Why Software Is Edging Closer to Obsolescence
Generative AI is Set to Disrupt the Software Industry by Redefining Problem-Solving
For the past year, I’ve been exploring a vision that’s becoming increasingly clear — one that has profound implications for the global economy and how humanity approaches problem-solving.
Computers and the software that runs on them were born out of a fundamental human need: to solve problems faster and more efficiently and create new value propositions that could streamline business and personal processes. For decades, this technological evolution fueled incredible gains in productivity, innovation, and economic growth. Software has become the cornerstone of our modern world.
Since using ChatGPT in December 2022, I’ve seen firsthand how dramatically AI can shift our problem-solving approach. What began as an experiment in faster solutions has led me to believe we are reaching a critical inflection point: the software we’ve relied on since the 1960s is on the verge of obsolescence. This evolution will impact everyone and is part of a more significant transformation redefining the technology industry.
The transformation is fundamental. Since the dawn of software deployment in the 1960s, users have had to contort their problem definitions to fit the limitations of the available tools.
Whether legacy mainframe systems or modern SaaS and Apps, software has dictated how we define, approach, and solve problems. However, there were rare moments in computing history when software transcended this rigid paradigm and empowered users to customise solutions to their specific needs. These moments sparked genuine enthusiasm because they gave people control over their problem-solving processes.
There are lessons scattered throughout the history of the software industry.
Consider the invention of word processing or the Apple Mac’s pioneering of the WYSIWYG (What You See Is What You Get) interface. These technologies became incredibly popular because they adapted to the user’s needs rather than forcing the user to adapt to the software.
Similarly, spreadsheets became a revelation in the business world because they allowed users to mould the tool to their specific problems, enabling flexibility that had never existed. People weren’t buying these tools just because they were faster or more efficient; they were buying them because, for the first time, they could solve their problems their way.
Another pivotal moment came with the rise of APIs (Application Programming Interfaces). APIs allowed developers and companies to take off-the-shelf software, making it more adaptable to their needs. By enabling different software systems to communicate with each other, APIs provided users the flexibility to extend and customise existing solutions without needing to reinvent the wheel. This gave businesses the power to solve their unique problems by integrating multiple tools, leveraging the capabilities of each in a way that was impossible with standalone products. In many ways, APIs were a bridge between rigid, one-size-fits-all software and the custom solutions users truly needed.
The enthusiastic embrace of these innovations was more than just a testament to their functionality. They allowed people to shape technology to meet their unique needs — a trend that reaches its pinnacle with Generative AI. But where word processors and APIs offered customisation within fixed limits, AI promises something far more transformative: dynamically creating problem-specific solutions on the fly.
Just like word processing, WYSIWYG, spreadsheets, and APIs, GenAI has the potential to become the ultimate customisable problem solver.
This time, the customisation is far more sophisticated. AI can consider the problem definition before proposing a solution that it then implements, showcasing its immense potential.
Incremental Innovation is an Oxymoron
The term “incremental innovation” is often used in the software world. But let’s be clear — that phrase is an oxymoron. Innovation suggests radical change, not the minor, marginal improvements that most software companies cling to to stay relevant. Actual innovation changes how we think, work, and solve problems. It is not a minor feature update or a new API wrapper. It comes when we rethink what is fundamentally possible.
That’s precisely what AI is doing. GenAI isn’t about adding new bells and whistles to existing systems or making software faster or more user-friendly. It’s about fundamentally redefining how we define, approach, and solve problems. AI doesn’t just help us solve problems better — it rethinks the problem definition itself, a task beyond traditional software's capabilities.
The term “operating system” historically referred to the backbone of how machines work. Shortly, AI will assume this role, becoming the backbone of technology and human problem-solving. We’re already seeing components of this future. With tools like GPT and Claude, we can ask AI to solve specific problems — often bypassing entire suites of software that were once indispensable.
Why should we rely on static applications or even SaaS when we can interact with an intelligent system that generates and runs specialised code on the fly, tailored to a singular need? Once that problem is solved, the code dissolves. There is no need for licenses, updates, or maintenance. AI becomes the ultimate custom problem solver, evolving with each interaction to better understand our needs.
This is an exciting leap for individuals. We no longer have to mould ourselves to fit the limitations of the tools available. But this is a cataclysmic shift for businesses, especially those entrenched in the software industry.
Economic Fallout
The consequences of this transformation aren’t just technical; they’re also economic. Software companies, particularly those built on the SaaS model and on-device Apps, face an enormous challenge. They are walking blindfolded toward a cliff, with billions of dollars of intellectual property capital, investments, physical assets, and workforce expertise at risk of becoming stranded. SaaS, cloud-based solutions, and apps face obsolescence in a world where AI becomes the universal problem solver.
It’s not just that AI will replace software — it’s that software will become unnecessary. Why build or maintain a program when you can instruct an AI to create what you need when you need it? Companies that don’t recognise this shift will see their products and the jobs attached to them vanish almost overnight.
This isn’t a distant future for the employees and executives in these industries. The pace of AI’s evolution is exponential, and what feels like a distant possibility today could arrive much sooner. The economic fallout from this shift will be enormous. We’re talking about one of the global economy’s largest and most profitable industries that has defined the last 70+ years — becoming obsolete.
However, perhaps ironically, AI might provide solutions for these companies if they are willing to recognise their precarious position. They could chart a path toward continued relevance by leveraging AI to create strategic plans, experiment with new models, and adapt their business models. The motto here is not complacency but plans and experimenting.
Those who actively explore how to integrate AI as a central part of their operations have a slim chance to position themselves at the forefront of the transition.
We’re approaching a transformative moment, and one thing is clear: the traditional concept of software is on precarious ground. While Generative AI positions itself as the next logical step in problem-solving, it leaves a massive question mark over the future of the software industry — an industry that has been one of the most valuable on the planet.
So, what now? How do software companies prepare for the inevitability of this transition? First, they can’t survive by thinking incrementally. There is no incremental path to survive a revolution.
But I’m not optimistic it will be feasible. I’m convinced that GenAI and AGI will subsume software as we know it today.
The reality is the entire foundation on which the software business model was built could become irrelevant. Billions of dollars of intellectual property, infrastructure, and human expertise may be left stranded as AI takes over tasks that once required massive software suites and engineering teams.
Uncertainty always presents threats and opportunities. For now, in order to survive, companies need to experiment, adapt, and embrace ambiguity.
We’re on the brink of a paradigm shift in which the notion of static and pre-built software fades into obsolescence, replaced by dynamic, problem-solving AI agents.
The good news is we’re entering a new age of problem-solving. Just as spreadsheets and word processors revolutionised how people worked by offering customisable solutions, GenAI has already democratised access to advanced problem-solving. But the leap this time is far more profound.
AI will provide new tools and replace the established concept of software with something more fluid, adaptable, and, ultimately, more human.
Postscript from OpenAI’s developer conference, DevDay, today, Oct 1st 2024, in San Francisco.
Everything OpenAI announced today endorses the views I’ve expressed in this article.
The big news is that the company launched a Realtime API that promises to allow anyone to build functionality similar to ChatGPT’s Advanced Voice Mode within their own app. Paired with their new model o1, released a few weeks ago, OpenAI is creating an new way to build software.
o1 can prototype anything you have in your head in minutes rather than months. And the Realtime API enables developers to build software with a novel interface — life-like voice conversations — that was previously only the domain of science fiction.
OpenAI also announced the following:
- An increase of the API rate limits on the o1 model equal to that of GPT-4o (10,000 requests per minute)
- A reduction of the price of GPT-4o API calls with automatic prompt caching, making repeated API calls 50 percent cheaper with no extra developer effort
- A multi-modal fine-tuning API that allows developers to fine-tune GPT-4o with images in addition to text
- Triple the number of active apps are on the OpenAI platform from last year to this year, and there are 3 million active developers.
Let’s get into the details.
o1 victory lap
OpenAI released its new reasoning model o1 two and a half weeks ago, and the company’s excitement about it was palpable in the room. OpenAI’s head of product, API Olivier Godement described it as a new family of models, distinct from GPT-4o, which is why they reset the number on the model back to one.
At the beginning of the AI wave there was a lot of talk about what artificial general intelligence would look like: Would it be one model to rule them all — GPT-5 for all comers — or would there be different models for different purposes? Today OpenAI told developers that they would be investing heavily in both next-generation GPT-4o and o1-type models. The company is betting on a diversity of models for different use cases as the way forward.
o1 models excel at reasoning — which OpenAI defines as being able to think in a chain of thought format — which makes them better at tasks like programming, but slower and more expensive. These models will be used for tasks that require more advanced reasoning, but they won’t become the default model choice because most prompts won’t require it.
The increase in programming power was on display today. Romain Huet, OpenAI’s head of developer relations, did a live demo where he used o1 to build an iPhone app end-to-end with a single prompt in less than 30 seconds. He also demonstrated building a web app to control a drone that he brought on stage, and used it to pilot the drone for the audience.
It would’ve been possible to do these demos with previous GPT models, but they would’ve taken much longer to build, and probably wouldn’t have been suitable for a live audience.
With o1, OpenAI is previewing a future where you can go from idea to app in a minute or two.
About the author: Greg Twemlow, Founder of XperientialAI©.
Greg Twemlow: “Empowering future leaders and organizations by designing and delivering AI-integrated experiential learning programs that blend technology, ethics, and philosophy. Through consultancy, mentorship, innovation coaching, and thought leadership, I help CEOs, business leaders, and individuals ethically and efficiently implement AI solutions while fostering a culture of trust, integrity, and wisdom in an AI-driven world.” Contact Greg: greg@xperiential.ai
