The Red Ball Method
A High-Velocity, Question-Driven Innovation Protocol for Interdisciplinary Teams
Author: Gunnar Øyvin Jystad Fredrikson
Version: Draft for professional and academic review
Abstract
The Red Ball Method is a high-velocity innovation protocol designed to compress both effort-time and calendar-time from problem framing to minimum-value delivery. Unlike many innovation approaches that prioritize structured ideation or controlled failure, the Red Ball Method optimizes for rapid forward momentum by combining interdisciplinary equality, question-driven exploration, and immediate build-to-answer cycles.
Rather than treating convergence as the primary objective, the method treats convergence as a byproduct of accelerated insight generation and minimum value delivery. It can operate independently or function as a protocol embedded within larger frameworks such as Design Thinking, Dual Track Agile, and Lean Startup.
This document formalizes the method, articulates its positioning and novelty, defines boundary conditions, and proposes falsifiable hypotheses for future empirical validation.
1. Positioning and Core Thesis
Most innovation frameworks optimize for one of three things:
- Quality of exploration
- Quality of validation
- Quality of execution
The Red Ball Method optimizes for velocity toward usable value, without structurally sacrificing interdisciplinary equality or learning.
Its central thesis:
Innovation velocity increases when interdisciplinary teams are allowed to own questions, explore them in parallel, and build minimum value artefacts that answer those questions while delivering real value.
Velocity is defined along two dimensions:
- Effort velocity — minimizing unnecessary meetings, permission loops, and idea filtering
- Calendar velocity — minimizing time to something real that stakeholders can use
2. The Shift: From Idea-Led to Question-Led Innovation
Traditional brainstorming is idea-led.
The Red Ball Method is question-led.
A Red Ball is not merely an idea. It is:
A question with enough shape to explore through action, and enough potential to become a minimum value product.
This shift has structural implications:
- Questions reduce ego attachment
- Questions invite exploration rather than defense
- Questions expose unknowns explicitly
- Questions enable build-to-answer cycles
Where some methods promote “fail fast,” the Red Ball Method promotes:
Advance fast toward anything that can work.
Failure is not eliminated. It is de-prioritized in favour of credible forward motion.
3. Core Design Principles
3.1 Velocity as a Primary Metric
The method explicitly optimizes:
- Time from question to build
- Time from build to stakeholder exposure
- Total effort spent per iteration
3.2 Distributed Ownership as Structural Rule
Every participant:
- Selects one Red Ball
- May adapt or reframe any ball
- Cannot cancel another’s ball — only add to it
Equality is designed, not assumed.
3.3 Interdisciplinary Team as Latent Toolbox
Teams are treated as dynamic toolboxes.
The full capability of the team is not known at the outset.
It reveals itself through exploration.
This is a critical departure from role-based models.
3.4 Minimum Value Product (MVP 2.0)
A Minimum Value Product must:
- Be usable by a real stakeholder
- Deliver measurable value
- Answer at least one key innovation question
- Enable forward movement
This differs from many MVP interpretations that focus solely on hypothesis testing.
4. The Red Ball Velocity Loop
Step 1 — Assemble a Small Interdisciplinary Team
Maximum seven including facilitator.
Step 2 — Frame the Strategic Question Space
Define what we know, what we do not know, and what must become true.
Step 3 — Generate Question-Balls
Each participant selects one question worth pursuing.
Step 4 — Parallel Exploration
Participants explore independently through rapid builds, reframing, and critique.
Step 5 — Build-to-Answer
Each ball must produce something tangible.
Step 6 — Momentum Selection
The team selects the ball with the strongest forward momentum.
Step 7 — Minimum Value Delivery
The selected ball becomes a usable artefact.
Step 8 — Continue or Release
Evidence determines scaling, adaptation, or archive.
Convergence is present, but velocity is the driver.
5. Embedding Inside Other Frameworks
The Red Ball Method is best understood as a high-velocity upstream protocol.
Inside Design Thinking
Design Thinking emphasizes empathy, problem framing, and iterative prototyping.
Red Ball can replace or compress:
- Early ideation workshops
- Extended brainstorming phases
It accelerates the move from empathy insights to tangible artefacts.
Inside Dual Track Agile
Dual Track separates discovery and delivery.
Red Ball can serve as:
- The discovery engine within the discovery track
- A bridge between discovery and backlog-ready delivery
Inside Lean Startup
Lean assumes hypotheses are clear.
Red Ball operates before hypotheses are stable.
It generates testable hypotheses through action rather than speculation.
6. Comparison with Adjacent Frameworks
| Framework | Primary Focus | Speed Orientation | Ownership Structure | Question-Driven? | Minimum Value Focus? | Red Ball Differentiator |
| Design Thinking | Empathy & divergence | Moderate | Collective | Partially | Iterative | Faster divergence-to-build |
| Google Sprint | Time-boxed validation | High (fixed) | Centralized decision | Limited | Prototype validation | More flexible, parallel ownership |
| Lean Startup | Hypothesis testing | Iterative | Founder-led | Yes | MVP | Operates before hypotheses stabilize |
| Agile/Scrum | Delivery | Iterative | Role-based | No | Incremental delivery | Upstream velocity engine |
| Dual Track Agile | Continuous discovery | Continuous | Mixed | Yes | Backlog ready | Stronger parallel exploration |
| Lean UX | Collaborative UX | Moderate | Shared | Partially | Prototype feedback | Structured equality rule |
| Double Diamond | Divergence/convergence | Moderate | Phase-based | Partially | Not explicit | Velocity prioritization |
| Jobs To Be Done | Customer need framing | Slow/moderate | Analytical | Yes | Not build-centric | Build-to-answer emphasis |
| TRIZ | Structured problem solving | Analytical | Expert-driven | Yes | No | Hands-on interdisciplinary builds |
| Effectuation | Entrepreneurial logic | Adaptive | Founder-centric | Yes | Action-based | Formalized team equality |
| Stage-Gate | Governance control | Slow | Hierarchical | No | Formal validation | Radically higher velocity |
| Continuous Discovery | Ongoing testing | Continuous | Product-led | Yes | Yes | Explicit parallel ownership |
| Cynefin-based experimentation | Context classification | Contextual | Expert-led | Yes | Varies | Less analytical, more build-driven |
| Theory of Constraints (innovation flow) | Bottleneck removal | Flow-based | System-level | No | No | Micro-level question velocity |
7. Strengths Emerging from This Reframing
- Structural velocity as measurable outcome
- Distributed agency embedded in process rules
- Question-driven exploration reduces ego defence
- Build-first mentality compresses theory-to-practice gap
- Flexible insertion into other frameworks
8. Boundary Conditions and Safety Constraints
The method is not ideal when:
- Regulatory validation must precede exploration
- Strategic direction is fully defined and stable
- Hierarchical decision control cannot be relaxed
- Psychological safety cannot be reasonably established
Safety rule:
Ideas violating legal, ethical, or safety standards are filtered before exploration.
9. Falsifiable Hypotheses for Validation
H1: Teams using parallel question-balls will reach usable artefacts faster than teams using serial ideation.
H2: Explicit distributed ownership increases engagement and iteration throughput.
H3: Question-led framing reduces defensive behaviour compared to idea-led brainstorming.
H4: Minimum Value Products produced under velocity constraints will produce comparable learning outcomes to traditional MVP cycles but in shorter time.
10. Is This Novel or a Remix?
The Red Ball Method is not a rejection of existing frameworks.
It is a recombination with a different center of gravity.
Its novel contribution lies in:
- Making velocity the primary design constraint
- Treating distributed ownership as structural rule
- Replacing brainstorming with question-driven build cycles
- Formalizing minimum value delivery as both learning and value instrument
- Operating as a bridge protocol across frameworks
If future validation shows it is merely a variant of existing discovery practices, it may be best positioned as:
The Red Ball Protocol: A velocity loop for interdisciplinary innovation teams.
Closing Statement
The Red Ball Method is an attempt to formalize a practice observed across multiple innovation contexts over a decade: interdisciplinary teams move fastest when they are equal, question-driven, and building immediately toward value.
This document is an invitation for critique, testing, and refinement.
Beyond Competitive Advantage: Strategic Continuity in the Infinite Business Landscape
Modern strategy literature is saturated with the language of advantage: competitive advantage, first-mover advantage, sustainable advantage. Yet the historical record suggests something more sobering: advantages erode.
Technologies decay.
Markets reorganize.
Institutions mutate.
From a Darwinian perspective, survival is not secured by dominance, but by adaptation. From a thermodynamic perspective, systems naturally move toward entropy unless energy is continuously applied. From a game-theoretic perspective, equilibrium is rarely permanent. If we accept that business operates in a dynamic, non-equilibrium environment, then perhaps the central strategic task is not to win — but to remain viable.
This article proposes a structured model for what I call strategic continuity: the capacity of an organization to survive the death of its own services while remaining coherent in identity and direction.
The Infinite Game as Structural Premise
The distinction between finite and infinite games, articulated in philosophy and later applied to organizational theory, offers a useful lens.
• A finite game has known players, fixed rules, and a defined endpoint.
• An infinite game has shifting participants, evolving rules, and no terminal victory condition.
Most individual services are finite games. They launch, compete, generate returns, and eventually decline. But the organization itself participates in an infinite game. There is no final win-state. There is only continued participation or exit.
Game theory formalized by John Nash demonstrates that equilibrium conditions depend on player expectations and strategic interaction. In real markets, players continuously enter and exit, altering the payoff matrix. Stability is provisional.
From evolutionary biology to dynamic systems theory, the pattern repeats: systems that survive are those capable of adaptation under changing constraints. The question therefore becomes:
How do we design organizations structurally capable of continuity in an infinite landscape?
The Infinite Service Continuity Model
To answer this, I propose a layered model. Imagine the organization as a structured system composed of four concentric layers.
- Infinite Vision (Core Identity)
At the center lies Infinite Vision: purpose, identity lock points, moral anchors, and sustainability commitments. In philosophy, identity persists through change if core properties remain stable. In constitutional law, enduring principles anchor evolving interpretation. In finance, long-term value presumes consistent underlying thesis. Vision must survive product cycles. If a company’s identity collapses when a service fails, the service was mistaken for the organization. - Endless Strategy (Governance and Continuity Logic)
Surrounding the core is Endless Strategy: governance structures, cultural norms, legal compliance, and value systems. Ronald Coase demonstrated that firms exist to reduce transaction costs within markets. Governance, therefore, is not administrative overhead; it is structural logic. In legal theory, durable institutions balance flexibility with rule-based constraint. Endless Strategy defines how the organization moves without dissolving its identity. It is the stabilizing field around the core. - The Time Spectrum (Strategic Awareness Across Horizons)
Strategy is often collapsed into annual plans. Yet time itself is multi-layered.
The Time Spectrum includes:
• Reflective – Institutional memory and post-mortem learning
• Actual – Present operations
• Focus – Immediate directional prioritization
• Tomorrow – Forecast based on available evidence
• Future – Scenario exploration beyond current models
• Endless – Long-arc existential positioning
Physics reminds us that systems are path-dependent. Economics shows that expectations shape behavior. Psychology, particularly Daniel Kahneman’s work on cognitive bias, demonstrates that short-term focus often overrides long-term reasoning.
The Time Spectrum corrects for temporal blindness by institutionalizing multiple horizons simultaneously. - Finite Services (Orbiting Activations)
Beyond the strategic ring are finite services. Each service is an activation:
• It has a birth.
• It generates value.
• It accumulates entropy.
• It eventually declines.
Joseph Schumpeter’s concept of creative destruction captures this dynamic: innovation dismantles previous structures. In finance, portfolio theory (Markowitz) spreads risk precisely because individual assets are volatile. Services are not identity. They are portfolio elements. An organization that understands this can allow services to die without existential panic. - The Ecosystem Layer (Experience and Data)
Surrounding everything is the ecosystem:
People.
Processes.
Market structures.
Regulation.
Technology.
Data.
Claude Shannon’s information theory reminds us that data is not meaning, it is signal. Meaning arises through interpretation. Data precedes technology. It may exist as archival documents, lived experience, or mathematical models. Technology, including artificial intelligence, is a retrieval and transformation mechanism.
Within this model, AI systems – including systems that simulate “personalities” – function as advanced tools for engaging with data. They are not identities. They are structured interfaces for navigating complexity, enabling interdisciplinary teams to interrogate archives, simulate scenarios, and detect patterns at scale. Used correctly, AI becomes a cognitive amplifier within the ecosystem layer. Used incorrectly, it becomes a distraction from structural design.
Finite Innovation Inside Infinite Structure
Continuity requires renewal. High-velocity innovation frameworks for rapid convergence and scalable outcomes operationalizes innovation as a finite, repeatable cycle within the infinite structure. Small interdisciplinary teams pursue ideas in parallel. Psychological ownership is given and preserved to ensure process momentum. Convergence is structured. Key concepts advance to scalable testing; others remain archived.
This resembles scientific hypothesis testing: multiple models compete, one survives provisional validation, but none claim final truth. Thomas Kuhn’s work on paradigm shifts reminds us that even dominant frameworks eventually yield. Innovation methods that allows organizations to generate new service orbits continuously, at low cost and high velocity, without destabilizing the core are critical to keep evolving and adapting into an ever changing business ecosystem.
Cross-Disciplinary Foundations
This model can draw implicitly from multiple enduring fields such as:
• Natural Sciences: Evolutionary adaptation and entropy.
• Mathematics: Game theory and equilibrium instability.
• Psychology: Bounded rationality and cognitive bias.
• Finance: Portfolio diversification and risk distribution.
• Law: Institutional continuity through rule structures.
• Philosophy: Identity persistence through structured change.
• Marketing: Brand coherence amid tactical variability.
None of these fields alone explain continuity. Together, they converge on one insight:
Stable systems are those that separate core identity from adaptive components.
Who This Framework Serves
This architecture is not optimized for early-stage startups struggling for viability. In the earliest phase, focus must dominate. The Infinite Service Continuity Model becomes relevant when an organization already occupies space in the landscape – when it is already playing the game.
It is particularly relevant for:
• Established technology firms navigating platform transitions
• Media institutions adapting to digital ecosystems
• Public agencies balancing regulation and innovation
• Mature organizations managing multiple service portfolios
These entities face a different problem than survival through growth. They face survival through transformation.
Beyond Advantage
Competitive advantage is a finite concept. Strategic continuity is infinite.
Advantage may secure temporary dominance. Continuity secures participation across generations of services, technologies, and market structures. In the infinite business landscape, the objective is not to defeat competitors permanently. It is to design an organization capable of replacing itself, repeatedly, without losing itself. The only true failure is structural inability to adapt. And that, unlike market fluctuations, is a choice.
What Is an AI Personality, Really?
As artificial intelligence systems become more conversational, persistent, and agentic, the question of personality moves from metaphor to design concern. The term is increasingly used in product descriptions, research papers, and public discourse, yet it often lacks precision. Sometimes personality refers to tone or style. Sometimes to friendliness or empathy. Sometimes it simply describes the feeling that a system behaves consistently enough that users stop noticing its variability.
However, once an AI system operates across time, remembers context, and participates in decisions, personality can no longer be treated as a surface-level attribute. It becomes a structural property of the system.
To understand this shift, it is useful to disentangle several concepts that are often conflated.
First, there is agent identity. Identity refers to role, mandate, and responsibility. It answers questions such as what the system is meant to do, on whose behalf it acts, and within which boundaries. In philosophical terms, identity is tied to continuity and responsibility rather than expression. John Locke’s discussion of personal identity, for example, places continuity of consciousness and memory at the center of what makes an entity the same over time. While AI does not possess consciousness, users still evaluate it through similar lenses of continuity and coherence.
Second, there is the notion of a character archetype. This concept originates in narrative theory and psychology, from Aristotle’s Poetics to Carl Jung’s archetypes. Archetypes are not personalities in themselves, but recognizable patterns of motivation and role. An AI may behave like an advisor, a facilitator, a critic, or an analyst. These archetypes help users quickly orient themselves in interaction, much like narrative characters do, but they do not yet define how the system behaves under changing conditions.
This is where a third concept becomes essential: the behavioural signature. A behavioural signature describes the stable patterns in how a system responds across contexts. It includes how cautious or assertive the system is, how it handles uncertainty, how it responds to disagreement, and how it balances exploration against conservatism. In psychology, this maps closely to dispositional traits rather than situational behaviour. Personality psychology has long emphasized that traits are not single actions, but tendencies that manifest across situations, a principle formalized in trait theories such as the Five Factor Model.
Recent research suggests that this analogy is not merely conceptual. Large language models already exhibit measurable and relatively stable personality-like patterns in their outputs, even without explicit personality conditioning. A study by Serapio-García et al. demonstrates that language models can be assessed using adapted psychometric instruments, revealing consistent behavioural tendencies across prompts and contexts.
https://arxiv.org/abs/2307.00184
Related work has shown that these tendencies can influence how users perceive trustworthiness, competence, and intent. In other words, personality is already present as an emergent property. The design choice is not whether AI systems have personality, but whether that personality is intentional, inspectable, and governed.
This aligns with earlier findings in human–computer interaction. Nass and Moon’s seminal work on social responses to computers showed that humans apply social rules and expectations to machines as soon as those machines exhibit even minimal social cues. This phenomenon, often referred to as the Media Equation, explains why users react emotionally and morally to systems they rationally know are not human.
https://doi.org/10.1111/0022-4537.00153
From this perspective, personality is not an optional layer added for engagement. It is an inevitable outcome of language-based interaction combined with memory and goal orientation. What matters is how clearly that personality is defined and constrained.
Using more precise terminology such as Agent Identity Model and Behavioural Signature helps anchor this discussion. These terms shift attention away from anthropomorphism and toward structure. They invite explicit decisions about what should remain stable, what is allowed to adapt, and what must never change. They also make it possible to discuss accountability, governance, and ethics in concrete terms.
Literature and philosophy offer useful parallels here. In narrative theory, a character is defined not by isolated dialogue, but by how actions remain intelligible as circumstances change. A character who behaves inconsistently without explanation is not perceived as complex, but as poorly written. The same applies to AI systems. Flexibility without continuity does not feel adaptive. It feels unreliable and annoying.
As AI systems increasingly move from tools to collaborators, personality becomes a core design concern with implications for trust, safety, and long-term use. Treating it as an emergent side effect leaves organizations reacting to user perception rather than shaping it. Treating it as a designed, named, and governed construct allows for clarity and responsibility.
The central question, then, is not whether AI should have personality. The question is whether designers, organizations, and institutions are prepared to define and take responsibility for the behavioural identities they are already deploying.
When the Job Listing Is Nowhere to Be Found: Why AI Needs Roles That Don’t Yet Have Names
Over the past year, I’ve found myself asking a question that feels increasingly relevant, not just to my own situation, but to how organizations approach artificial intelligence more broadly. How do you get hired when the role you are looking for does not quite exist yet?
After a year of unemployment, the question has sharpened. Is the position I am trying to describe simply unnamed, or am I already considered too old for a role that sits somewhere between established disciplines? But stepping back, this personal uncertainty also reveals something structural. We are building systems that behave more and more like social actors, while still organizing work as if those systems were only tools.
Over the years, my work has lived at the intersection of UX, service design, psychology, and AI supported collaboration. Recently, that intersection has narrowed into a very specific concern: what happens when AI systems begin to display continuity, memory, personality, and role based behavior? At that point, interaction design alone is no longer sufficient. We are no longer just shaping interfaces. We are shaping relationships.
Large language models and agentic systems are increasingly perceived by users as conversational partners, advisors, collaborators, and sometimes even authorities. Research consistently shows that people apply social and psychological expectations to systems that display human like cues. This is not a design flaw. It is a human reflex.
One of the foundational works in this area is Nass and Moon’s research on social responses to computers, which demonstrates that humans instinctively apply social rules to interactive technologies, even when they consciously know they are machines. A foundational research concept showing that humans instinctively apply social behaviours and expectations to computers and interactive technology and very relevant to why AI personality matters:
Machines and Mindlessness: Social Responses to Computers by Clifford Nass and Youngme Moon — Journal of Social Issues (2000)
https://doi.org/10.1111/0022-4537.00153
This article reviews how individuals mindlessly apply social rules and expectations to computers even when they know they are machines.
From a business and organizational perspective, this creates a gap. Companies are deploying AI systems that speak, reason, remember, and adapt, yet responsibility for their behavioral coherence is often fragmented. Engineers optimize performance. Designers shape interactions. Product managers define scope. Legal teams manage risk. But no one is explicitly accountable for the personality, identity, and long term behavioral integrity of the system as experienced by users.
This gap matters.
When an AI system behaves inconsistently, forgets its role, shifts tone unpredictably, or crosses implicit social boundaries, trust erodes quickly. Users disengage, misuse the system, or over trust it in the wrong contexts. In regulated or high stakes environments such as healthcare, public services, finance, or decision support, these failures are not cosmetic. They are strategic risks.
This is where the unnamed role begins to take shape.
Organizations increasingly need people who can think across psychology, system design, ethics, and AI capabilities. People who can define what an AI agent is allowed to remember, how it should behave over time, how its personality is constrained or allowed to evolve, and how this is monitored. In other words, someone responsible for the agent’s identity as a coherent, accountable construct.
This is not about making AI more human for its own sake. It is about making AI predictable, trustworthy, and aligned with human expectations. From a strategic standpoint, this directly affects adoption, brand trust, compliance, and long term value creation.
There are emerging academic signals pointing in the same direction. Research on personality modeling in AI, reinforcement learning from human feedback, and agent alignment increasingly emphasizes stability, transparency, and behavioral consistency over raw capability. For example, a scientific preprint examining how personality-like traits emerge and can be measured in LLM outputs, supporting the idea that personality design in AI is empirically meaningful:
Personality Traits in Large Language Models (Serapio-García et al., 2023)
https://arxiv.org/abs/2307.00184
This study presents methods for assessing and validating personality patterns in LLM behaviour and discusses implications for responsible AI design.
Seen through this lens, the question of job titles becomes secondary. What matters is recognizing the function. Someone needs to own the space between human psychology and machine behavior. Someone needs to ensure that as AI systems become more agentic, they do not become socially incoherent, ethically ambiguous, or strategically misaligned.
This is the work I am trying to describe. It may be called AI experience design, human centered AI strategy, agent behavior design, or something else entirely. The label matters less than the impact.
As AI systems continue to move from tools to collaborators, organizations that invest in this kind of competence early will have a significant advantage. Not because their models are smarter, but because their systems are easier to trust, easier to work with, and easier to integrate into real human contexts.
Sometimes the job listing is nowhere to be found not because the role is unnecessary, but because it has not yet been named. And this seems to be a reality I am facing today in my search for the right position or project to fill my professional endeavors in the year to come.
Updates and work in progress
The reverse position has been live for a couple of weeks, but I am not seeing any effect so far. I have also initiated a series of LinkedIn posts to potentially increase views and impressions, and had some positive results here – but still no direct messages or noteworthy comments. Strategically I still feel this is worth more than sending out multiple applications that get lost in the void of digital filtering, but I am tactically adding another ad this week as well as continuing my topic oriented posts. The idea is that this will slowly help my general visibility and through this be able to reach the right people. I have yet to see a job remotely including this type of work or even tasks touching upon it, so either I have ‘invented’ a position that will never exist or it is yet to be discovered and identified as a valuable concept. It feels very much like walking into the unknown and trying to explore where there is no visible landscape to interact with.
For my extended CV I have added an area for narratives, fiction and media listing books, comics, movies, TV-series and computer games that has helped me discover concepts and ideas that I consider part of my professional foundation. As this extended CV will only be shipped when requested I do not think this part of the CV will be more than a data set that will help an AI pick up on cues and ideas to use when prompted to extract an shorter CV to use for a specific role or project. That said I also feel that it is absolutely something that adds value to this extended CV even if it blurs the borders between what might be considered professional versus personal. The way forward now will be to search for other topics or concepts that would or could add value, depth or increase the overall quality of the document. To be continued…
Turning the tables
So the network has been contacted, LinkedIn updated, job postings tracked and applied to relevant positions. But compared to last time I went exploring the job market the landscape has changed – and not “just like a little bit”. Where I was called into interviews more or less immediately after contacting relevant employers and hired within a month. Now I am having a one year anniversary with my project of finding my next perfect position. That said it has also been a journey where I have covered what it is I want to work with, figuring out what to call myself, what job postings seem to be asking for someone like this. And then we have challenges with CV and online profiling, how to contacting make use of my network and how to apply for a position. Working through all of these topics and issues have just made it perfectly clear that the scene has change and become radically more chaotic and confusing. Trying to navigate all of it has not helped and rather had the opposite effect. So to summarize quickly; I have tried to plan, prepare and reach out. It is obviosuly not working. Hence the title!
So I have some idea as to what I want to work with. And that means I have some ideas as to what such a job posting might look like. But so far I have never seen anything remotely close to this. Which means I should have some idea as to what it might look like. So I took some time to put one together, and right now I am having my friends, mentor and network look through and commenting on it. Because rather than applying for a position I plan to post the job description where I am the perfect candidate and at the same time promoting such a position in my own network. And maybe this will find its way to the right employer out there…
Extended Curriculum Vitae
The world of recruiting and applying to jobs is changing rapidly these days and like everyone else it quickly becomes as situation of “adapt or die”. As part of my experimentation during 2024 and 2025 I developed what I refer to as my extended CV that expands on both my work history as well as my personal life to create a thirty three page long document. This document became my data set for experimenting with AI as well as my main resource for “cherry picking” content for what to include in actual job and project applications as well as generating any type of content related to such activities. In its complete version it quickly becomes a massive inaccessible dataset that has very limited use, but when used and filtered by me to target and adapt positions or projects it quickly became the perfect reference. Either it was for me to find specific data or to have an AI generate a targeted version of it the document contained as much information as possible and it is still getting updated today as this document is very much alive and almost living a life of its own.
These were the main headlines used for the CV:
- Basic information
- Sammendrag
- Education
- Certifications
- Courses
- Experience
- Selected projects
- Personality
- Positions of trust
- Feats
- Participation
- Knowledge of programming/coding
- Knowledge of software
- Knowledge of methodology
- Language
- Favorite quotes
With planned additions for favorite literature, movies, television series, tabletop and computer games.
Gunnar Øyvin Fredrikson 