The decision over what not to hold can be as important as what to hold.
Since the dawn of the mainframe era in the 1950s through to the two most recent computing platform transitions – mobile and cloud – Software 1.0 has remained at the heart of the technology stack, helping create one of the largest markets in the world, dominated by companies such as Microsoft, Apple, and Alphabet. Historically, the cost of computing power and the limited number of software developers were the main obstacles to broader digitisation. However, the emergence of Software 2.0 has radically redefined what software can achieve, collapsing the costs of building highly customised intelligent applications and, in turn, unlocking a market poised to expand by more than tenfold over the coming two decades.
The transition from ‘Software 1.0’ to ‘Software 2.0’ represents a paradigm shift in the way software is developed and deployed, moving away from traditional structured programming towards machine learning and neural networking. The disruptive consequences are hard to overstate: the entire computing stack is being reinvented to focus on building AI as opposed to traditional software, which has significant ramifications for software, hardware and data processing companies.
Software 1.0 is what we all interact with on a daily basis, whether that be through Microsoft document or excel, Salesforce’s CRM portal, or Workday’s HR management system. Here, humans write explicit code – thousands and thousands of lines – to instruct the computer how to act in every given situation (also known as ‘deterministic software’). The underlying unit of compute for Software 1.0 is the CPU (central processing units, sold predominantly by Intel and AMD); the most entrenched operating system is, of course, Microsoft.
Over the past decade, as every company became a software company and ‘software as a service’ business models became popularised, these have been fantastic investments. Customers have been locked into these ecosystems, revenue has been sticky, competition has struggled to break down the walled gardens of Software 1.0. That is now changing. Traditional enterprise software companies are being challenged for the very first time by a cohort of companies built on Software 2.0 from the outset.
Software 2.0, a concept first introduced by Andrej Karpathy in 2017, is driven by machine learning, with an AI model infused into the software.
This type of software is capable of deciding the best course of action by itself: large datasets define desirable behaviour and neural network architectures provide the skeleton of the software code, with the model weights determined through the machine learning process. Programming is done through high-level instructions or by providing examples, and the system automatically translates instructions into executable code or model behaviours. The underlying unit of compute here is the GPU (graphics processing units, sold predominantly by Nvidia, which are necessary to accelerate computations and enable real-time processing of complex tasks previously impractical), and the underlying operating system is in fact also Nvidia.
Why does this matter?
Software 2.0 challengers are offering superior products at a fraction of the cost of Software 1.0 incumbents. This comes at a time when CIOs and company executives are scrutinising their IT budgets in order to invest in AI and inject productivity across their businesses. Software 2.0 is built on accelerated computing – an architectural innovation pioneered by Nvidia, based on GPUs, which is 100x faster, 98% cheaper than traditional compute based on CPU architectures. You cannot run AI on traditional compute. As a result, these Software 2.0 challengers are dramatically undercutting the price points of legacy software providers.
The best way to contextualise this price differential is to think of the cost of Software 2.0 as mirroring the cost of inference (i.e AI deployment). The cost of inference fell by c.95% in 2024, driven by OpenAI’s model progress, and we are already seeing tangible cost reductions in 2025 aided by DeepSeek’s innovations. As the cost of inference continues to plummet, this is collapsing the cost of building AI software applications. Much ink has been split over recent weeks about potential disruption to the AI infrastructure and hardware layer as AI models become more cost efficient, but we believe the most potent disruption occurs to what is built on top of these models – barriers to building software are disintegrating at a pace which is eyewatering.
How much better is Software 2.0 vs Software 1.0?
It turns out, a lot. While the costs of Software 2.0 are collapsing in line with inference costs, its capabilities are simultaneously improving in line with model reasoning capabilities. In the first half of 2024, AI was capable of automating c.20% of what a human software engineer could achieve (measured by SWE-bench − the benchmark for tasks the average human software engineer performs). By the end of 2024, this moved up to 50% with the launch of OpenAI’s o1 reasoning model. With OpenAI’s just launched o3 model, this moves to 73%. This means that agential software is an order of magnitude more capable – AI agents can now accomplish around three quarters of our tasks, which we expect to reach c.90% by the end of the year.
The marginal cost of providing Software 2.0 services is falling in line with the plummeting cost of inference and improving in line with large language model reasoning capabilities
Cheapest LLM above 42 MMLU cost/1m tokens
Software Engineering (SWE-bench Verified)
Source: DeepSeek Debates: Chinese Leadership On Cost, True Training Cost, Closed Model Margin Impacts – SemiAnalysis, February 2025
How are we seeing this show up in the real world?
The battle lines have been drawn. Disruption is first of all manifesting itself within vertical software applications (i.e software for specific use cases), because this is the low-hanging fruit. Dominant enterprise software companies today cover too many bases to deliver the highest quality user experience, compounded by the fact that they are purchased by C-suite executives, not the end user. Specialised software 2.0 companies catering to a certain vertical within enterprise software are proving capable of delivering a 10x better experience, with vertical AI agents emerging as the unlock to make it into enterprise’s budgets based on the early productivity gains we are witnessing.
Take Sierra, a software 2.0 startup founded by the former co-CEO of Salesforce, which is now taking on Salesforce head-to-head with its AI agent platform for customer service and relationship management. Based on an architecture that embeds multiple AI models, Sierra’s AI agents are resolving 70-75% customer cases without human intervention, and they are doing so at a price point we estimate that is c.90% cheaper than Salesforce. Put yourself in your CIO’s shoes – which would you choose?
Private company, Perplexity, backed by some of the biggest names in venture capital and AI, is taking on Google’s 90% share in internet search and winning. As opposed to providing sponsored links to our questions a la Google (even when we receive ‘AI overviews’), Perplexity gives us answers in seconds. It even suggests our next question for us. This superior service has been created by a company with just 160 employees versus Google’s 160,000 employees, illustrating how what we once thought was the most dominant business model in the world (Google search commands c.90% gross margins) is now facing the Innovator’s dilemma. We wouldn’t be surprised if ‘to plex’ became the next verb to originate from Silicon Valley.
Further examples span a myriad of use cases, from Tesla’s neural networking approach to autonomous driving, to the AI lawyer Harvey, which is boosting legal document review efficiency by 90%. These productivity gains across industries are simply too large to ignore. The key question, therefore, is whether today’s traditional software incumbents can pivot to embrace Software 2.0.
From a first-principles perspective, we believe that for the most part, today’s Software 1.0 incumbents are on the back-foot. Without getting too technical, the two most important features to build Software 2.0 applications are firstly the ability to integrate thousands of data points at massive scale, and secondly a model-driven architecture. What we are observing is that traditional platforms are struggling to ingest and normalise data from multiple, disparate siloed sources at enterprise scale. This is not something they were built to do. We are talking about the ability to aggregate data across thousands of IT systems, process it in real-time (today’s data velocities are dramatic – sometimes exceeding 1,000Hz cycles) and then store these petabytes or even exabytes of data in an organised and linked fashion. This is no small feat.
Furthermore, without a model-driven architecture (which serves as an abstraction layer to simplify programming), developers have to employ structured programming (the bread and butter of software 1.0) to stitch together all tools and services. The result is a tangled web of API connections which is slow, costly and ineffective. As evidenced by C3.ai – a holding of ours which alongside Palantir, has been building AI applications for the enterprise on model-driven architectures from the outset – building an AI application (in this case a predictive maintenance application) on a model-driven architecture (i.e software 2.0) is 40x cheaper and 100x faster than using structured programming and AWS services to build the same application.
The architectural advantage of Software 2.0
The point here is that those companies built on Software 2.0 from the outset have an architectural advantage. The key misunderstanding of AI or machine learning software for the enterprise is that it all hinges on the prowess of the AI model. This in fact only accounts for 5% of what you need to efficiently design, develop, deploy, and manage AI applications at scale. 95% comes down to model architecture, including data integration and persistence capabilities.
Think of Software 1.0 the way you might think about the house you live in right now: it is constrained by the very foundations on which it was built. By contrast, Software 2.0 requires an entirely new blueprint – one that demands far more than a superficial upgrade. While incumbents such as Alphabet, Microsoft, Salesforce, Adobe and Workday may have the financial clout to attempt a retrofit, they remain weighed down by ageing architectures and entrenched business models. This situation is reminiscent of earlier computing paradigm shifts – from mainframes to PCs, then mobile and cloud – which heralded new market leaders rather than simply reinforcing existing ones.
In our view, these software-as-a-service titans no longer enjoy the same unassailable ‘moats’ they once did. Accordingly, we have divested our holdings in them – most notably selling Microsoft in the first week of October 2024 – as it now faces competitive threats on both price and quality for the first time in its history. We believe the enormous potential of Software 2.0 will not accrue to these incumbents but rather to a new breed of companies purpose-built on a fundamentally different architecture, poised to define the next era of computing.
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