The world of software is on the cusp of a radical transformation. AI is moving faster than anyone could have predicted, and we’re about to enter the era of Software 3.0, where software companies moves from using AI – to becoming AI. They’ll become autonomous entities, managed and powered by fleets of AI agents that do it all: build, maintain, and improve software with almost no human input. This is a whole new game of software.
This research piece explores how software companies of the future will be built, how they’ll operate, and how they’ll scale when AI becomes the main engine behind every line of code, every product update, and every strategic decision. Welcome to the frontier of autonomous software, welcome to the future.
Imagine a software company that never sleeps, a company where AI agents craft code, fix bugs, talk to customers, and redesign products – all being done autonomously. In Software 3.0, software companies will comprise of interwoven systems of AI agents with distinct specialties – experts working together toward a common company goal.
Currently, we’re moving from traditional programming in “Software 1.0”, to (partly) cloud-based and machine learning-driven optimization in Software 2.0, and in the coming year or two – we’ll enter the unchartered waters of Software 3.0, where an entire company goes AI native. Including parts of the backend hardware stack.
This means that the concept of what constitutes a software company will (again) be redefined, which means that anyone currently developing code, building a software company, or investing into one – will have to change their playbook.
The winners in this next generation of software will likely be those who embrace the AI-driven evolution, fundamentally transforming every bit of their operations to build what could be described as “autonomous software companies”. This novel software species will undertake a ground-up reinvention of how their whole tech stack is structured.
We believe that the next generation of software companies will be less about teams of coders and more about systems of AI agents and collaborators. We’ll have “AI Agent Directories” that becomes one of the most valuable assets of a company. And for investors, this would mean that it is high time to rethink the playbook of evaluating the future of what a software company is.
THE SOFTWARE PARADIGMS – FROM CODE TO MODELS
Software 1.0: Think of the first software era as the one with manually written rules and procedures. Code was (and still is) human-generated, line by line, with programmers translating logical steps into instructions. The tech stack in Software 1.0 was relatively simple – with languages (like Python, C++, JS, CSS, etc,) frameworks, databases. Humans doing all the work.
Software 2.0: This was and currently is the beginning of AI as we know it. This is where machine learning models started doing the heavy lifting for specialized tasks. Today, a lot of engineers train data models that recognize patterns, make predictions, and automate complex tasks. However, the ecosystem is still very much human-centric. These models are working alongside traditional codebases and teams, extending but not really transforming the software company structure.
Software 3.0: In Software 3.0, the company itself is increasingly a network of AI agents. Each piece, from product design to deployment, to customer success, becomes autonomous and interdependent, working like a symphony with zero downtime. In this new stack, the distinction between developer and machine blurs, pushing entrepreneurs to reimagine software sort of like a self-sustaining, evolving entity. Knowledge graphs, retrieval-augmented generation (RAG), and a sophisticated AI Agent Directory are core components.
NEW BLUEPRINT – WHAT IS A SOFTWARE 3.0 COMPANY?
In a world where AI agents can create, replicate, and self-optimize, the concept of a “software company” requires a fundamental rethink. AI will not be add-ons to their main engine, instead, it’ll become the company itself. It’ll become a system of interlocking and autonomous AI functions, sitting on top of proprietary data. Below are a few (among many more) key elements in the Software 3.0 tech stack:
AI Agent directory: Imagine a virtual employee directory, but instead of human staff, it's filled with AI agents. Each AI is an expert in its designated field, from software development to UX, marketing to compliance. These agents work together seamlessly, communicating across departments to carry out company goals. Teams, as we know them today, are evolving into clusters of inter-operating agents, managed by a small group of human strategists.
Self-upgrading codebase: As the backbone of Software 3.0, this codebase is in a constant state of evolution. Imagine it like an AI-driven metabolism that continuously ingests user feedback, adapts, and tests new features. AI agents monitor usage patterns, anticipate issues, and roll out updates without a single line of human code.
RAG & Knowledge graphs, on top of proprietary data: Retrieval-augmented generation (RAG) and advanced knowledge graphs are at the core of Software 3.0. These systems will access, organize, and interpret massive amounts of proprietary data. They will allow for ultra-fast data retrieval and situational learning, helping companies respond in real time with deeply informed decisions and customized experiences for each user.
Proprietary AI models: One of the most crucial assets in a Software 3.0 company could be its proprietary AI model, especially LLMs trained on unique and proprietary datasets. This model could become the prime market differentiator for some software vectors, offering capabilities that generic models can't match. It is not unfathomable to imagine a future where more or less all companies train their own AI models, on their proprietary data, and distributes this model through platforms where customers can access them.
INVESTING PARADIGM – BUY THE MACHINE, NOT THE PRODUCT
For investors investing into the software space, the best-practise investment playbook of the past, will most likely become outdated at some point in time. It might take one year, or three, but it will no doubt get there. Where Software 2.0 companies focused on scalable products and developer talent, Software 3.0 demands something very different. It requires agile, autonomous architectures, AI-centric operations, and a large portion of creativity constantly being embedded as code.
As such, investors must look for companies that think beyond product features and focus on autonomous, self-improving systems. In other words – you’ll be rewarded by buying the machine, not the product. Companies that rely on their current product and traditional internal structures, i.e. without having an embedded core of constantly adapting to new market setting – these will not stand the test of time. Chegg Inc., is an example of this, whose stock is down 99% since 2021, as their competitive edge has been obliterated by the emergence of OpenAI and AI models. More examples will come.
Consider an investor having two options to invest in a specific product category. Either Company A, which has built a product with thousands of lines of code that is immensely successful – or Company B, which has not come as far as Company A when it comes to the product that solves a customer problem, but it has started to employ AI agents that collaborate and even write code. In this case, Company A has much better financial metrics than Company B. However, if we know that we’re about to enter a time of structure-shattering disruption, in favor of companies that are built like adaptable machine – an investor needs to look beyond the horizon of financial metrics.
And furthermore, if the future of software truly is autonomous – then in the first few years of this transition there will surely be a significant disruption of the current software business landscape. Because, when an industry transforms itself from something old, to something new – there is always market shaking disruption. No matter the decade or industry. In a high majority of historical disruptions, novel companies being built and scaled into the marketplace, has disrupted many legacy companies.
NOVEL MARKET ACTORS WILL ARISE
When it comes to the shift to Software 3.0, it means that there will be novel companies with novel customer values and mechanisms. Firstly, in our view at least, there will be new types of organizations comprised of AI agents, that form autonomous software companies.
Secondly, there will be companies that monetize singular AI agents that become part of another company’s product or product portfolio. Sort of like white labelling. This thesis is enabled by the fact that AI agents will autonomously be able to connect to and bridge between various types of code and software applications, to a much higher level than what is possible today. AI agents will be able to become their own types of companies, earning profits for their owners. You can even imagine multiple companies (with their respective agents or applications) coming together and building a unified frontend. Like an alliance of companies with a unified brand, but different backends.
And lastly, there will be new types of platforms that enable these so-called singular AI agents (for lack of a better name), so that anyone can access and use them. We already have OpenAI’s store of GPTs made by both companies and private individuals, as a prime example. There will be many more.
THE DARK SIDE – AUTONOMOUS COPYCAT FACTORIES
In a Software 3.0 world, it’s also easier than ever to replicate successful models. Imagine an autonomous “meta-company” of sorts, that exists solely to clone other software companies. With AI agents working non-stop, it could release a new, fully autonomous competitor every hour, each optimized for different sectors or niches. This level of replication challenges the notion of a unique competitive advantage – if every successful software company can be replicated in real-time, what does “moat” even mean?
This could mean a world of intense, rapid iteration, but also relentless competition. Companies that differentiate through unique algorithms, personalized customer insights, or proprietary data may survive, while others risk being commoditized or even erased by these AI-driven copy factories. If a company only provides a specific application, without building a proprietary database for example, they will have a tough time..
AUTONOMOUS FINANCE – WHEN MONEY MOVES ITSELF
Now, let’s take this a step further – because if AI agents can build and run software companies, who says they can’t manage money too? Enter autonomous finance, where AI agents can move and invest capital on their own. In this future, financial software companies (or specific AI agents) will be able to manage portfolios, handle transactions, and allocate resources without a human ever lifting a finger.
A tangible use case would be an AI agent being employed by a company to autonomously invest their capital in real-time, and constantly calibrate and mitigate their outstanding risks for their cashflow and balance sheet. All handled by one, or many AI agents or autonomous software company.
For entrepreneurs and investors, this means the rise of financial entities that can operate 24/7, navigating global markets and capital flows autonomously, with precision and speed that no human could match. We’ll most likely have highly secured financial accounts, banks in other words, that are able to be connected to autonomous agents and companies – that in turn blurs the lines between algorithms and financial advisors.
THE NEW AGE OF CREATION – WHEN EVERYONE’S AN ARTIST
Furthermore, in a Software 3.0 world, creativity will not only be reserved for the talented, trained, or the famous – it’ll be open for anyone with an idea and an internet connection. Thanks to AI, the barriers to entry in music, film, gaming, and art – will diminish quite significantly. For example, with platforms like Suno, you can create world-class music in the style of your favorite artist, or your very own original creation, without years of specialized skills or a band behind you. Actually, we created a song about this research piece, check this link out.
In my view, this shift should be considered as potent as the shift from CDs and DVDs to streaming – but potentially even more so because the application layer (the artists and movie makers themselves) could feel the burn of anyone becoming an artist of different sorts.
Put differently, the entertainment landscape could move from “centralized brands” and star-powered icons to a democratized world of individual creators. You’ll not need a record label, a movie studio, or a game development team. For me, it’s not far-fetched to fill my music playlist with AI-generated tracks created by people with absolutely no artistry in their. I just want something good to listen to.
This prospective wave of democratizing AI tools could shake up many of the software companies that cater these industries. Some of the sub suppliers of cloud services, data storage, and of course music record labels – could at some point see demand diminishing and moving to another type of tech stack and distribution channel. It has happened before, and it will surely happen again.
CONCLUSIONS
1) AUTONOMOUS SOFTWARE COMPANIES UPENDS SOFTWARE
Over the course of the past 30 years, we’ve seen the gradual construction of the current software landscape. This structure is about to change. In Software 3.0, the company itself is increasingly becoming a network of AI agents. Each piece, from product design to deployment, to customer success, becomes autonomous and interdependent, working like a symphony with zero downtime. This will usher in a paradigm shift where companies not becoming AI companies, could worsen their competitive values.
2) AI AGENTS BECOME THEIR OWN COMPANIES
Just like autonomous vehicles, when something can perform a task on their own – they’ll no doubt become their own revenue generating units. This means that they can be incorporated and be monetized autonomously to its owner(s). Unique AI agents, meaning that they are difficult to replicate because they have access to proprietary data or AI models (for example), these will monetize their utility for their owner.
3) INVESTING PARADIGM – BUY THE MACHINE, NOT THE PRODUCT
In the past, startups investors have been rewarded by finding impressive teams that have built impressive software products. Going forward, if these teams and software products are not transformed to become autonomous software companies with something that sets them apart (proprietary data, network of market participants, or an AI model, etc.), it will be difficult to withstand the incoming paradigm shift where anything can be copied. The investment playbook could change in favor of “buy the machine, not the product”, because adaptability will become an even more paramount company quality.
4) COPYCAT FACTORIES TO CHANGE THE DEFINITION OF A MOAT
Similar to the thesis above, but a bit different, there will be companies whose sole purpose is to spin out autonomous software companies in real-time. Every single minute, hour, day, and week of the year. This could lead to a future where, just like spambots on social media platforms, we have billions of “artificial” companies that really doesn’t have a human maker. In this future, the definition of a software “moat” could forever change.
5) NEW DIGITAL INTERFACE (SPEAK TO AI AGENTS)
In society, we’ve always had new interfaces for how we consume information. We’ve gone from written paper, to morse code, the telegraph, phones, TV, computers, smart phones, headphones, and so on. Going forward, the informational interface will most likely change in favor of natural language. Meaning that we’ll talk to AI’s that are a million times more capable than ourselves. For example, we could see a near term future where customers place their AI agents (from respective providers) directly through their web browser – accessible by a single click. This would be a new type of digital interface.
6) DISRUPTION TO HIT MANY INDUSTRIES (DIFFERENTLY)
US education services provider Chegg provides a plethora of learning tools, including textbook rentals, online tutoring, and study resources. Over the past few years, their stock is down 99%. This is fully correlated with the emergence of LLM models. Going forward, when anyone can become a coder simply by asking AI to code it for them, it will lead to disruption of many companies, and potentially entire sectors.
Thank you and until next time,
Christopher Lyrhem
Chief Future Officer
Sircular
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