Embracing the Shift: The Future of Work and Software in the Age of AI
In a recent exchange between Alexis Ohanian and Sam Altman, Altman revealed a fascinating wager on when we’ll see the first one-person billion-dollar company. Additionally, the Harvard Business Review offers a sharp and increasingly not very controversial insight: “AI won’t replace humans, but humans using AI will replace humans not using AI tools.” These statements aren’t just provocative soundbites. They’re harbingers of the acute shifts in how we work and how software is developed and distributed.
Ignoring these insights might seem the easier route, allowing us to remain close and comfortable to familiar work structures. However, acknowledging and extrapolating these trends is crucial for everyone, particularly in software and in the context of work functions.
The Future of Company Efficiency: Fewer People, More Output
Altman’s prediction about the emergence of ultra-lean enterprises highlights a pivotal trend: the diminishing need for large teams to achieve high outputs. This shift isn’t just about smaller headcount necessities— it’s about using software to maximize efficiency. As AI and machine learning continue to evolve, businesses that integrate these technologies will outpace those that don’t. The focus will increasingly be on using AI to handle specialized tasks, allowing human employees to pivot to more generalist roles who oversee and integrate AI outputs. For people who are in Sales, Marketing, Channel, Partnerships and adjacent functions today, that could mean a consolidation into a singular “GTM” role in the near-future where all of these roles, tasks and outcomes are owned by a few generalists, or possibly even one, with the extension of AI and agents.
The Evolution of Software: Specialization and Consolidation
The next decade will witness a Cambrian explosion of software development, surpassing the volume seen in the previous ten years. B2B software that emerges today, in an AI-native world, will reshape how work is done, and dramatically increase output efficiency with the agent at the nucleus. Last week alone, AI native startups raised $1.4 Billion.
This new wave of software will likely be more specialized, designed to perform specific tasks exceptionally well. The creation of such software will be driven by smaller, more agile teams that can quickly adapt and use specialized AI tools.
However, the landscape of how this software is distributed and used could bifurcate in the near future. On one hand, the mega-platforms like Microsoft, Google, and OpenAI may further consolidate their ecosystems, simplifying user experiences by integrating a multitude of tools into seamless interfaces, personalized to the individual user. Imagine if these interfaces could spin up native, ephemeral apps, each with customizable UX tailored in some way to the individual. This could reduce the learning curve associated with adopting new technologies, a significant barrier in today’s software landscape. We see a similar type of consolidation today in the jobs-to-be-done on our phones. Android and iOS have monopolies on the apps we use. There are numerous 3rd party application choices available, yet we are limited to selecting from just two primary platforms where these tools originate: Android or iOS.
On the other hand, there’s a potential for a flourishing market of independent software, agnostic to these large mega-platforms, but still integrated. These tools would integrate into the larger ecosystems while also offering unique functionalities not tied to any single platform, like the majority of B2B software built in the pre-AI native world today.
A more plausible scenario is a hybrid of both; however, the majority of software development and innovation will likely gravitate more strongly toward one framework than the other.
Timing the Transformation
The critical question is not whether these changes will occur, but rather when they will manifest fully. Will we see these shifts in 5 years, or will it take longer? The timeline remains uncertain, but the direction is clear. We’ll see an ever-emerging full-stack professional persona, “the generalist,” who can manage multiple functions within a business by “outsourcing” repetitive and even multi-step, more complex tasks to AI.
So, to summarize in some bullet points:
➼ The future will likely hold smaller person companies, higher output
➼ Rise of human generalists, and diminishing of human specialists
➼ Software may consolidate and shift to being mostly native applications on the larger platforms, or software could continue to emerge as it does today, more agnostically and independently
➼ Either way, there will be more software applications in the next 10 years, but they will likely be more specialized
➼ As specialized software flourishes to compliment the human generalist, traditional software platforms will have to adapt. This presents a challenge for traditional SaaS platforms today with seat-based pricing and packaging models, as seat and team volume will decrease per org.
*Travis Fischer & I are researching a new product. Sign up here to get notified of our progress: Walter.
