the processing of building smarter, easier-to-use software that may even write code for you.
What would you do if you were BMW today?
At this point, no one can reliably predict how quickly electromobility will progress, or which drive train will prevail… There is no customer requests for self driving BEVs. (electric vehicles)CEO, BMW
A classic trap most big enterprises with established business fall for is getting micro-focused on existing business segments while losing sight on the slowly eroding economic and business climate.
Tesla’s story as an electric car is known to all but many may not know that it is the self-driving feature and the heavy use of AI in both software and hardware where the secret sauce lies.
They have already driven 10 billion electric miles and the cars are collecting all the more data to disrupt not just the automotive markets but its adjacent markets in manufacturing, servicing, sales and in general mobility.
Tesla’s AI is eating all other automotive industry’s business.
A few weeks later after his annual address, the BMW chief had resigned.
CEO’s and executives who however do wish to proactively adopt AI should do the following 5 things.
Step 1: Do you have your AIPlaybook Ready?
Last year I did a keynote panel together with a few industry peers and I was asked if AI could eat software and I said “Yes”.
Take a listen.
Any company that is not in possession of its AI Playbook, that is not armed with data, algorithms and machine learning models, is certainly going to find itself in serious quandary.
An example of an AI playbook is to assess your firm’s maturity thoroughly and plan for ROI driven projects.
Step 2: Upskill and/or hire a robust AI team
Upskilling your staff to be able to drive your AI transformation is the key to success for any organization aspiring to become an AI company.
We’ve advised several large-scale data-intensive projects and here are a couple of key arguments that executives should take to heart.
- In a couple of years embracing AI is not a matter of trend riding, but survival;
- To survive an era in which AI is dominating both market and software, CEOs and executives need to level up their mindset for successful adoption and application of AI within their enterprise, for which they either have to upskill or find a good data science team;
- Know your game: A good team helps you understand how AI will make your company survive;
- Examples are abundant in the industry and it is key for companies to pay attention to latest trends and launch several smaller projects to extract out the key projects that can be industrialized at scale.
Step 3: Develop Algorithms and Execute your Data-Play from Day 1
Upgrading your technical infrastructure that can develop the latest AI algorithms, process large quantities of heterogenous datasets, build and train both industry benchmarked and novel AI models is an important first step.
Step 4: Implement a distributed knowledge strcture
As access to the right data is a key to valuable AI solutions, ensuring access to data generated or acquired within the company and outside will be of crucial importance. Following this realization, pharmaceutical companies are starting to create central repositories of the data gathered in their clinical trials. Consequently, their data science teams will have access to a structured knowledge database they can use to train AI algorithms.
Step 5: Tap into AI startup ecosystem with relevant knowledge
Andreessen’s example of Disney buying Pixar in order to stay relevant has paid off for Disney, which sold for over 8 billion dollar in movie tickets this year, making Disney the second biggest media company (Forbes).
So yes, AI has started eating software.
What is your play?