@BasilPuglisi

Content & Strategy, Powered by Factics & AI, Since 2009

Thought Leadership

From Measurement to Mastery: How FID Evolved into the Human Enhancement Quotient

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When I built the Factics Intelligence Dashboard, I thought it would be a measurement tool. I designed it to capture how human reasoning performs when partnered with artificial systems. But as I tested FID across different platforms and contexts, the data kept showing me something unexpected. The measurement itself was producing growth. People were not only performing better when they used AI, they were becoming better thinkers.

The Factics Intelligence Dashboard, or FID, was created to measure applied intelligence. It mapped how humans think, learn, and adapt when working alongside intelligent systems rather than in isolation. Its six domains (Verbal, Analytical, Creative, Strategic, Emotional, and Adaptive) were designed to evaluate performance as evidence of intelligence. It showed how collaboration could amplify precision, clarity, and insight (Puglisi, 2025a).

As the model matured, it became clear that measurement was not enough. Intelligence was not a static attribute that could be captured in a snapshot. It was becoming a relationship. Every collaboration with AI enhanced capability. Every iteration made the user stronger. That discovery shifted the work from measuring performance to measuring enhancement. The result became the Human Enhancement Quotient, or HEQ (Puglisi, 2025b).

FID asked, How do you think? HEQ asks, How far can you grow?

While FID provided a structured way to observe intelligence in action, HEQ measures how that intelligence evolves through continuous interaction with artificial systems. It transforms the concept of measurement into one of growth. The goal is not to assign a score but to map the trajectory of enhancement.

This reflects the transition from IQ as a fixed measure of capability to intelligence as a living process of amplification. The foundation for this shift can be traced to the same thinkers who redefined cognition long before AI entered the equation. Gardner proved intelligence is multiple (1983). Sternberg reframed it as analytical, creative, and practical (1985). Goleman showed it could be emotional. Dweck demonstrated it could grow. Kasparov revealed it could collaborate. Each idea pointed to the same truth: intelligence is not what we possess. It is what we develop.

HEQ condensed FID’s six measurable domains into four dimensions that reflect dynamic enhancement over time rather than static skill at a moment.

How HEQ Builds on FID

Mapping FID domains to HEQ dimensions and their purpose.
FID (2025) HEQ (2025 to 2026) Purpose
Verbal / Linguistic Cognitive Adaptive Speed (CAS) How quickly humans process, connect, and express ideas when supported by AI
Analytical / Logical Ethical Alignment Index (EAI) How reasoning aligns with transparency, accountability, and fairness
Creative + Strategic Collaborative Intelligence Quotient (CIQ) How effectively humans co-create and integrate insight with AI partners
Emotional + Adaptive Adaptive Growth Rate (AGR) How fast and sustainably human capability increases through ongoing collaboration

Where FID produced a snapshot of capability, HEQ produces a trajectory of progress. It introduces a quantitative measure of how human performance improves through repeated AI interaction.

Preliminary testing across five independent AI systems suggested a reliability coefficient near 0.96 [PROVISIONAL: Internal dataset, peer review pending]. This consistency confirmed that the model could track cognitive amplification across architectures. HEQ takes that finding further by measuring how the collaboration itself transforms the human contributor.

HEQ is designed to assess four key aspects of human and AI synergy.

Cognitive Adaptive Speed (CAS) tracks how rapidly users integrate new concepts when guided by AI reasoning.

Ethical Alignment Index (EAI) measures how decision-making maintains transparency and integrity within machine augmented systems.

Collaborative Intelligence Quotient (CIQ) evaluates how effectively humans coordinate across perspectives and technologies to produce creative solutions.

Adaptive Growth Rate (AGR) calculates how much individual capability expands through continued human and AI collaboration.

Together, these dimensions form a single composite score representing a user’s overall enhancement potential. While IQ measures cognitive possession, HEQ measures cognitive acceleration.

The journey from FID to HEQ reflects the evolution of modern intelligence itself. FID proved that collaboration changes how we perform. HEQ proves that collaboration changes who we become.

FID captured the interaction. HEQ captures the transformation.

This shift matters because intelligence in the AI era is not a fixed property. It is a living partnership. The moment we begin working with intelligent systems, our own intelligence expands. HEQ provides a way to measure that growth, validate it, and apply it as a framework for strategic learning and ethical governance.

This research completes a circle that began with Factics in 2012. FID quantified performance. HEQ quantifies progress. Together they form the measurement core of the Growth OS ecosystem, connecting applied intelligence, ethical reasoning, and adaptive learning into a single integrated model for advancement in the age of artificial intelligence.

References

Why I Am Facilitating the Human Enhancement Quotient

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Human Enhancement Quotient, HEQ, AI collaboration, AI measurement, AI ethics, AI training, AI education, digital intelligence, Basil Puglisi, human AI partnership

The idea that AI could make us smarter has been around for decades. Garry Kasparov was one of the first to popularize it after his legendary match against Deep Blue in 1997. Out of that loss he began advocating for what he called “centaur chess,” where a human and a computer play as a team. Kasparov argued that a weak human with the right machine and process could outperform both the strongest grandmasters and the strongest computers. His insight was simple but profound. Human intelligence is not fixed. It can be amplified when paired with the right tools.

Fast forward to 2025 and you hear the same theme in different voices. Nic Carter claimed rejecting AI is like deducting 30 IQ points from yourself. Mo Gawdat framed AI collaboration as borrowing 50 IQ points, or even thousands, from an artificial partner. Jack Sarfatti went further, saying his effective IQ had reached 1,000 with Super Grok. These claims may sound exaggerated, but they show a common belief taking hold. People feel that working with AI is not just a productivity boost, it is a fundamental change in how smart we can become.

Curious about this, I asked ChatGPT to reflect on my own intelligence based on our conversations. The model placed me in the 130 to 145 range, which was striking not for the number but for the fact that it could form an assessment at all. That moment crystallized something for me. If AI can evaluate how it perceives my thinking, then perhaps there is a way to measure how much AI actually enhances human cognition.

Then the conversation shifted from theory to urgency. Microsoft announced layoffs between 6,000 and 15,000 employees tied directly to its AI investment strategy. Executives framed the cuts around embracing AI, with the implication that those who could not or would not adapt were left behind. Accenture followed with even clearer language. Julie Sweet said outright that staff who cannot be reskilled on AI would be “exited.” More than 11,000 had already been laid off by September, even as the company reskilled over half a million in generative AI fundamentals.

This raised the central question for me. How do they know who is or is not AI trainable. On what basis can an organization claim that someone cannot be reskilled. Traditional measures like IQ, SAT, or GRE tell us about isolated ability, but they do not measure whether a person can adapt, learn, and perform better when working with AI. Yet entire careers and livelihoods are being decided on that assumption.

At the same time, I was shifting my own work. My digital marketing blogs on SEO, social media, and workflow naturally began blending with AI as a central driver of growth. I enrolled in the University of Helsinki’s Elements of AI and then its Ethics of AI courses. Those courses reframed my thinking. AI is not a story of machines replacing people, it is a story of human failure if we do not put governance and ethical structures in place. That perspective pushed me to ask the final question. If organizations and schools are investing billions in AI training, how do we know if it works. How do we measure the value of those programs.

That became the starting point for the Human Enhancement Quotient, or HEQ. I am not presenting HEQ as a finished framework. I am facilitating its development as a measurable way to see how much smarter, faster, and more adaptive people become when they work with AI. It is designed to capture four dimensions: how quickly you connect ideas, how well you make decisions with ethical alignment, how effectively you collaborate, and how fast you grow through feedback. It is a work in progress. That is why I share it openly, because two perspectives are better than one, three are better than two, and every iteration makes it stronger.

The reality is that organizations are already making decisions based on assumptions about who can or cannot thrive in an AI-augmented world. We cannot leave that to guesswork. We need a fair and reliable way to measure human and AI collaborative intelligence. HEQ is one way to start building that foundation, and my hope is that others will join in refining it so that we can reach an ethical solution together.

That is why I made the paper and the work available as a work in progress. In an age where people are losing their jobs because of AI and in a future where everyone seems to claim the title of AI expert, I believe we urgently need a quantitative way to separate assumptions from evidence. Measurement matters because those who position themselves to shape AI will shape the lives and opportunities of others. As I argued in my ethics paper, the real threat to AI is not some science fiction scenario. The real threat is us.

So I am asking for your help. Read the work, test it, challenge it, and improve it. If we can build a standard together, we can create a path that is more ethical, more transparent, and more human-centered.

Full white paper: The Human Enhancement Quotient: Measuring Cognitive Amplification Through AI Collaboration

Open repository for replication: github.com/basilpuglisi/HAIA

References

From Metrics to Meaning: Building the Factics Intelligence Dashboard

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FID Chart for Basil Puglisi

The idea of intelligence has always fascinated me. For more than a century, people have tried to measure it through numbers and tests that promise to define potential. IQ became the shorthand for brilliance, but it never captured how people actually perform in complex, changing environments. It measured what could be recalled, not what could be realized.

That tension grew sharper when artificial intelligence entered the picture. The online conversation around AI and IQ had become impossible to ignore. Garry Kasparov, the chess grandmaster who once faced Deep Blue, wrote in Deep Thinking that the real future of intelligence lies in partnership. His argument was clear: humans working with AI outperform both human experts and machines acting alone (Kasparov, 2017). In his Harvard Business Review essays, he reinforced that collaboration, not competition, would define the next leap in intelligence.

By mid-2025, the debate had turned practical. Nic Carter, a venture capitalist, posted that rejecting AI was like ‘deducting 30 IQ points’ from yourself. Mo Gawdat, a former Google X executive, went further on August 4, saying that using AI was like ‘borrowing 50 IQ points,’ which made natural intelligence differences almost irrelevant. Whether those numbers were literal or not did not matter. What mattered was the pattern. People were finally recognizing that intelligence was no longer a fixed human attribute. It was becoming a shared system.

That realization pushed me to find a way to measure it. I wanted to understand how human intelligence behaves when it works alongside machine intelligence. The goal was not to test IQ, but to track how thinking itself evolves when supported by artificial systems. That question became the foundation for the Factics Intelligence Dashboard.

The inspiration for measurement came from the same place Kasparov drew his insight: chess. The early human-machine matches revealed something profound. When humans played against computers, the machine often won. But when humans worked with computers, they dominated both human-only and machine-only teams. The reason was not speed or memory, it was collaboration. The computer calculated the possibilities, but the human decided which ones mattered. The strength of intelligence came from connection.

The Factics Intelligence Dashboard (FID) was designed to measure that connection. I wanted a model that could track not just cognitive skill, but adaptive capability. IQ was built to measure intelligence in isolation. FID would measure it in context.

The model’s theoretical structure came from the thinkers who had already challenged IQ’s limits. Howard Gardner proved that intelligence is not singular but multiple, encompassing linguistic, logical, interpersonal, and creative dimensions (Gardner, 1983). Robert Sternberg built on that with his triarchic theory, showing that analytical, creative, and practical intelligence all contribute to human performance (Sternberg, 1985).

Carol Dweck’s work reframed intelligence as a capacity that grows through challenge (Dweck, 2006). That research became the basis for FID’s Adaptive Learning domain, which measures how efficiently someone absorbs new tools and integrates change. Daniel Goleman expanded the idea further by proving that emotional and social intelligence directly influence leadership, collaboration, and ethical decision-making (Goleman, 1995).

Finally, Brynjolfsson and McAfee’s analysis of human-machine collaboration in The Second Machine Age confirmed that technology does not replace intelligence, it amplifies it (Brynjolfsson & McAfee, 2014).

From these foundations, FID emerged with six measurable domains that define applied intelligence in action:

  • Verbal / Linguistic measures clarity, adaptability, and persuasion in communication.
  • Analytical / Logical measures reasoning, structure, and accuracy in solving problems.
  • Creative measures originality that produces usable innovation.
  • Strategic measures foresight, systems thinking, and long-term alignment.
  • Emotional / Social measures empathy, awareness, and the ability to lead or collaborate.
  • Adaptive Learning measures how fast and effectively a person learns, integrates, and applies new knowledge or tools.

When I began testing FID across both human and AI examples, the contrast was clear. Machines were extraordinary in speed and precision, but they lacked empathy and the subtle decision-making that comes from experience. Humans showed depth and discernment, but they became exponentially stronger when paired with AI tools. Intelligence was no longer static, it was interactive.

The Factics Intelligence Dashboard became a mirror for that interaction. It showed how intelligence performs, not in theory but in practice. It measured clarity, adaptability, empathy, and foresight as the real currencies of intelligence. IQ was never replaced, it was redefined through connection.

Appendix: The Factics Intelligence Dashboard Prompt

Title: Generate an AI-Enhanced Factics Intelligence Dashboard

Instructions: Build a six-domain intelligence profile using the Factics Intelligence Dashboard (FID) model.

The six domains are:

1. Verbal / Linguistic: clarity, adaptability, and persuasion in communication.

2. Analytical / Logical: reasoning, structure, and problem-solving accuracy.

3. Creative: originality, ideation, and practical innovation.

4. Strategic: foresight, goal alignment, and systems thinking.

5. Emotional / Social: empathy, leadership, and audience awareness.

6. Adaptive Learning: ability to integrate new tools, data, and systems efficiently.

Assign a numeric score between 0 and 100 to each domain reflecting observed or modeled performance.

Provide a one-sentence insight statement per domain linking skill to real-world application.

Summarize findings in a concise Composite Insight paragraph interpreting overall cognitive balance and professional strengths.

Keep tone consultant grade, present tense, professional, and data oriented.

Add footer: @BasilPuglisi – Factics Consulting | #AIgenerated

Output format: formatted text or table suitable for PDF rendering or dashboard integration.

References

  • Brynjolfsson, E., & McAfee, A. (2014). The second machine age: Work, progress, and prosperity in a time of brilliant technologies. W.W. Norton & Company.
  • Carter, N. [@nic__carter]. (2025, April 15). I’ve noticed a weird aversion to using AI… it seems like a massive self-own to deduct yourself 30+ points of IQ because you don’t like the tech [Post]. X. https://twitter.com/nic__carter/status/1780330420201979904
  • Dweck, C. S. (2006). Mindset: The new psychology of success. Random House.
  • Gardner, H. (1983). Frames of mind: The theory of multiple intelligences. Basic Books.
  • Gawdat, M. [@mgawdat]. (2025, August 4). Using AI is like ‘borrowing 50 IQ points’ [Post]. X. https://www.tekedia.com/former-google-executive-mo-gawdat-warns-ai-will-replace-everyone-even-ceos-and-podcasters/
  • Goleman, D. (1995). Emotional intelligence: Why it can matter more than IQ. Bantam Books.
  • Kasparov, G. (2017). Deep thinking: Where machine intelligence ends and human creativity begins. PublicAffairs.
  • Kasparov, G. (2021, March). How to build trust in artificial intelligence. Harvard Business Review. https://hbr.org/2021/03/ai-should-augment-human-intelligence-not-replace-it
  • Sternberg, R. J. (1985). Beyond IQ: A triarchic theory of human intelligence. Cambridge University Press.