From: aidotengineer
The shift towards integrating artificial intelligence within organizations presents two distinct models: AI-enhanced businesses and AI-native companies [00:02:06]. This distinction is crucial for understanding the profound transformation occurring in the workplace [00:06:02].
Defining the Models
AI Enhanced Businesses
An AI-enhanced business incorporates AI here and there, using it for tasks like chatting or document generation to achieve efficiency goals [00:05:11]. The key characteristic of an AI-enhanced business is that it would still function without AI, albeit less efficiently [00:05:22]. This model is akin to a car with driver assist [00:05:33].
AI Native Organizations
An AI-native company (also referred to as an “agent native” company) is built from the ground up with AI agents at the core of everything [00:03:05]. AI is central to how work gets done [00:02:00], augmenting human productivity and intelligence [00:03:11]. For these organizations, AI is integrated into the foundation of their product, operations, and culture [00:03:17]. It serves as an engine that moves product, operations, and culture forward, with every employee relying on it to perform their job [00:03:34].
If AI agents were removed from an AI-native company, employees would be unable to achieve as much, mundane or unsatisfactory tasks would need to be performed manually, and productivity would significantly decrease, leading to increased costs [00:03:52]. An example of this is an agent handling change logs and documentation, which, if removed, would be a major loss for engineers [00:04:11]. Products would also feel outdated, unintelligent, and less useful, failing to offer the 10x or 100x efficiency boost to customers [00:04:50]. This model is comparable to a car on autopilot, directed by humans but fundamentally self-driving [00:05:36].
Core Attributes of an AI Native Company
While still in early stages, several defining attributes characterize an AI-native company:
-
AI is Omnipresent AI is not confined to a single team, feature, or aspect of the company’s culture; it is everywhere and in everything [00:06:36]. Departments like product, customer support, and operations are expected to have agents performing routine and daily work [00:06:48]. These departments rely on agent interfaces for handoffs and integration, ensuring efficiency [00:06:59].
-
Humans as Conductors In an AI-native company, people are no longer cogs in a machine but act as conductors [00:07:43]. This implies a need for different hiring profiles and organizational charts [00:08:03]. The structure becomes flatter and leaner, with middle management layers shrinking as intelligent systems handle coordination and execution [00:08:14].
- For example, after a morning product discussion, agents can begin building prototypes and assist with messaging, copy, and documentation within a day [00:08:23]. This enables rapid prototyping, refinement, and learning [00:08:43].
- The organizational chart in the near future is expected to resemble a network of humans and AI working together, rather than a pyramid [00:08:55].
-
Experimentation and Iterative Culture While a core value in tech startups, the ultimate realization of an experimental and iterative approach is possible when AI handles routine work and assists with prototypes [00:09:10]. This allows humans to focus on higher-level tasks [00:09:32]. Furthermore, agents working within the company can learn and improve over time, like Cognition’s dead end, which has learned and documented code, becoming a “superpower” [00:09:44].
These attributes—AI at the core, humans as orchestrators, rapid experimentation, and self-learning agent evolution—combine to create an organization that operates on a fundamentally different model than traditional companies [00:10:05].
The Typical AI Native Workday
A typical workday in an agentic enterprise includes overseeing what AI has done or needs to do [00:10:53]. Individuals might start their day by interacting with AI (e.g., ChatGPT or deep reasoning tools) during a walk or drive to kick off complex thinking or product ideas [00:11:22]. This can lead to agents (like “Devon”) already working on tasks, generating pull requests (PRs), or addressing bugs and documentation issues by the time the human focuses on their daily to-do list [00:11:51].
The morning might involve checking on agent activities and initiating new tasks for them, with reviews of their output (e.g., PRs, collateral, emails) occurring around lunchtime [00:12:25]. Every employee essentially becomes a “lead manager” of their AI agent counterparts, responsible for the jobs they are hired to do [00:12:46].
- For example, orchestrating content marketing “swarms” of multiple agents to optimize copy, determine social posting times, and schedule automatically [00:13:02]. This leverages human expertise with asynchronous AI workloads, making everything ready when needed [00:13:19].
This workflow leads to a flatter team structure and potentially new titles that combine domain expertise with AI know-how, such as “AI engineer” or “AI customer lead” [00:13:30].
Rethinking Hiring
The rise of AI-native organizations necessitates a fundamental rethinking of the hiring process and candidate profiles [00:14:03].
- AI Fluency as a Must-Have: Just as word processing skills became expected for office jobs, AI fluency is becoming a prerequisite for hiring in an AI-native company [00:14:41]. The expectation is that employees can efficiently guide agents to tasks and leverage their expertise through AI [00:15:26]. Interview processes are becoming skeptical of candidates unfamiliar with AI [00:15:53].
- Shift in Leadership Hiring: When hiring for leadership roles, such as a VP, the focus shifts from their network of human hires to their ability to use and bring in people who know how to use AI agents, emphasizing AI fluency [00:16:25].
- Onboarding: Onboarding processes will also change, with new hires potentially spending their initial weeks solely focused on setting up their agents to perform their jobs [00:17:03]. It may even become reasonable to attach an engineer to a team to ensure their agents are set up and running successfully [00:17:19].
Conclusion
The current era represents a profound shift, akin to the Industrial Revolution or the advent of the car industry [00:17:42]. AI agents are expected to be deeply embedded in every aspect of business, requiring a rethinking of roles, skills, culture, and operations [00:18:06]. This movement is not just about using AI as a tool; it’s about building businesses around AI as a core primitive [00:18:22].
Companies like Agentuity have demonstrated the potential, building an entire agent cloud infrastructure from scratch in a few weeks—a feat previously unheard of [00:18:48]. For founders and tech leaders, the challenge is to fully embrace this paradigm shift, even if it means re-evaluating years of built-up experience that may no longer be entirely valid [00:19:21].
As a PWC report noted, using AI only for small efficiency gains means falling behind [00:19:55]. Organizations must start from first principles, step back, and reimagine their company and culture for this future [00:20:12]. This includes redesigning org charts and redefining roles and skills to allow human-to-agent teams to scale their impact exponentially [00:20:21]. While this shift may create friction, it also offers a significant “unfair advantage” for those who embrace it [00:20:43].
The ultimate question for businesses is: Is your company merely using AI, or is it ready to be built around AI? [00:20:50]