Professional | Gunnar Øyvin Fredrikson

Feb

The Red Ball Method

A high-velocity innovation framework for parallel exploration, disciplined convergence, and scalable delivery

Author: Gunnar Øyvin Jystad Fredrikson
Version: Draft for professional and academic review

Abstract

The Red Ball Method is a structured innovation methodology designed for high-speed, interdisciplinary teams operating under uncertainty. It combines parallel exploration (each participant pursues a distinct concept) with a deliberate convergence mechanism (the team selects one concept to scale), while preserving psychological safety and individual ownership. This white paper formalizes the method, provides a theoretical grounding in established research on team effectiveness, psychological safety, divergence–convergence processes, sprint-based experimentation, and parallel prototyping, and proposes evaluation criteria suitable for both practitioners and academic scrutiny.

Executive Summary

Many innovation initiatives stall because they either (a) broaden indefinitely without convergence, or (b) converge too early and kill promising options before they have been explored. The Red Ball Method addresses this tension by separating exploration and convergence into explicit phases with a small interdisciplinary team (≤7 including the process leader), rapid parallel work, and a receiver-driven governance gate.

The method is particularly suited to:
• new product or service concepts in uncertain markets
• migration strategies (sunsetting products and moving users)
• business model discovery and reframing
• rapid concept validation and early-stage delivery

The result is a scalable deliverable or a portfolio of well-articulated options, supported by retained learning assets and traceable decision logic.

1. Why “Red Ball” and why a ball?

1.1 The Red Ball as an attention mechanism

The method uses a tangible metaphor to create shared focus and individual ownership at the same time.

Red signals salience: it is the attention color—easy to spot, hard to ignore, and cognitively “sticky.” In practice, it represents the idea that each participant selects something that feels meaningful enough to hold attention through uncertainty and criticism.

Ball signals motion, transfer, and momentum: a ball can be carried, passed, dropped, picked up again, refined, and made bigger. That maps directly to the method’s behaviour: ideas move between team members, evolve through exploration, and survive even when they are not selected as the “one ball.”

In other words, the Red Ball is not a decoration. It is a deliberate coordination artefact that supports speed, autonomy, and convergence.

1.2 Why not “persona”, “concept”, or “prototype”?

“Concept” is abstract and tends to flatten ownership. “Prototype” implies a specific artefact. “Red Ball” is deliberately playful but precise—it invites exploration while establishing an expectation of movement and eventual convergence.

2. Conceptual and research foundations

The Red Ball Method does not replace established methodologies; it recombines compatible mechanisms into a distinct process logic.

2.1 Psychological safety as a prerequisite for speed

Team learning and candid exploration require an environment where interpersonal risk-taking is safe. Psychological safety is consistently linked to learning behaviour, error reporting, and adaptive performance in teams (Edmondson, 1999).

2.2 Small-team effectiveness and coordination overhead

Keeping teams small reduces coordination cost and increases speed. This aligns with widely used agile guidance that teams should remain small enough to be nimble (e.g., Scrum Guide: “typically 10 or fewer”), and with broader team-effectiveness research emphasizing enabling structures and clear direction (Schwaber & Sutherland, 2020; Hackman, 2002).

2.3 Divergence and convergence as the core innovation rhythm

The method follows an explicit divergence–convergence rhythm comparable to the Double Diamond model: explore broadly, then focus and commit (Design Council, Framework for Innovation).

2.4 Parallel prototyping to avoid fixation and increase quality

Parallel exploration reduces “design fixation” and increases breadth and quality of outcomes. Empirical research in HCI shows parallel prototyping yields better results and greater divergence than iterating a single early option (Dow et al., 2010).

2.5 Validated learning and minimum viable delivery

The build–measure–learn loop and MVP logic support rapid learning under uncertainty (Ries, 2011). The method’s “minimum value solution” phase is an execution-oriented counterpart to the exploration stage.

2.6 Sprint logic as time-bound acceleration

Timeboxing and structured facilitation enable fast decisions and prototyping, consistent with sprint-based problem solving (Knapp et al., 2016).

3. The Red Ball Method: process overview

3.1 Design objectives

The method is designed to:

maximize idea throughput without early elimination
preserve individual ownership while enabling group alignment
convert exploration into a scalable deliverable or an actionable portfolio
retain learning assets to reduce waste

3.2 Roles

Process leader (facilitator):
designs the cadence, protects psychological safety, manages convergence.
Team members (≤6):
pursue one “ball” each, critique constructively, and contribute to convergence.
Receiver (decision owner):
provides context, constraints, and makes continuation decisions when convergence is ambiguous.
Builders (optional, later):
execution-focused contributors who transform the selected ball into a testable deliverable.

4. The eight-step method (formal specification)

Step 1: Recruit and compose the team

Requirement: interdisciplinary expertise and diversity of perspectives.
Constraint: maximum 7 participants including the facilitator.
Rationale: reduce coordination overhead while maintaining cognitive diversity.

Step 2: Introductions and team contract

Goal: establish psychological foundations and norms.

Operating principles:

participation is voluntary
respect and open-mindedness are non-negotiable
neurodiversity is encouraged and supported through structured autonomy

Output: lightweight “team contract” (norms, communication, decision rules).

Step 3: Explore and anchor the problem space

Even vague inputs must be anchored to:

a future owner (receiver)
a strategic objective
a boundary condition (time, market, constraints)

Output: a one-page problem frame: context, desired direction, constraints.

Step 4: Rapid parallel ideation

Run a short ideation session to generate options.
Rule: each member chooses one idea they want to pursue—no early idea killing.
Output: each member declares their initial Red Ball in one sentence.

Step 5: Run with the ball (parallel exploration)

Each participant explores their ball independently.

Allowed moves:

deepen your own ball
swap balls
challenge someone else’s ball by exploring weaknesses or new angles
replace your ball if it becomes unpromising

Key rule: no ball can be blocked, removed or cancelled – challenging a ball involves adding to it not removing.
Expected artefacts: sketches, value hypotheses, risks, minimal prototypes, narrative framing, quick tests.

Step 6: One ball to rule them all (disciplined convergence)

The group converges on one ball to scale.
Key rule: no ball is “lost.” All explored balls become retained assets.
If no convergence: each member presents their preferred ball; the receiver decides what continues.

Step 7: Keep the ball rolling (handoff and continuity)

The chosen ball moves into delivery.
Rule: continuation is voluntary; the team can be refilled.
Output: a continuation plan: who continues, what must be built, what will be tested.

Step 8: Expand and build (minimum value solution)

The core team strengthens:

value proposition
target users / customers
success metrics
key assumptions and risks

Builders create a testable, measurable artefact.
Decision gate: continue scaling, pivot, or stop based on evidence.

5. Governance and evaluation

5.1 Convergence criteria (recommended)

To reduce politics and increase repeatability, use explicit criteria:

strategic alignment
customer value potential
feasibility and scalability
time-to-test
risk and dependency profile

5.2 Evidence package (what the receiver receives)

chosen ball: narrative + value hypothesis
alternatives considered (retained balls)
key risks and assumptions
proposed test plan and success metrics

5.3 Measurement and learning

Suggested metrics:

cycle time from framing to test
number of parallel options explored
degree of convergence (time and clarity)
quality of evidence at decision gates
downstream adoption or scaling success

6. Strengths, limitations, and academic critique

6.1 Strengths

Parallel exploration reduces fixation and increases the probability of identifying higher-quality options (aligns with evidence from parallel prototyping research).
Ownership is protected, which can increase motivation and persistence.
Convergence is explicit and disciplined, reducing endless divergence.
Retained assets reduce waste by building organizational “innovation memory.”
Adaptive team architecture separates exploration from execution by design.

6.2 Limitations and failure modes

Facilitation dependency: weak facilitation can lead to dominance effects, fragmentation, or stalled convergence.
Attachment risk: ownership can become identity; convergence can become political.
Context drift: without anchoring, parallel exploration may diverge too far.
Regulated contexts: where compliance gates must precede exploration, adaptations are required.

6.3 Mitigation strategies

formal convergence criteria
explicit team contract and timeboxing
receiver involvement early (context and constraints)
structured critique formats (e.g., “steelman” + risk mapping)

7. Comparison with adjacent methodologies

7.1 Design Thinking

Design Thinking excels at empathy and structured exploration, but can become ceremonial and slow in high-velocity contexts. The Red Ball Method compresses cycles and increases ownership while maintaining a divergence–convergence rhythm.

7.2 Lean Startup

Lean Startup is strong when hypotheses are identifiable. The Red Ball Method is stronger earlier, when hypotheses are not yet clear and exploration must remain broad.

7.3 Agile / Scrum

Agile is optimized for delivery once a concept exists. The Red Ball Method sits upstream, generating and converging concepts that are ready for agile execution.

7.4 Design Sprints

Sprints are powerful timeboxed sequences for prototyping and testing. The Red Ball Method is more flexible in how exploration is distributed, and adds a distinct convergence and asset-retention mechanism.

Publishing posture: what to validate next

To strengthen publishability, the next step is to operationalize the method for empirical study.

Suggested study designs:

comparative case studies across innovation programs
controlled team experiments (parallel vs serial exploration)
longitudinal studies of retained-asset reuse

References (starter set)

Edmondson, A. C. (1999). Psychological Safety and Learning Behavior in Work Teams. Administrative Science Quarterly, 44(2), 350–383. https://doi.org/10.2307/2666999
Hackman, J. R. (2002). Leading Teams: Setting the Stage for Great Performances. Harvard Business Review Press.
Design Council. Framework for Innovation (Double Diamond). https://www.designcouncil.org.uk/our-resources/framework-for-innovation/
Dow, S. P., Glassco, A., Kass, J., Schwarz, M., Schwartz, D. L., & Klemmer, S. R. (2010). Parallel Prototyping Leads to Better Design Results, More Divergence, and Increased Self-Efficacy. ACM. https://doi.org/10.1145/1879831.1879836
Schwaber, K., & Sutherland, J. (2020). The Scrum Guide. https://scrumguides.org/scrum-guide.html
Ries, E. (2011). The Lean Startup. Crown Business. (MVP definition and validated learning)
Knapp, J., Zeratsky, J., & Kowitz, B. (2016). Sprint: How to Solve Big Problems and Test New Ideas in Just Five Days. Simon & Schuster.

Feb

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.

Jan

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.

Jan

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.

Dec

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…

Oct

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…

Oct

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.