As we see it…
AI presents the most significant transformation we will witness in our lifetime and potentially all of human history.
Every company is now an AI and data company.
However, while it is easy to do AI, it is hard to do it well.
The latest advancements in generative AI have led to massive democratization and transformative effects. However, integrating these capabilities into your systems, business, and culture is far more complex than just prompting ChatGPT.
Especially as new advancements, tools, models, approaches, and architectures appear to be announced on a daily basis.
It is A LOT.
We exist to distill the signal from the noise and help you realize the value of AI through the power of your data.
The opportunity at hand is MASSIVE.
We believe three core forces are driving five paradigm-shifting changes to create this opportunity.
Causes:
#1. The commoditization of models
AI models are becoming both more powerful and more affordable—driving mass adoption.
We are witnessing Jevon’s Paradox firsthand, which suggests that when technological improvements increase the efficiency of using a resource, demand for that resource paradoxically increases. This makes even the seemingly most impossible AI use cases viable, skyrocketing the demand for AI.
Then you have open-source, which is driving competition and shared learning at the foundation model level, making AI more accessible than ever.
#2. The emergence of reasoning
New architectures and approaches—such as Mixture of Experts (MoE), Chain-of-Thought, Tree of Thoughts, and ReAct—are giving AI the ability to reason. This unlocks a host of use cases that were once exclusively reserved for humans with our unique ability to reason.
#3. The emergence of AI agents
The element that ties all this together is the emergence of AI agents. At their core, agents are applications that attempt to achieve a goal by observing the world around them and acting upon it using specific tools at their disposal.
Agents are composed of 3 core components:
- The Model: The LLM or LLMs powering the agent—essentially its brain.
- The Orchestration Layer: Governs reasoning and decision-making.
- The Tools: Enable agents to take action and interact with the world.
This combination gives the agent autonomy, allowing it to act independently of human intervention to achieve a specific goal.
Agents won’t work alone either. We will see agents working with a multitude of other agents in asynchronous and synchronous ways on our behalf.
Effects:
#1. Software-as-a-Service (SaaS) will shift to Service-as-Software:
Just as cloud computing transformed software into a service, AI is now transforming services into software.
Businesses that once relied on human labor to deliver services will increasingly leverage AI agents to automate and enhance service delivery at scale. This shift will expand the addressable market from software alone to the entire service economy, measured in the trillions of dollars.
#2. A New Blue Ocean at the Application Layer Will Emerge:
The shift to Service-as-Software will create a vast new opportunity at the application layer, ripe for disruption.
While foundation models are powerful, they are also general-purpose—meaning that without the right interface, structure, and integrations, they can be difficult for users to leverage effectively. Add in hallucinations, messy workflows, and disparate data sources, and the complexity only increases.
Some argue that AI wrappers lack defensibility. However, just as most SaaS products today are structured workflows that could technically be executed in Excel, users consistently choose convenience, usability, and seamless integrations over raw capability.
Humans value ease of use, and AI applications that provide well-designed, domain-specific solutions will dominate.
The biggest opportunities will emerge in verticalized industry-specific AI applications that combine foundation models with fine-tuning, proprietary data, and domain-specific workflows—unlocking massive new markets at the application layer.
#3. How We Build Software Will Foundationally Change:
AI is not just an enhancement to software development—it will foundationally change the entire practice.
From gathering requirements to designing architecture and user interfaces, from coding to testing, AI will play an integral role. Small teams with AI agents at their disposal will accomplish what once required massive engineering teams.
This shift will fundamentally change the business model of software development, lowering barriers and enabling anyone with an idea to build. However, the most critical roles in this new paradigm will be senior engineers with strong architecture and systems knowledge who are skilled at effectively guiding, interacting with, and deploying AI.
Our Generative-Driven Development™ (GenDD) methodology was built to capitalize on this shift. By seamlessly integrating AI and agents throughout the entire software development lifecycle (SDLC), we enable faster innovation, greater efficiency, and a novel approach to software engineering.
#4. Novel UI and UX Paradigms Will Emerge:
Traditional user interfaces were built around the limitations of human interaction—buttons, menus, and structured workflows.
AI introduces a more natural, conversational, and adaptive user experience. Instead of users adapting to rigid interfaces, AI-powered interfaces will adapt to users. Expect AI-driven copilots, voice-activated workflows, and dynamic, context-aware applications that anticipate needs rather than requiring explicit inputs.
Additionally, AI agents will no longer just be tools—we must now consider them as users and producers of software.
The emergence of AI-driven systems means that UI and UX paradigms will need to evolve to accommodate machine-to-machine interactions alongside human interactions. This shift requires rethinking workflows, permissions, and how AI agents collaborate with both humans and other agents to maximize efficiency and outcomes.
#5. The Promise of Automation Will Finally Be Realized:
For decades, automation followed a deterministic approach—where rule-based systems executed predefined tasks with minimal deviation.
Now, with AI agents leveraging reasoning capabilities and dynamic access to tools, we are shifting to a probabilistic approach to automation. Instead of rigid workflows, AI agents will make real-time decisions, adapt to changing environments, and complete complex, multi-step tasks that previously required human intervention.
This evolution means we are not just automating individual workflows—we are automating entire job functions. Industries will see roles traditionally performed by humans being augmented or fully replaced by AI agents capable of reasoning, problem-solving, and executing high-level strategic tasks.
The implications of this shift are profound, redefining the nature of work, business structures, and value creation across every sector.
So what does this mean for you?
This is leading to a massive inflection point between new entrants and incumbents, with incumbents facing the most significant Innovator’s Dilemma of our time.
New entrants have the advantage of building AI-native businesses from the ground up, leveraging constraints to drive innovation in ways that incumbents cannot. Their need to operate lean compels them to integrate AI into every aspect of their business from day one, leading to business model disruption and rapid innovation.
However, incumbents hold one key advantage—their proprietary data.
Success in this new landscape will depend on their ability to transform fragmented, poorly governed data into clean, accessible, and well-governed data and then build AI-native solutions that capitalize on this asset—both by transforming operations and creating entirely new and novel, AI-powered products and services.
This is why we have invested in Databricks as our core partner—the industry leader in Data Intelligence.
And that’s why we exist: To distill the signal from the noise and help you realize the value of AI through the power of your data.
