AI-generated summary
The future of programming is being transformed by Software 2.0, a concept popularized by Andrej Karpathy, Tesla’s AI director. Unlike traditional programming with languages like C or Python, Software 2.0 leverages artificial intelligence and machine learning—particularly neural networks—to automate software development. This approach trains models using labeled examples of desired and undesired behaviors, enabling programs to be created or refined by machines or with machine assistance. This innovation promises to accelerate complex tasks such as programming autonomous vehicles and streamline code customization and automation for organizations.
A key development within Software 2.0 is AutoML (Automated Machine Learning), which automates the entire machine learning pipeline, including data processing, algorithm selection, parameter tuning, error detection, and result analysis. This technology democratizes AI development, allowing even those without prior experience to build machine learning applications. Furthermore, the emergence of MLOps—the machine learning counterpart to DevOps—is creating standardized practices for deploying AI systems more efficiently and securely. Software 2.0 is already impacting diverse industries, including automotive, entertainment, and finance, by enabling continuous AI improvements, faster deployment cycles, and regulatory-compliant model management. Additionally, its applications extend across multiple domains such as visual and speech recognition, language translation, and gaming, signaling a profound shift in how software will be developed and utilized in the coming decade.
Software development will be one of the fields where artificial intelligence will be really useful for people: more ease, more speed, and fewer errors. That's the promise of software 2.0
We recently published the major technological trends that are coming this decade. Today we talk about one of them, the future of programming. Software 2.0, which refers, among other things, to the use of artificial intelligence – neural networks that use machine learning – to assist programmers in software development. That is, programs written by machines or by programmers helped by machines.
Software 2.0 promises to accelerate highly complex use cases such as programming for autonomous vehicles. It will also provide organizations with a much easier, iterative, and intuitive way to customize existing code and automate programming tasks.
Origins of the Software 2.0 concept
The concept of software 2.0 was coined by Andrej Karpathy, Tesla’s director of Artificial Intelligence. Karpathy argues that we are at the beginning of a profound change in the way software is developed. Until now, we’ve built software with programming languages like C, Java, or Python, using a process that’s slow and error-prone. He suggested something radically different: using machine learning to program by example. That is, we can collect many examples of what we want the program to do and what it shouldn’t do (examples of correct and incorrect behavior), label them appropriately, and train a model to work properly with new inputs. In short, we can use machine learning to automate software development itself.
The use of software 2.0 is going to become widespread due to emerging trends in machine learning that try to solve many of the difficulties and complexities that currently have the development and application of AI models.
Artificial intelligence that knows how to program
Thus, one of the most surprising branches of Software 2.0 is what is called AutoML (Automated machine learning), that is, it is the process of automating the tasks necessary to use machine learning solutions. Giants such as Google already have
- Perform data processing and filtering,
- choose which machine learning algorithm to use,
- optimize algorithm parameters,
- detect configuration errors and finally,
- Perform the analysis of the results obtained.
Surprising, isn’t it?
From DevOps to MLOps
Well, that’s not all. The relative homogeneity of neural networks could create new open-source libraries that are increasingly modular, interoperable and usable across domains. That would further reduce the technical barriers to entry for this class of applications.
In any case, the entire process of putting machine learning applications into production still needs to mature. Just as until now we talked about DevOps as the process of designing, developing, and putting into production software applications, from now on we begin to talk about MLOps, as the practice of designing, developing, and putting into production machine learning applications.
A shift in focus is taking place in the production of AI systems, creating a set of best practices and replicable MLOps processes that will help deploy services much faster and more securely. If you want to know more about MLOps, one of the pioneers in this field, Yaron Haviv, explains it very well in his article Looking into 2022: Predictions for a New Year in MLOps.
Areas of application
There are potential applications in practically any industry, we highlight three:
Automotive industry: The collection of millions of driving data enables continuous AI improvements from automated driving applications, without human intervention in reprogramming.
Entertainment industry: Metaflow, developed by Netflix, provides a unified API to design the workflow, run it, deploy it to production, and provide automatic version tracking. As a result, the average deployment time on Netflix dropped 16 times, from four months to seven days. Metaflow is currently available as open-source.
Financial: Model-as-a-Service (MaaS) platforms already exist to help financial services firms assess risks when building models for AI applications, with simple monitoring and debugging tools and metrics. These platforms reduce model design and deployment time from months to minutes without compromising risk and ensuring regulatory compliance.
Horizontally, Software 2.0 is beginning to be applied in the following fields:
