Two kicks drive real growth: one to the head(Triumph), one to the butt(Setbacks).

The human brain has always fascinated me. Everyone has roughly the same biological matter, less than 1.5kg of it, and yet some people have done things with it that shifted the course of history. That gap between the same material and wildly different outputs is what I keep coming back to.

I recently finished reading “The Biology of Belief”. It’s part of a longer effort to understand the human mind more deeply, not just intuitively but scientifically. A central argument in the book is that the environment shapes identity, not as a soft idea, but as a biological reality.

“I believe that cells teach us not only about the mechanisms of life, but also teach us how to live rich, full lives”- Bruce H Lipton (Author: The Biology of Belief)

The author uses cell biologist Irv Konigsberg’s work to make the point concrete. When Konigsberg was studying cellular growth in his lab, some cells refused to thrive in certain conditions. When things didn’t work out in his favour, Konigsberg didn’t blame the cells. He looked at what surrounded them, changed the fluid, and watched them recover. The cells were fine. The conditions weren’t. That’s the whole argument in one experiment.

I believe this plays out in the same way in people, too. The same person can be sharp, creative, and full of ideas in one environment, and completely flat in another. That’s not about talent or potential. It’s about what surrounds them.

The question worth asking now is what makes an environment one where we find our best and actually thrive.

The Biology of Growth & the Dichotomy.

Every force has a counter. Every high has a low. Nature doesn’t balance by accident. It balances by design, and it applies to growth just as much as anything else. The book makes a fascinating point. Every healthy cell chooses between two states: growth or protection; neither one is designed to dominate permanently. The body is always tilting between the two, a cellular seesaw that keeps life in balance.

Think of your body as a factory with two completely different operating modes: the first one for building, and the second for survival.

Building mode is the body at its best, hunky dory in the truest sense. Cells repair, digestion flows, and the immune system holds the line. The body is calm, and every bit of energy goes toward building something better.

In survival mode, the brain fires a signal to all 50 trillion cells: stop building, start defending. Energy shifts away from digestion and immunity. Sugar floods the bloodstream, heart rate spikes, lungs open up. The body prepares for the extreme situation of survival.

Cortisol shoots up during survival mode; it doesn’t just slow things down; it actively blocks insulin and suppresses Human Growth Hormone (HGH), which is the direct manager of growth. The rebuild can’t happen under stress. But calm the system, cortisol drops, and the growth hormones take over, delivering nutrients and restoring what was lost.

If it stays in protection(emergency) mode, eventually the system starts to break down. But staying in growth mode without a break is just as dangerous. But here’s the other side of the coin: staying locked in growth mode is just as harmful. The body needs both, in balance.

When growth hormones keep pushing cells to divide with no off switch, mutated cells pile up, and tumours begin to form. It can also shut down the body’s internal cleanup process, letting toxic waste build up and accelerate diseases like Alzheimer’s. Meanwhile, organs grow beyond their limits, the heart loses its ability to pump, and constant insulin activity burns out the metabolic system.

Without rhis cellular harmony, the body cannot sustain itself. Protection without growth leads to decay. Growth without protection leads to collapse.

Darwin’s argument puts it plain and direct: struggle is not incidental to nature; it is what drives it. His The Origin of Species frames evolution as a product of war, famine, and death. The universe treats balance as a condition for survival, not a preference. Defy it, and things break down. That pattern holds in human life and growth as well. The highs and lows we all move through aren’t random. They reflect the same two forces playing out at a bigger scale.

It is a universal law. Newton’s Third Law sets it into motion. Karma centres an entire philosophy around it. Atoms are stable only when their charges are equal. Chemical equations that don’t balance simply cannot exist.

Growth in life and career follows the same logic as biology, and the author is deliberate about this. It is not a metaphor. Human beings are multicellular organisms, and because of that, we inherit the same fundamental behavioural patterns as our cells. The parallel runs deeper than language. It is written into our biology.

Evolution/ transformation is rarely a straight line, and it is rarely comfortable. It is an ongoing back and forth between success and setbacks, and the timing of each shapes you just as much as the experience does.

To say it plainly: real growth needs a kick in the head (Triumph) and a kick in the butt(Setbacks). One pushes you forward. The other keeps you grounded. You cannot have one without the other and expect to go anywhere worth going.

Conclusion

It would be easy to walk away from this, thinking we are passengers, that hormones and neurotransmitters are behind the wheel, and we have no say. The author argues the opposite, and it is the beating heart of the book. Belief shapes biology. We can intervene at the cellular level. We can reverse engineer our own lives.

Setbacks are not optional. Nature delivers them regardless of preference. But the response is ours. Cells do not collapse under threat. They read the situation, select a behavioural response, and adapt. That is the model.

When a setback hits, the instruction is straightforward: Panic will come. The question is whether you let it define the moment. You are more than your worst response to pressure. Rising does not require certainty. It just requires movement, even if that movement starts as performance before it becomes real.

Growth can be manufactured before it is felt. Set the conditions, go through the motions, and let the biology catch up. The setback is rarely the problem. What breaks people is losing the rhythm for so long that they stop believing it was ever theirs. There is no comfortable alternative. There is only the choice to move.

Stand up. Respond. Rebuild.

P.S.

The core principle running through this book, that growth requires two opposing but complementary forces, does not stop at biology. It shows up in how teams think. Drawing on Dr Daniel Kahneman’s (1934–2024) work on the duality of human thinking, I have written about the hunter and the farmer as two essential mindsets for building a winning team. Read the article here. It’s the same concept, just seen from a different angle. This time through the lens of exploration and optimisation.

The Science Behind Growth and Progress. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

A tool for teams to understand their talent density across Problem-Solution-Execution spaces in the product development flow.

The context

The hierarchy of professional growth is often misunderstood. While many focus on collecting specific skills, the real engine of long-term success lies in the relationship between Competency, Skills, and Mindset.

To understand how these three work together, think of a high-performance vehicle:

• Competency (The Engine): This is your core capacity, the innate or deeply developed ability to think critically, solve problems, or empathise with others. Competencies like “Systems Thinking” or “Strategic Intuition” are foundational. They compound over time, meaning the better your engine, the more power you have to tackle any terrain.
• Skills (The Tyres): These are the specific tools you use to apply your power to the road (e.g., coding in Python, using Figma, or data analysis). Skills are context-dependent; they wear out or need to be replaced as the “road” (the industry) changes. If you have a powerful engine, swapping tyres is a manageable task. But the best tyres in the world won’t move a car with a broken engine.
• Mindset (The Driver): This is the outcome multiplier. A driver who is fearful or stagnant will never push the engine to its limit. A driver with a “Growth Mindset” or “Iterative Idealism” chooses the best paths and knows when to accelerate. Mindset dictates how effectively the engine (Competency) and tyres (Skills) are used.

Why Competency > Skills?

Skills are often “perishable.” The software or specific methodology you use today might be obsolete in five years. However, the Competency to understand human behaviour or structural logic remains relevant forever. When you possess the right competencies, you can learn new skills rapidly because you already understand the underlying principles where those skills “shine.”

The Multiplier Effect

Without the right mindset, even the most talented individual hits a ceiling. Mindset acts as a coefficient:

• High Skill + Wrong Mindset = Linear Growth (You do what you’re told, but rarely innovate).
• High Competency + Right Mindset = Exponential Growth (You constantly reshape your skills to fit the context).

In short, don’t just build a toolkit of skills. Build a powerful engine of competency and cultivate a mindset that knows how to drive it. Skills will naturally follow the demands of the moment, but your core capacity is what creates lasting impact.

Philosophy

Based on the concept of ‘talent density’ popularised largely by Netflix. It refers to the ratio of high-performing, collaborative, and self-motivated individuals within a team. Instead of focusing on sheer headcount, a team with high talent density focuses on the concentration of excellence within the team

Core Mechanics

The theory suggests that talent is not just additive; it is multiplicative. When you have a high concentration of top-tier talent, several things happen:

• The “Talent Magnet” Effect: High performers generally want to work with other high performers. This makes recruiting easier and retention higher for your best people.
• Reduced Oversight: High-density teams require less management and fewer bureaucratic “guardrails.” Because the team is composed of responsible, skilled individuals, you can lead with context rather than control.
• Faster Innovation: Smarter, more efficient people solve complex problems faster and with fewer resources than a large group of average performers.

Why Average Performance Can Be “Toxic”

A key pillar of this philosophy is the idea that “adequate performance infects a team.” In a low-density environment:

1. Managers spend 80% of their time managing the bottom 20% of performers.
2. High performers get frustrated because they have to pick up the slack or repeatedly explain simple concepts.
3. The quality of the “vibe” or collective output drops to the lowest common denominator.

The Trade-off

While talent density leads to high speed and innovation, it can also create a high-pressure environment. It shifts the culture from a “family” (where you are safe regardless of performance) to a “professional sports team” (where you are supported and cared for, but you must maintain your position through performance).

Why Deejay-Graph

Most product and design teams operate on vibes. Someone thinks the team is strong on execution. Someone else thinks the real gap is in problem framing. Nobody has a shared map. So friction builds quietly, in reviews, in missed briefs, in recurring disagreements that never quite get named.

Deejay-Graph is a free, browser-based tool built to fix that.

What Is It?

Deejay-Graph is a six-axis spider chart; a radar visualisation tool designed specifically for product and design teams. Created by Dhaneesh Jameson and available at DeejayGraph, it lets team members individually plot their self-assessed scores across six core parameters, then overlays everyone’s results into a single merged view.

Understanding the 2 Mindset model used here

• Hunter: proactive, opportunity-seeking, growth-oriented
• Farmer: nurturing, systematic, detail-oriented

Through an evolutionary lens, humans developed two opposing modes of thinking: one built for exploration and risk-taking, the other for optimisation and preservation. In theory, these two modes are binary. The brain runs one at a time, not both together. At the system level, they are opposites.

These two distinct modes of cognitive processing were introduced by Nobel laureate psychologist Daniel Kahneman in his landmark book, Thinking, Fast and Slow.

<read more>

4 Core Competencies

• C1, C2, C3, C4: four professional competencies spanning Problem, Solution, and Execution domains.

This is one area often misunderstood by many when I explain its theoretical part. Most people think that I am suggesting a waterfall or a relay model of handoff from one competency to another, as it was in the past.

This is hard to prove. Without a parallel team working on the same problem, you cannot objectively show that the result would have been better. And when people don’t know what’s possible, they accept what they have as good enough.

One person doing everything feels faster, and that can be true if there is no true benchmark of the quality of what is produced. It rarely is, especially when the work spans different competencies.

For example, before Ford, building a car took a small team of generalists the better part of two weeks. Ford flipped that by breaking the work into specialised roles and moving the car down a line from one expert to the next. That shift alone brought production time down from weeks to 90 minutes.

The point isn’t an assembly line. It’s a collaboration. People building together, not passing work down a line, part by part.

Competency-based team building means matching the most critical parts of a project to the person best placed to lead them. Others contribute where needed. There are no hard handoffs.

The goal is internal collaboration and conscious orchestration of the talent pool, which raises the quality of the output.
<read more>

Each axis scores from 0 to 12 across three levels:

• Level 1: Up to 4 units
• Level 2: Up to 8 units
• Level 3: Up to 12 units

The three-level structure forces honest gradation. The resulting shape is irregular, personal, and rarely symmetrical.

Relevance in the AI Era

With AI commoditising execution, a product team’s value is increasingly defined by its foundational human competencies and attitudes, not just tools. The Hunter/Farmer split and the C1 to C4 competency axes are precisely the qualities that resist automation, judgment, framing, relational intelligence, and strategic orientation.

Deejay-Graph doesn’t measure what AI can do for your team. It measures what your team brings and what AI can further amplify. In that sense, it is more relevant now than ever.

The Problem It Solves

Teams are often assessed on outputs. Deejay-Graph shifts the lens to profile what dominant competency people bring, what they naturally gravitate toward, and where collective capability has gaps.

Without a tool like this, two things tend to go wrong:

Misaligned expectations. A team heavy in Hunter energy will sprint toward solutions before the problem is properly defined. A team of Farmers may refine endlessly and ship the wrong feature without validating the solution. Neither knows it until something breaks.

Hidden friction. When a team consistently struggles in the same phase, execution always feels chaotic, or problem definition never lands cleanly, the root cause is often a competency gap nobody has visualised.

Use Cases

New team formation. Before a product team kicks off, map the room. If everyone is a Hunter and nobody scores on Farmer axes, you know you need to deliberately assign someone to own the follow-through.

Hiring decisions. Plot your current team and look at the gaps. The shape of the hole tells you exactly what kind of person to hire for, not just a job title, but a genuine capability need.

1:1s and growth conversations. Managers can use individual profiles as a visual anchor for honest development conversations. “Your C3 score is lower than last quarter. What changed?” becomes a question with something to point at.

Cross-functional alignment. When design, product, and engineering work together, their profiles often reveal why collaboration breaks down. A design team skewed toward execution, working with an engineering team skewed toward solution, is a recipe for recurring scope disagreements. Seeing it removes the blame.

What Makes It Different

Most team assessment tools are locked behind SaaS paywalls, take 20 minutes to onboard, and produce PDFs nobody reads again. Deejay-Graph runs in a browser tab, takes three minutes, and produces an image you can drop straight into a Notion doc or a Slack thread.

It is also deliberately opinionated about what to measure. The Hunter/Farmer mindset split alongside C1–C4 is a specific lens that maps well to how product and design work actually gets done, not a generic 360-review framework repackaged.

No login. No paywall. Unlimited downloads.

The Verdict

If your team has ever had a post-mortem where the root cause was vague, “we weren’t aligned,” “there was no clear ownership”, Deejay-Graph gives you a way to surface those dynamics before they become post-mortem material.

As AI takes over more of the execution layer, the teams that thrive will be the ones who understand their human edge clearly. This tool helps you find it.

Start your team audit →

Deejay-Graph: Orchestrating Your Team’s Unique Strengths. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

Your daily ‘Vibe-Check’ at work

Forget those “This could have been an email” meetings that drain your soul before lunch. Knock-Knock is here to kill the corporate cringe and save your deep-work flow.

Knock-Knock replaces the dreaded, scheduled stand-up with high-energy, <0-second “vibe-checks.” It’s built for the remote-first generation that actually values their time (and their sanity).

Why Knock-Knock?

If your stand-up takes longer than a knock-knock joke, it’s not a stand-up.

Think of it as the social-style pulse check for your work crew. After all, if your stand-up takes longer than a knock-knock joke, it’s not a stand-up. If you’re being dragged into a meeting when you’re in the zone, the joke’s on you.

Knock-Knock: Your 30-second team pulse. No meetings required. Want to try the prototype for Knock-Knock? 😊 Here it is.

Why your team will actually like it:

• RIP Performance Anxiety: No more rehearsing your “What I did yesterday” speech while someone else drones on.
• Context-Switching is Cancelled: Stay in the zone. Drop your update whenever you’re ready, no calendar invites required.
• Vibe-Driven, Not Data-Driven: We aren’t here to replace your Jira board. We’re here to keep the team spirit alive without the dry, bureaucratic fluff.
• Time Zone Agnostic: Because 9:00 AM for you shouldn’t mean 11:00 PM for your teammate.

Bonus: Here’s a digital version of a board game I put together. Note that I didn’t design the original game itself, just brought it to life in a digital format. Check it out here:

A Tiny First Draft: My Initial Experiment with Vibecoding was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

We often treat ‘Art’ and ‘Design’ as interchangeable synonyms for creativity, but I’ve always found that perspective to be a missed opportunity. To me, they are distinct disciplines with fundamentally different objectives. Understanding the friction between the two is essential, especially now, as we introduce a third, more complex variable into the equation: Artificial Intelligence.

While they may share the same tools, their hearts beat for different purposes: one for the creator’s internal truth, and the other for the user’s external need.

AI has introduced a third dimension to the creative process: the Machine. This shift toward human-machine collaboration is no longer a choice; it is a fundamental evolution of how we design.

The most exciting byproduct of this shift isn’t mere efficiency or speed; it is Productive Serendipity, an exponential boost in the creative alternatives at our fingertips.

The “Vibe-Coding” Epiphany

I recently sat down to build a small app for a niche user group. I decided to try something different: I used a “vibe-coding” approach with a Large Language Model. I stepped back from my role as a professional designer and approached the machine as if I knew nothing of grids, hex codes, or logic. I gave it a problem statement and asked it to hand-hold me through the process.

What happened next was the “spark.”

The machine didn’t just follow instructions; it took a creative direction that would have normally taken me three or four manual iterations to reach. In seconds, it put a “final-ish” concept on the table.

But the real magic wasn’t in what the machine did; it was in what it allowed me to do next. Because I didn’t have to spend my mental energy building the foundation, I was immediately free to critique it. I wasn’t exhausted by the labour of creation; I was energised by the power of curation. I found myself looking at the white space and realising it created a separation we didn’t intend. I looked at the “meaning” of two elements placed together, a nuance the machine’s pattern-matching missed, and I began to sculpt.

From Builder to Judge

This is where the designer’s value now lives. AI creates through patterns, but it doesn’t always understand meaning. It can place elements, but it doesn’t feel the “weight” of a layout or the subtle cultural friction of a specific colour or motifs.

We are shifting from being the primary builders to being a co-builder and the ultimate judges. As the machine scales the volume of “what is possible,” the designer’s value scales in judgment and intent. We are no longer sweating every pixel from scratch; we are navigating an infinite garden of options, using our taste and empathy to decide which path leads to a true solution.

Why This is an Opportunity, Not a Threat

Many fear that a “third party” in the creative process will dilute human talent. I believe the opposite. We are moving from a world of directed effort to a world of directed discovery.

The fundamental workflow of design is reshaping itself to accommodate a capacity beyond our own. This isn’t just a new tool; it’s a new partnership. By embracing AI as a co-creator, we are freed from the mechanical to focus on the meaningful.

Leading the Discovery

As designers, our mission is to guide our work toward this “Productive Serendipity.” We must foster an environment where the machine’s suggestion is welcomed as a starting point, not a final answer.

Our job is to ensure that while the machine connects the dots, the human provides the soul. We are the ones who must have the wisdom to know when a machine-generated accident is actually a breakthrough, and the skill to refine it into a masterpiece.

Cheers,
Dhaneesh Jameson

Creative serendipity and AI: The new dimension in the designers’ world. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

In October 2025, in a small gathering of seasoned designers, the host posed a question that lingered in the room longer than introductions did:

“What kind of designer are you?”

It was a simple inquiry, but to those who have spent decades shaping form, space, and meaning, it struck with unexpected depth.

The word “design” originates from the Latin designare, meaning to mark out or plan. Initially referring to symbolic markings or patterns in the Middle Ages, its meaning expanded during the Renaissance and Industrial Revolution to encompass both creative intention and structured planning across art, architecture, engineering, and manufacturing, bridging conceptual vision and practical creation.

The year 2025 marked a boom in generative AI and an abundance of accessible tools; the baseline of what we call “good design” has risen dramatically, yet the ceiling seems oddly flat. Templates, algorithms, and pre-trained systems now produce with astonishing speed what once took days of study and struggle. The barrier to entry has dissolved.

In an age when creating digital design is cheaper, quicker, and easier, the meaning of being a ‘designer ‘is also changing.

But perhaps, this is not a tragedy. Every era of progress has faced its own disruption, and every craftsman of one age eventually gives way to a new one. The meaning of “designer” has always shifted with time: from artisan to stylist, from systems thinker to storyteller.

The question “what kind of designer are you?” feels urgent again, not as a challenge to defend our past, but as an invitation to reimagine our future.

But design has never been static; it has always adapted, absorbing the language of each new age. Fighting this evolution is not wisdom; it is fear. The real task ahead is not to guard what design was, but to elevate what it can become.

The Japanese concept of Takumi, the master craftsman, is a powerful reminder that excellence demands immersion, repetition, and reflection. When Toyota or Sony speaks of Takumi, they speak of artisans who have spent over 60,000 hours perfecting their craft. Design once celebrated such patience; today, we celebrate speed.

Empathy, once the invisible thread of good design, has also changed shape. Once cultivated through lived experience, years of honing material sensitivity, spatial intelligence, and psychological empathy. It is now being reduced to a word we recite rather than practice. Yet, amidst this flux, lies an opportunity: to rediscover the true essence of empathy, not as a process, but as presence, something that no machine can mimic.

The challenge is not technological. It’s cultural.

I do not know if the future of design belongs to unicorn designers, where everyone is expected to know everything. But we must still cultivate a culture that celebrates collaboration over independence, interdependence over designing in isolation. Only then might we witness true masterpieces of modern design creations that fully reflect the depth of our collective capability and the extraordinary potential of our modern times.

And perhaps, when we ask again, “What kind of designer are you?”, I believe, the truest answer will not be about tools or titles, but about how deeply we choose to act, or how consciously we choose to ignore, in service of the agenda we carry inside.

When craft meets convenience: Searching for the meaning of design in the age of generative AI. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

Reflections from Building a Deep Tech Healthcare Startup With No Engineering or Bio-tech Background(2023–25).

SugarStrings.ai is a preventive health startup I cofounded in 2023 alongside two friends with a life sciences background.

We had built a sophisticated algorithm that could analyse genetic data across 6,000+ diseases, producing incredibly complex findings and insights. It was essentially a black box generating dense, technical output. My role was to build a system around it that would make sense to users and, critically, make it worth buying. Because an algorithm alone isn’t a product.

Having worked in startups and product and service design roles, I naturally gravitated toward and led the areas where I could add the most value, which centred around translating complex technology into meaningful human experiences.

In early-stage startups, roles cannot be very rigid; you do what needs to be done. I worked across several fronts:

• Service Design: Mapping the entire user journey — from awareness to purchase to receiving results — ensuring each touchpoint builds confidence and clarity
• Brand Design: Creating a cohesive visual and verbal language that conveyed credibility, approachability, and scientific rigour
• Communication Strategy: Articulating our unique value proposition through stories that made the service desirable, not just understandable

However, I clearly underestimated the complexity of achieving PMF in the preventive healthcare space and the unique GTM challenges of the DNA-based healthcare services in India.

Beyond that, the inherent complexity and opacity of deeptech introduced unforeseen friction in building trust, even among early adopters with higher economic and educational backgrounds.

Trust wasn’t one-dimensional, especially when introducing a new category of products. I strongly believe we need to solve the following for the early adopters, who can then become the champions of our solution.

• Clinical trust: Finding doctors who could serve as proxies for credibility and help with adoption
• Technical trust: Communicating effectively with scientists and technical audiences who needed to understand the algorithmic rigour
• Consumer trust: Speaking to everyday early adopters willing to try something new in a sensitive domain like health

Here is my reflection on the last two years of building SugarStrings.ai

The black box of AI and the challenges in building clinical and technical trust.

As AI and genetics move from behind the scenes into our everyday lives, the main challenge lies in making it understandable to commoners.

This is especially true for all modern deep learning systems in the direct-to-consumer (D2C) space. A vast network of neural layers making millions of micro-decisions, patterns built upon patterns, in ways that even their creators can’t fully trace back, is a reality. For the masses, AI has become a ‘black box’: a system that not necessarily provides explanations without revealing how it arrived at them.

I realised that to build credibility, we had first to win the trust of early adopters; only if those power users could be satisfied, they could serve as proxies for the early majority, before we could reach the laggards. The idea was to find the first 100 early adopters and reach PMF through those learnings.

The PMF stage

Our early adopters came from three distinct groups: those who trusted us, the founders personally, those intrigued by the product’s business potential and wanted to test, and tech-aware individuals, motivated by their family history of health risks.

The third group proved to be the true early adopters, curious, optimistic, and eager to experiment.

A product in the preventive space is often seen as non-urgent, and with a tag of INR 58,000, we were clear about the challenges from the start.

What worked in our favour was the natural urgencies triggered by a health event among friends or family, which created a sense of fear and motivation to act. Which we did not have full control over. That’s where we saw some early signs of product–market fit.

As a product and service designer in such complex scenarios, my role boils down to three timeless challenges of selling any product in the D2C market: explainability, transparency, and interpretability.

1. Explainability is user-facing and non-negotiable. It’s about ensuring comprehension and reliability, not overwhelming users with information. People seek just enough understanding to trust that the product can help them achieve positive outcomes; the technical details can come later.
2. Transparency is process-facing. It’s about giving early adopters a clear view of how the system is built and operates, who made it, what data it uses, and how credible, authentic, and limited it may be.
3. Interpretability is model-facing; it allows experts to examine the technical workings of a model when consumers cannot. In India, where people trust doctors more than scientists, physicians often serve as proxies for credibility, but most lack the tools or training to audit the underlying algorithms. Validating such a product requires deep-tech expertise and a working knowledge of DNA and life sciences, rather than a clinician’s medical degree.

Just telling impact stories is not enough anymore.

We started with the assumption that if we can communicate the value in simple terms, people won’t have friction to pay. The question is not the amount, but the value of what one is willing to pay for.

While that is technically correct, communicating values is not all that black and white. It goes beyond simply providing digestible information, especially today, when customers have easy access to knowledge and are increasingly cautious of scams.

Most early adopters will always dig deeper before they convert.

The inevitable question that sparks a whole chain of others is why it costs more than similar products, and what makes it different. Righteousness alone doesn’t sell healthcare products and services. Here comes the importance of transparency and why it matters for the early adopters.

Unlike traditional products and services, deeptech products are inherently more sophisticated and often, more opaque. For many, even the idea that DNA can reveal so much about future disease risks is mind-bending, let alone explaining the magic of algorithms. And when they find out it all comes from a quick cheek swab, that’s when you hear the inevitable: ‘Wait… what?!’

The initial communication tactics only take them to a level where they say, “I think I understood, but let me come back later after consulting with my doctor before I buy”. What they mean is, they need to understand not just what the risk factor is, but what it means for them, how reliable it is, and, more importantly, what they can do about it.

However scientifically accurate it might sound, it’s also deeply confusing on an emotional level for anyone to learn about a future health risk alarm, which does not have any traces of visible evidence at the moment. Until the people have clarity to actual resolution, they do not want to risk their peace of mind, which is more emotional than practical.

But most doctors, even specialists, may not have the capacity to fully grasp the black box of deeptech or validate a model’s accuracy. And with no formal agency yet to evaluate such technologies, interpretability becomes crucial, not just for credibility, but also in guiding early adopters on their journey

Such challenges could be solved through direct collaborations through centres of excellence in life sciences and technology places like IITs, IISc, and the Biotechnology Industry Research Assistance Council (BIRAC), to name a few. They must start with our own scientific documentation and evidence through research publications, and credible white papers from the internal team, and they complete the communications challenges in the early adoption phase and build trust and credibility for a product like SugarStrings.ai.

Eventually, the early adopters build the early majority, which becomes the proxy for mass adoptions later, where no one would be questioning the credibility anymore.

This is where design & communication becomes not just helpful, but essential when supported with technical nuances, and especially in true medtech inventions, communication cannot be reduced to a mere marketing gimmick, where people are pulled in through false promises, but earn them through ethics and dignity.

Given the complexity of the subject and the opaque nature of the domain and the technology behind it, our work at SugarStrings.ai is far from complete, but we’ve come a long way from where it began, when it was just an algorithm. But there is a long way to go before it is adopted widely by the masses.

Addressing the complexity in MedTech communication and building consumer trust.

1. Make the invisible visible through visual artefacts & mental models. (What)

Good design can turn opacity into clarity with simple, digestible insights first, then enable users to explore deeper if they wish. This must help in the explainability factor of the technology, especially since the product is built for everyone in the D2C market.

One such effort was to use Google Maps’ navigation as a mental model to explain the outcome and potential paths to mitigate many of the health risks.

2. Humanise the System (Why)

We cannot design medical AI as an unquestionable oracle; they have life-altering consequences. Understanding the why also helps to ease the explainability factor of the offering.

It needs to engage people, helping them understand the information in ways that make sense to them, connect meaningfully with their own lives and make the right decisions at the right time. Here is how we introduced a concept called ‘Golden window’.

Tone matters at every service and product touchpoint: Clinical accuracy without empathy creates distance.

3. Credibility and Competence through transparency

People don’t trust what they don’t understand, but they also don’t need every technical detail. They just need to know that your offering is credible and best-in-class.

Relying on proxies of trust is a crucial tactic in this regard, especially when the offering is complex and the average person cannot easily verify its authenticity. In such cases, people trust the names, groups and institutions that can vouch for credibility.

Design plays a vital role here; it can communicate confidence, convey data sources, and signal scientific credibility, all while building competence trust without confusion.

Be transparent about where information comes from, acknowledge uncertainty when it exists, and clearly state what the system can and cannot do. This honesty, paradoxically, builds more trust than false certainty. The credibility of the people behind is surely a good starting point, but it is very important to have external validations when it is a commercial product offering.

4. Give Users Agency

Let them ask why. Let them explore how. Let them disagree. That interactivity, the ability to see how a system reached its decision or what happens if you change an input, makes explainability participatory, not passive. That would be the surest way to build trust by giving them a sense of control.

At Sugarstrings.ai, users could, through the initial online touchpoints, easily navigate to their specific disease type and were openly invited to connect with our team for any clarifications or questions.

6. Design for Emotion, Not Just Cognition

Trust isn’t built through accuracy alone. It’s built through empathy, tone, and aesthetic calm. Although complex, even the right amount of direct communication, if used well, could connect the dots and send the message home.

Colour choices, wording, and even the rhythm of interaction can either amplify anxiety or inspire agency. When someone is learning about their genetic disease risks, the emotional design is as important as the information architecture.

We chose optimistic images and warm colours, avoided alarmist language, and created breathing room in the interface. These weren’t decorative choices; they were trust-building strategies.

From Explanation to Relationship and the Path Forward

Perhaps the real role of design isn’t to make AI fully explainable; that may be impossible for truly complex systems. Instead, design makes the relationship between human and AI understandable.

Design becomes the bridge, turning black boxes into windows of understanding.

As AI systems become more capable, they will inevitably become more complex. Ultimately, it’s not about opening the entire box and exposing every neural weight and activation function. It’s about creating enough transparency that people feel informed, enough control that they feel empowered, and enough empathy that they feel respected.

We might never open the entire black box of AI, but through thoughtful design, we can make people feel that they’re standing in the light, not in the dark.

In a world where AI is making increasingly consequential decisions about our health, opportunities, and lives, that light matters more than ever. Design and explainability hold immense potential in bridging this gap. But cracking the initial shell of early adopters with a new category of products is not just about telling stories; it’s about building upon evidence of impact, generating trust through proxy collaborations, and welcoming the curiosity of early adopters who later become the ultimate proxies for the masses who consume the stories of success to be the believers.

Cheers,
Dhaneesh Jameson

Some additional contexts from building SugarStrings.ai

Traditionally, DNA reports are lengthy, often running into hundreds of pages, and deliberately complex, packed with unnecessary details. This complexity is often used as a tactic to make the report appear extraordinary and justify the price customers pay. But the truth is, it doesn’t need to be that way.

We took a simpler approach by using progressive disclosure, presenting information step by step to reduce cognitive load and make the report more accessible. On the very first page, customers get all the key information they need. The remaining sections are available only if they wish to explore further, or for clinicians to take appropriate action.

At the second level also it is written in simple language for anyone to understand the nature of the risk more descriptively.

At the third level, it is meant for experts or clinicians to explore the details more deeply with more technical details.

The branding decisions were carefully crafted to avoid clichés common in the genetics space and to stay clear of technical complexities, even in naming. The visual identity draws from the idea that genetic diseases are diverse yet share overlapping symptoms and traits that make them hard to distinguish. The structure of sugar molecules inspires the form of the brand identity, while the name reflects DNA’s essence as a string of sugars.

EXTRAS

Initially, it was envisioned as a B2B platform for doctors dealing with the diagnosis of complex and challenging conditions.

When Algorithms Decide: Designing for Trust and Explainability in Healthcare AI. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

The Creativity Crisis and the Template Trap

Holding the title of designer doesn’t necessarily mean one is creative by default. While some may show greater aptitude, creativity can be nurtured in a supportive environment that encourages people to think differently and break patterns; However, I think the rise of new AI tools has blurred this distinction.

Creativity is often mistaken for aesthetic skill. While aesthetics arise from taste and technical mastery, creativity is an intangible, dynamic ability that can influence the output and the outcome. A highly creative individual may lose their sense of creativity when confined to a restrictive or non-stimulating environment.

Today, this distinction matters more than ever as the growing prompt-template culture has led to a new form of design homogeneity. The Template Trap, where algorithmic processes replicate polished yet formulaic results.

AI has democratised execution, the polish, the technical craft, but in doing so, can we really rely on execution as proof of our thinking or creativity? With ‘best prompts’ widely shared online, everyone generates virtually stunning, yet similar outputs because they’re all using the process (read algorithm) behind. The abundance we experience today goes far beyond what we once called abundance, making true rarity even rarer than before the AI era.

AI gives you the statistically probable output. Creativity lives in the improbable. We must ask ourselves: how does the equation change when you come into play between the tool and the result? The answer may define your relevance in the future.

What Actually Differentiates

The designers who will thrive aren’t the ones who are just building prompt libraries. They’re doing the work AI can’t replicate:

The messy pre-AI work: The human science-backed, curiosity-fuelled research that does not shy away from the uncomfortable user conversations. The countless terrible sketches before a good idea emerges. AI can’t notice the tiny pause and non-verbal cues when a user mentions a pain point.

Bringing unusual contexts together: AI learns from patterns in data, not from a bank of true lived experience. A person from another field might spot connections in your domain that even experts within it miss.

Tactics may win moments, but only strategy wins the day: Defining the problem remains a human responsibility. Asking AI to “design a dashboard” may feel like a win when it produces stunning visuals in seconds, but questioning whether a dashboard is even the right solution and finding the truly meaningful one is the real mark of human creativity.

Knowing when to ignore AI’s suggestions: The designer who can articulate “here’s why we should do the opposite of what the AI suggested” is demonstrating genuine creative thinking.

Protecting the Creative Muscle: Designers must develop personal practices of creative resistance to preserve their creative capacity. Much like athletes build strength through resistance training, creativity too thrives when challenged, enriched by insights from disciplines such as anthropology and behavioural economics.

As designers, we must make AI more valuable, not less.

AI has made creativity more valuable while simultaneously making it easier to avoid the hard work of being creative. The long-term winners will use AI as a collaborator, but at the same time, they’ll recognise sophisticated mediocrity when they see it.

Are we taking this in the right direction? The danger is that if we are not, we won’t recognise the difference until it’s too late, until every product looks the same, sounds the same, solves problems the same way. Then they’ll desperately search for creative thinkers and realise they’ve atrophied that muscle by over-relying on templates.

On the optimistic side, I believe AI will push us back to the fundamentals, where the true differentiator is the authenticity of thought and creativity, not the title, not the tool, but the ability to connect the dots that AI cannot.

Cheers,
Dhaneesh Jameson

AI Must Make Designers More Valuable, Not Less. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

Reflections on the Designer’s Role in the Age of AI

In 2025, the design world stands at a historic inflexion point. Artificial Intelligence has not just entered the creative process; it’s redefining it. What once took entire design teams days or even weeks to refine interfaces, elegant mockups, seamless prototypes, AI now produces in minutes. Its efficiency is breathtaking, its precision unmatched. Yet amid this acceleration, one question echoes through every creative space: What becomes of human imagination when machines can design?

The answer lies not in resistance, but in redefinition. The arrival of AI doesn’t mark the end of design; it marks its evolution. As technology takes command of production and pattern recognition, the designer’s role expands from crafting artefacts to crafting intent. The work of the future is not about how fast something can be made, but why it should exist, how it should feel, and what it should mean in the human context.

Beyond Execution: The Depth of Human Intent

The intelligence behind new-age design tools is remarkable, yet far from all-knowing. It thrives within the boundaries of data, brilliant at identifying patterns, yet often blind to what lies beyond them. Cultural nuance, emotional subtext, and ethical complexity still elude algorithmic logic. Machines can mirror human expression, but they cannot interpret it within its lived context.

Here lies the human edge. Designers bring what AI cannot simulate: the ability to make context-aware judgments those rooted in empathy, experience, and the capacity to anticipate edge cases where data runs thin.

At this point, AI can propose infinite solutions, but it takes a human to know which one resonates with a community, a culture, a moment in time.

From Artefacts to Ecosystems: The Holistic Mindset

As AI handles the mechanics, human designers must master the macro view of the interconnected ecosystem of experiences. Great design now extends beyond screens or interfaces; it spans systems, services, and stories. This is the essence of Holistic Design: seeing every touchpoint as part of a continuous, emotionally coherent journey.

AI excels at optimising fragments, but only humans can maintain the narrative thread the vision that binds utility, beauty, and empathy into something meaningful. Companies like Apple and Airbnb exemplify this: leveraging AI’s precision to scale excellence, while grounding every decision in human-led purpose and cultural sensitivity.

Designers in this era are not just makers, they are sensemakers, weaving coherence and care through complexity.

Designing with AI, Not Against It

The true opportunity of this era lies in collaboration. Designers must learn to work with intelligent systems curating data, crafting prompts, and guiding outputs through ethical and contextual filters. The designer’s craft now includes prompt literacy, data ethics, and systems thinking. AI amplifies creativity, but humans anchor it in reality.

Equally important is organisational maturity. For designers to lead with intent, leaders must create environments that value critical reflection over output speed. When leadership understands that great design is not about producing more screens but designing better decisions, human-centred innovation thrives.

The Human Future of Design

Design in the AI era is both demanding and liberating. It calls for resilience, humility, and a renewed sense of purpose. The designer’s role is no longer to outpace the machine, but to humanise its power to infuse each creation with meaning, cultural awareness, and ethical foresight.

In a world where machines can design anything, the true measure of creativity will be knowing what should be designed and why. The human edge isn’t speed or perfection; it’s wisdom to connect the right dots and orchestrate the right outcome, the ability to connect technology to humanity with intent, empathy, and courage.

That is the next frontier of design.

What’s left for human Imagination?

AI can design faster, smoother, and smarter, but can it understand why something should be designed? This AI-generated podcast is built on an initial draft on the power of intent in the age of AI, where human creativity evolves from mere execution to thoughtful orchestration.

Discover why the future of design isn’t about faster or prettier outputs, but about crafting intangible experiences that truly move the needle, building trust, meaning, and connection in unique ways.

Cheers,

Dhaneesh Jameson.

When Machines Design, What’s Left for Humans. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

The startup world holds a magnetic allure, especially for young and ambitious minds, where the promise of innovation and impact inspires thousands to dive in with zeal. But behind the inspiring tales of groundbreaking successes lies an equally critical, often unspoken truth: Boundless enthusiasm and unwavering positivity, while inspiring, can sometimes lead to burnout and overlooked opportunities.

Consider the familiar mantra: “Winners don’t quit.” While well-meaning, this advice can become a trap, encouraging people to cling to unsustainable paths.

As Naveen Tewari of InMobi points out in his recent post (which inspired me to write this article) perseverance alone doesn’t guarantee success; it’s knowing when to persist and when to pivot that sets winners apart.

The line between optimism and toxic positivity can be blurry, and without careful reflection, young entrepreneurs can find themselves stuck, pushing forward in all the wrong ways.

So, how can one tell the difference? Toxic positivity often looks like hollow encouragement — relying solely on faith, hoping things will work out, without a solid plan or adjustment to a strategy. True optimism, on the other hand, combines hope with a realistic approach. It involves posing tough questions, taking strategic steps, learning from each misstep, and through retrospection, continuously redefining objectives as new insights come to light.

A real-life story illustrates this well: a fan camped outside Shah Rukh Khan’s home for over three months, hoping for a chance to meet him. Instead of relying on luck alone, he planned his approach, staying in a spot holding a placard and expressing his wish to meet SRK. He made the best effort in his capacity and had a higher likelihood of success with that approach than simply waiting by the gate or requesting the security team to arrange a meeting.

If he had spent time lingering in posh areas where the movie star had been seen, banking on a lucky meeting, he’d be channelling his efforts into what I’d refer to as ‘toxic positivity.’ I hesitate to call it ‘false hope,’ because, technically there’s still a slim chance it could happen — but the odds are so low that it would rely almost entirely on random luck. intention.

The startup journey is similar. Success doesn’t just require vision and enthusiasm; it demands resilience, adaptability, and a readiness to let go of one approach to try something new. Aspiring founders would do well to ask themselves regularly: Am I pushing forward with intention and purpose, or am I stuck on a path that no longer serves my goals?

In the end, knowing when to quit isn’t a sign of failure, it’s a mark of wisdom. With every calculated move, every pause to reflect, and every pivot, we edge closer to building something not only successful but meaningful.

So, let passion fuel your journey, but let purpose be your guide.
Cheers,

Dhaneesh Jameson,
Co-founder SugarStrings.ai

Here’s another piece I’ve written on toxic positivity, but from a different perspective — one where people overly glorify failures to justify a dream that hasn’t yet come to life. These experiences can serve as “magic pills” only if you’re able to let go, move forward, and carry forward the wisdom they imparted.

The trap of toxic-positivity

Navigating the Unspoken Pitfalls of the Startup World: Balancing Passion with Purpose. was originally published in D. Jameson on Medium, where people are continuing the conversation by highlighting and responding to this story.

Preventive Genetic Test Benefits

5 Life-Changing Reasons to Take a Genetic Screening Sooner Than Later!

A 2020 report by the International Agency for Research on Cancer (WHO) tells a stark truth about the rising cancer incidents in India: over 1,413,316 new cases and 9,16,827 lives lost- a testament to what goes unseen and unspoken (1). According to this report, the number one killer is Breast Cancer. In 2022 alone, a woman in India died every 5 minutes due to breast cancer.

The 2022 report by FICCI and EY (2) presents even more concerning data, estimating India’s reported cancer cases at 19 to 20 lakh in 2022, with actual incidence thought to be 1.5 to 3 times higher than reported.

According to the Apollo ProHealth Report 2024 (3), India faces a critical health crisis, now considered the ‘cancer capital of the world,’ with cases emerging in Indians almost a decade younger than seen globally.

Yet, a simple step offers hope: Prevention and early detection can change lives. Awareness is the key. Be aware, and be prepared — after all, life is ours to cherish.

Modern cancer prevention is all about personalisation, and it starts with one powerful tool: genetic testing to understand your unique risk. The right genetic test is the essential first step in your prevention journey. Here’s why: 5 reasons you must take a preventive genetic test.

1. Health preservation is easier and more effective when you’re already healthy.

Chronic diseases like cancer don’t discriminate; they can affect anyone, young or old, often without warning or a family history. Early understanding of your genetic profile empowers you to address or mitigate the risks. Ignoring them won’t make them vanish.

It’s true — not every cancer is hereditary, and the odds may seem low. But for those at risk, having this knowledge can be life-changing. Just because past generations didn’t experience cancer doesn’t mean current lifestyle factors won’t trigger it. To know is to protect and honour the precious gift of life.

Did you know that over 85% of human diseases have genetic roots? Some genetic conditions progress quietly, showing no signs until too late, while others have a delayed onset. The key to uncovering these risks lies in looking deep into your DNA- where most answers about your health reside.

With this in mind, early awareness empowers us to choose wellness over illness for our families, reminding us that prevention is always better than cure.

Take breast cancer, for example. While global guidelines recommend starting regular screenings at age 40, recent data from the Health of the Nation Report 2024, by Appolo Prohealth suggests otherwise. Their analysis of nearly 150,000 health screenings conducted over five years found that 25% of breast cancer cases occurred in women under 40.

A clinical-grade genetic test enables early detection of various diseases, not just cancer, with some offering preventive interventions. Proactive steps, like lifestyle changes, are possible through comprehensive DNA analysis.

2. Genetic testing provides lifelong value from a single test.

Unlike other recommended health screenings, genetic testing is a one-time assessment of your DNA that provides insights into your lifetime risk factors.

Once a genetic risk is assessed, there’s no need to retake the test expecting changes in the original findings. However, as new insights emerge, your original data can be updated with a new report if supported by the provider, though policies and pricing may vary.

If someone in their 20s discovers an SNCA gene mutation linked to Late-onset Parkinson’s, they have 35–40 years for preventive action, potentially delaying or preventing the disease. Since the mutation doesn’t change for a lifetime, retesting isn’t needed. The key takeaway: diagnosing this at age 60 may limit the time for preventive strategies.

3. A clinical-grade genetic test offers baseline health information for a longer, healthier life.

If a clinical-grade genetic test is within reach, make it a priority for you and your family, it’s an essential first step in your wellness journey. Because the best time to preserve your health is before any signs of decline appear.

Routine health screenings are designed for early detection, not prevention. They provide a snapshot of your health at the time of testing, showing no risk unless your health has already begun to decline, leaving traces for routine checkups to detect.

A genetic test complements routine screenings, serving as a baseline to identify unique health risks. It enables more personalised, purposeful checkups guided by genetics rather than generalisations.

For example, a colonoscopy is crucial for the early detection of colon cancer, but without knowing the risk, few would proactively include it in regular checkups. Similarly, mammograms, even advanced 3-D tomosynthesis miss up to 50% of tumors in women with dense breast tissue. While technology improves daily, knowing your genetic predisposition to life-threatening diseases ensures a more thorough investigation beyond a single screening method

The bigger question is: how many diseases can realistically be screened individually, year after year, without guidance? A personalised approach, guided by genetic risk, offers a far more efficient and targeted solution.

However, not all genetic tests are suited for disease prevention. Many recreational tests offer general insights but lack the detail needed for actionable clinical guidance, often suggesting general healthy habits rather than targeted prevention — something that doesn’t necessarily require DNA analysis in my opinion.

4. Awareness brings responsibility: preparedness for the at-risk, and reassurance for the rest.

Some argue that knowing health risks can reduce quality of life by creating fear of disease. However, choosing not to know limits your options for change.

Think of it like navigating traffic in a busy city and you got to catch a flight on time: we use maps to avoid blocks and find better routes, even if they aren’t the primary options. Awareness provides similar guidance, offering proactive choices to improve health and mitigating the risk of getting stuck without options.

Knowing your genetic risk provides insights you might otherwise miss, helping you stay attuned to early warning signs while guiding you on a proactive path to avoid illness altogether.

5. Awareness of your drug response may save your life in urgent medical situations.

Understanding an individual’s genetic response to medications can be life-saving in medical emergencies, a vital but still underutilized approach. This will become more common as clinical exome and genome sequencing become routine, as it already is in cancer care.

Consider the blood thinner Warfarin, one of the top 10 drugs linked to serious side effects yet a common name in the genetic reports. Studies have shown that approximately 75-80% of the Indian population carries the VKORC1–1639GG (rs9923231) genotype, which is linked to a higher warfarin dose requirement. (4)

Clinical management of Warfarin is challenging as people respond differently. It requires a careful balance and even small dose changes can have ill effects. If the blood gets too thin (high INR a.k.a International Normalised Ratio), it can lead to fatal bleeding, and if it’s too thick (low INR), it raises the risk of clots.

Another practical challenge is that genetic tests take 2–4 weeks to deliver results. In emergencies, doctors must rely on limited information, making decisions while patient safety remains at risk.

Having a preventive genetic test with drug responses is like keeping your valid ID handy, you’re prepared when the need arises, ensuring safer, more effective care when every second counts.

Even if a genetic test shows no elevated risk, continuing recommended screenings is wise to catch any unexpected diseases. Starting with a clinical-grade genetic test brings awareness, clarity, and confidence — offering a personalised, proactive step toward health and longevity.

Cheers,
Dhaneesh Jameson,
Co-founder, SugarStrings.ai

References

1. India Fact Sheet by Global Cancer Observatory. Globocan 2022 (version 1.1)
2. Call for Action: Making quality cancer care more accessible and affordable in India by FCCI and EY
3. Health of the Nation Report 2024, by Appolo Prohealth
4. Warfarin pharmacogenetics: How close are we to clinical practice?

5 Life-Changing Reasons to Take a Genetic Screening Test Sooner Than Later! was originally published in SugarBeets on Medium, where people are continuing the conversation by highlighting and responding to this story.