The word “agentic” (which barely existed in vendor marketing eighteen months ago) appears on seven of them. Cube calls itself “THE Agentic Analytics Platform.” Acceldata calls itself an “Agentic Data Management Platform.” These companies do fundamentally different things. But have the same adjective.

AI-generated or lazy copywriting isn’t the issue though.

It’s a problem with positioning. When every company sounds the same, it means none of them have made a real choice about who they serve and why.

Snowflake ($3.4B in product revenue) and Databricks ($2.4B run rate) can afford this. They have billions in brand equity, sales armies, and analyst relationships doing the work their homepages don’t. Vague messaging is a luxury when you’re already the default name a CIO thinks of.

This piece is about everyone else. The Series A observability startup. The seed-stage BI tool. The open-source project building a commercial layer. For them, sounding like Snowflake isn’t “great-artists-steal”, it’s simply invisible. Invisible is fatal without a $500M sales team to compensate.

The Echo Chamber, Quantified

I audited eight companies in detail - dbt, Fivetran, Databricks, Snowflake, Monte Carlo, Bigeye, Acceldata, and Matillion and tracked their homepage language against a set of common “power words.”

The repetition is far from subtle. Snowflake uses "trusted, scalable AI Data Cloud." dbt promises to "deliver trusted data." Acceldata ensures "trusted, reliable, and AI-ready data at scale." Bigeye's entire repositioning hinges on "AI Trust Platform."

Buyer language is where the sameness gets absurd. Databricks addresses “your entire organization.” Snowflake speaks to “organizations” broadly. Fivetran targets “startups to global enterprises” (that’s literally everyone). Bigeye lists six persona titles: Data Engineer, Data Analyst, Data Leader, Data Architect, Data Governance, Data Executive. Listing six personas is functionally the same as listing none.

Customer pages aren’t better. Every audited company displays logo bars across at least five unrelated industries with no segmentation logic. AtScale shows retail next to pharma next to cruise lines. Airbyte mixes Carlsberg Group with Disney, JetBrains, and the LA Dodgers.

G2 reviews show a gap between what vendors market and what the actual users say. The vendor language is so far from the buyer language that the two barely ever overlap.

Another pattern is the humanized AI copilot. Matillion now leads its homepage with “Meet Maia: Your data engineering buddy.” We saw shared adjectives and now shared narrative is on the rise. Name the AI, give it a personality, call it a buddy, make it the hero. Same playbook, new cover.

Three Reasons Everyone Sounds the Same

1. Category adolescence creates mimicry. In young markets, the best-funded company sets the vocabulary and everyone copies it.

   

   
   Then there’s “AI-ready data.” The phrase exploded after 2023, now used by IBM, Gartner, Alteryx, Denodo, Dremio, DataHub, Atlan, Informatica, dbt, and more. One company didn’t coin it. Gartner did; with a stat: “63% of organizations lack AI-ready data management practices” and vendors claimed it. It now appears so universally and says nothing about any specific offering.

   
   The vocabulary gets set by two or three leaders. Everyone else optimizes for proximity instead of differentiation.
   

2. Analyst vocabulary locks everyone in. Gartner and Forrester evaluate markets and create the vocabulary that defines them. Companies optimize messaging to match, because enterprise buyers use analyst terms in RFPs and budget justifications.

   
   Gartner coined “augmented analytics” and “data fabric,” both are now universally adopted. When Gartner published its first Market Guide for Data Observability Tools in 2024, it formalized a category that Monte Carlo says it created in 2019. Every observability vendor immediately aligned their language to Gartner’s definition. The 2025 Gartner Magic Quadrant for Data Integration Tools made “AI-ready data” the central evaluative lens. Within months, that vocabulary appeared on almost every homepage. Informatica leads its homepage with Gartner Magic Quadrant recognition across five separate MQs.

   
   Analyst categories serve as a linguistic constraint. Use different vocabulary from the Gartner category definition and you risk being excluded from the evaluation enterprise buyers rely on. The rational response is conformity. Homepage headlines read like Gartner category definitions because they are.
   

3. TAM anxiety stops anyone from choosing. The broadness of AI data company messaging is deliberate. It’s a strategic choice to not have a specific buyer, driven by investor expectations and competitive pressure.

   
   No company among the eight audited has a publicly stated Ideal Customer Profile. When Snowflake says “for everyone,” Databricks can’t afford to say “for data engineers only.” Platform economics reward horizontal claims. Sales teams need flexibility to pitch any prospect without marketing materials contradicting them.

These three reasons are mutually inclusive. Category adolescence creates a vocabulary vacuum that leaders fill. Analyst capture formalizes that vocabulary into evaluation criteria. TAM anxiety ensures no company breaks from the pack because the perceived costs of specificity like smaller market, analyst exclusion, sales friction, appear to outweigh the benefits. The system self-perpetuates.

Here’s the thing, though. For Databricks and Snowflake, this status quo is fine. For everyone else, the ones that are still earning their first thousand customers, copying the copy is a strategic error disguised as a safe bet. You end up competing on the incumbent’s terms, with a fraction of their budget.

Two That Broke the Pattern

The selection criterion was only positioning, and two stood out. These had undeniably a more specific positioning. They made the deliberate choice to be narrow and use it as an advantage.

1. Evidence told most of the market it wasn’t for them. Their tagline is “Business Intelligence as Code.” Reports are markdown files with embedded SQL queries. Dashboards are version-controlled by default. Changes go through PRs (pull requests) before publishing. The homepage shows an IDE screenshot and code syntax instead of a dashboard mockup. A visual way to filter out the wrong buyer before they read a word.
   
   Their buyer definition is so surgical that its documentation states, “To use Evidence you need to know SQL. A knowledge of basic markdown syntax is also helpful.” If you know SQL and markdown, you’re in. If you don’t, this product explicitly isn’t for you.
   
   This is a direct rejection of the dominant BI space. Where Tableau and Power BI use “empower business users,” Evidence uses software engineering vocabulary - version control, testing, deployment etc. The deliberate exclusion is what makes the positioning work so well. Growth signals also confirm it with 5,800+ GitHub stars, 17K+ weekly npm downloads, a 2K+ member Slack community, SOC 2 Type II certification, and transparent pricing at $15–$25/user.
   

2. MotherDuck named the 95% nobody else acknowledged. CEO Jordan Tigani helped create Google BigQuery and led it through its first ~$1B in revenue. Then he publicly argued that most people don’t need BigQuery.
   
   His thesis gave the most specific buyer definition in the cloud data warehouse market. “For the 95% of us who do not have petabyte-scale data.” It names a situation where you’re overpaying for compute you don’t need and a trigger when you realize your Snowflake bill doesn’t match your data volume. MotherDuck identified its personas based on the problem they’re trying to solve, their jobs-to-be-done.

   

   MotherDuck’s “Dual Execution” model runs queries simultaneously on your local machine and in the cloud. The client is a compute node. This is fundamentally different from every other cloud warehouse where all computation happens server-side.
   
   And, it’s working: ~$133M in total funding at a roughly $496M post-money valuation. Transparent pricing with a free tier at 10GB, paid tiers at $25/month and $100/month with per-second billing. Press coverage consistently uses MotherDuck’s own language. Journalists describe it as “serverless DuckDB in the cloud” and cite the “Big Data is Dead” thesis directly. The positioning has been validated and adopted by the market. It wasn’t forced on it and therefore, it’s more credible.

Two honorable mentions: Dagster published an essay called “The Rise of the Data Platform Engineer” that coined a new buyer identity about the specific person who builds the platform that enables others to build pipelines (quite different than a data engineer). Recce opens with a qualifier question: “You wouldn’t merge untested code. Why merge untested data?” Their self-described category, “data change management,” didn’t exist before they named it. Their ICP is the narrowest in the space: anyone opening a PR that touches dbt models.

The Common Denominator

All four share a trait that separates them from the sameness cohort. Their positioning specificity is a direct consequence of strategic choices that can’t be replicated as features.

So What?

The sameness across AI data companies isn’t a branding problem that better copywriters can fix. It’s an outcome of how enterprise software markets get shaped. Analyst firms define categories. Companies optimize messaging to match. Investors reward broad TAM claims. Sales teams prefer flexibility. Sounding the same makes sense; it feels safer.

The companies that escape this share a counterintuitive insight. They don’t differentiate through fancy words. They differentiate through product constraints that make certain words true only for them.

The next companies to break through won't be the ones that find smarter adjectives. They'll be the ones that make a strategic choice so specific that the positioning writes itself.

You're good at what you do. You put in the work. You hit deadlines, build decks, reconcile data, write docs, manage the next launch and so much more.

You’ve built your reputation on execution. You must be essential.

But here’s the thing: execution is getting cheaper. And that’s nothing to do with your time and effort being worthless. It’s only because machines can now do parts of your job much faster, more accurately, and at scale.

McKinsey reports that up to 70% of current work activities can be automated.

What used to be “doing your job well” is starting to look a lot like an operating expense ripe for optimization.

So if more than half of your job is automate-able today, what are you still doing manually and why?

Now, You Pivot

You don’t get a memo when the paradigm shifts.

It shows up in what teams stop doing. It starts with tools that feel “innovative”.

A dev team gets an AI assistant that writes production-ready code. An analyst wakes up to interactive dashboards built overnight. A PMM (me) uses a prompt to analyze customer feedback in under a minute.

Satya Nadella said it flatly,

As AI gets more efficient and accessible, its use will skyrocket, turning it into a commodity we just can't get enough of.

But the big insight is the higher the efficiency, the bigger the appetite.

Welcome to Jevons Paradox. In the 1800s, economist William Stanley Jevons noticed that as steam engines got more efficient, coal consumption didn’t drop as expected. It exploded. And therefore, counterintuitively, the more efficient a process becomes, the more it’s consumed.

Automation is shifting the nature of the work we do. What was once a win becomes a given. More campaigns, more analyses, more releases - not because the business grew, but because the friction dropped. For many, it has become synonymous with ‘losing job’. And this is what most of us have misunderstood about automation.

That mindset is holding people back more than the tech ever could. If you’re still measuring your value in tasks completed, you really need to catch up. The market is rapidly shifting toward smarter systems and outcome-first thinking.

Call it a trite but “work smarter, not harder” has never been more real.

Free Yourself From Repetitive Loops

The first instinct most people have toward automation is resistance.

If the function writes itself dynamically, where does that leave a developer?
If an LLM can write the copy, what’s the value of a writer?
If the data’s already analyzed, what’s left for an analyst to figure out?

Fair questions and the honest answer is if your job is defined by what a machine can do, it’s already under pressure, automated or not.

But there’s a deeper issue. The fear of identity.

Execution is often how we prove our worth. A well-built dashboard. A perfectly executed launch. A detailed audit report. These become the currency of credibility. Letting go of tasks you’ve mastered can feel like surrendering control (and pride).

But here’s what automation can’t do:

• Set priorities

• Understand nuance

• Create strategy

• Build consensus

• Make tradeoffs

• Spot gaps before they become fires

Discerning professionals don’t fear automation. They use it to move upstream. If you're clinging to execution as your safety net, you're standing in the way of your own growth.

Offload the repetition. Let automation handle the motions while you focus on creating value at scale.

Copy What’s Working (Peers, Proof and Pattern)

Look around. The people making the biggest career leaps right now aren’t busy doing more. They’re learning faster and acting smarter.

Sometimes it’s a teammate who demoed their new Zapier workflow. Sometimes it’s proof like case studies, pilot projects, a free trial. And sometimes, it’s hard data:

• With 20% efficiency gain, JPMorgan freed up talent for high-value projects

• 30% cost reduction; ANZ Bank redeployed staff to strategy and compliance

You don’t need to find a mentor. You need a pattern, a “network of mentors.” You could be:

• The product manager who automated reporting and used the reclaimed hours to design and test hypotheses around user behavior; uncovering why a feature wasn’t landing and turning it into one that did.

• The engineer who escaped deployment cycles and now shapes long-term technical direction; judging tradeoffs, anticipating scale challenges, and guiding architecture choices that no tool could predict.

• The marketer who stopped building decks and started shaping strategy by translating AI-generated insights into executive narratives that actually drive decisions and win stakeholder buy-in.

So yes, it works. Yes, it scales. And no, you’re not going to be relevant if you wait too long.

What’s the Task You Dread?

This is the moment when it clicks.

Now that you’ve done the homework, you’ve seen what others are doing: automating, delegating, moving faster. You’re no longer asking if automation helps; you’re wondering where to start.

Start here: What’s one task I never want to do manually again?

Pick something small. One report. One workflow. One thing you still do every week that doesn’t need to be manual anymore.

What changes is how you think about your role.

You stop measuring value in tasks crossed off. You start thinking in systems, leverage, and outcomes. What once felt like “the job” now looks more like setup. The real work is figuring out what’s worth your time.

That moment of clarity? That’s your ‘Aha’ moment.

Once you’ve experienced what it feels like to automate the routine and use that time to think more clearly, build more strategically, or collaborate more intentionally; you won’t go back.

One well-placed automation flips the script on how you work and what you’re worth.

That’s your first real win, and a smarter use of your time.

Solve Once, Scale Forever

Let’s be clear on one thing, your first automation won’t be perfect. It’s not supposed to be. That’s not failure, but a call for iteration. And iteration is the engine of strategic growth.

You’ll get things wrong. A script crashes. A workflow misfires. Someone asks why it took two hours to fix something that was supposed to “save time.”

Good. That’s data!

Iteration is feedback made actionable. It's how you move from reactive to proactive and shift to becoming someone who refines systems, raises the bar, and compounds returns. This is the inflection point where skills become judgment. You’re now designing how the work evolves.

The more you iterate, the more fluency you build. You stop duct-taping solutions and start making durable ones. You think further ahead. You move faster and smarter. You go from solving for symptoms to solving for systems.

That’s when people notice. You become the person who makes work work better. You don’t hoard efficiency, you share it. You earn trust by repeating a simple pattern: solve once, scale forever.

This is what iteration at its best looks like. Along with the system, it’s made you smarter. And then it hits you: the real shift was never technical. It was mental all along.

So next time someone says “AI is taking our jobs,” remind them:

You don’t lose your job to AI. You lose it to someone who uses it better.

The framework that helps explain and fix this gap is the Data–AI Value Chain. I’ll be unpacking each stage of the value chain, exposing its sticking points, and reveal why increasing efficiency at any stage won’t automatically reduce complexity or resource demand.

What Is the Data–AI Value Chain?

Its a four-stage framework that reflects the transformation of raw data into actionable business value:

1. Data Integration: Connecting disparate data sources and creating a unified data foundation

2. Data Governance: Ensuring data quality, security, and appropriate access

3. AI Implementation: Deploying AI capabilities that leverage the unified data foundation

4. Value Realization: Measuring and capturing the actual business impact of data and AI initiatives

Each stage has its own logic, bottlenecks, and breakthrough practices. But instead of needing to perfect any one link, the goal should be to strengthen the entire chain and do so in a way that accounts for how improvements in one area can expose weaknesses in others.

Stage 1: Data Integration

The Problem: Data analysts spend 80% of their time collectively on searching for, preparing, and governing data, leaving 20% of their time for actual data analysis. And it’s not getting better. The data is coming from hundreds (even thousands) of sources. According to a survey by Matillion and IDG, the average number of data sources per enterprise is 400, with more than 20% reported drawing from 1,000 or more. M&A activity, SaaS sprawl, and real-time data flows have made unification more fragile, not more reliable.

Emerging Practice: The solution isn’t more ETL tools. It’s architecture. Unified platforms, metadata-driven data fabrics, and hybrid data mesh models are replacing the “modern data stack” bloat that made integration someone else’s problem. A hybrid approach, a semantic data fabric with domain-owned data products, is proving both scalable and governable.

Insight: Low-code/no-code, metadata-first environments drastically reduce the time-to-integrate and allowing domain teams to contribute data without dependency on central engineering.

Enter Jevons Paradox: As integration becomes easier, organizations integrate more. Instead of consolidating systems, they connect more of them. This results in more data pipelines, more complexity, and higher cognitive overhead. Efficiency doesn’t reduce demand, it shifts the bottleneck downstream.

Stage 2: Data Governance

The Problem: AI projects fail when the data can’t be trusted. In one survey, 77% of data leaders said data quality issues were discovered by business users, not via automated checks or governance tools. Gartner reported that the most common data governance issues are compliance audits, warnings for non-compliance and data breaches.

Emerging Practice: Governance today has become a product requirement. Companies that build centralized metadata layers, implement federated governance models, and use purpose-based access controls are seeing faster adoption and fewer data incidents. AI-ready governance now includes bias detection, lineage tracing, and model accountability frameworks.

Insight: The shift toward active metadata, where context flows from integration into governance, is key to building trust at scale. Transparency in process builds organizational confidence in the output.

Jevons Applies Again: Improved governance lowers the friction for more teams to use more data. But as trust increases, demand does too. And that demand isn’t always predictable. The paradox is that stronger governance invites wider access, which can strain governance systems unless they’re automated and elastic.

Stage 3: AI Implementation

The Problem: Most AI lives in company slide decks, not systems**.** Only ~26% of companies have the capabilities to move AI beyond proof-of-concept to real value generation. Model sprawl, talent gaps, and missing MLOps infrastructure stall progress. Even when prototypes succeed, productionizing them is slow and brittle.

Emerging Practice: Organizations that scale AI treat it as product development, not experimentation. That means clear prototype-to-production paths, workflow redesigns, model registries, and unified

Insight: There’s a shift from PoCs to production. Success depends less on AI model accuracy and more on how seamlessly the AI integrates into business decision loops and feedback cycles.

The Jevons Tension: As AI implementation becomes easier via APIs, model hubs, and autoML, its usage explodes. But this also increases data consumption, model monitoring demands, and governance burdens. Every successful AI use case invites five more. Instead of reducing workload, efficiency multiplies use cases.

Stage 4: Value Realization

The Problem: Many teams can’t prove that their data or AI investments drive business value. Metrics are often post-hoc or disconnected from outcomes. A 2024 McKinsey study found that more than 80% of respondents say their organizations aren’t seeing a tangible impact on enterprise-level EBIT from their use of Gen AI.

Emerging Practice: Leading organizations are embedding business KPIs into their data and AI programs from the start. Value-based prioritization, feedback loops, and AI observability platforms ensure continuous alignment with business goals. Some firms now operate AI value offices with the mandate to tie models directly to revenue, cost savings, or risk reduction.

Insight: KPI frameworks and continuous feedback loops enable business alignment within data teams, finance, operations, and product functions.

Jevons' Final Lesson: Value realization does not cap consumption. Once a model proves value, demand surges. That leads to requests for new features, new models, and more data. Efficiency at this stage becomes a flywheel, increasing expectations, technical debt, and the need for cross-functional alignment.

Successful AI implementation and value creation requires addressing distinct challenges at each stage. A holistic strategy is needed to address all four stages.

Organizations must navigate specific challenges at each stage while implementing corresponding solutions to progress effectively through the Data-AI value chain.

Evolve or Loop

The lesson across all four stages is clear: efficiency without orchestration doesn’t scale. It gets fragmented and organizations trying to "optimize" each step in isolation fall into pilot trap, i.e. successful demos that never evolve into business-wide impact.

To avoid this, companies should implement and/or refine their Data–AI Value Chain. Some actionable strategies and recommendations:

1. From Linear Pipelines to Adaptive Loops:

   The Data–AI Value Chain cannot be a one-way street. It needs feedback loops from value realization back to data prep, governance updates, and AI retraining. Dynamic orchestration is key.

2. From Central Ownership to Federated Enablement:

   Data mesh and semantic data fabrics are not mutually exclusive. A hybrid model lets domain teams move fast within platform-level guardrails.

3. From Compliance-Only Governance to Proactive Trust:

   Governance should be built for discovery and confidence, not just risk mitigation. This requires metadata automation and integrated model oversight.

4. From AI Prototypes to Products:

   MLOps, model observability, and business alignment are the infrastructure that separates pilots from platforms.

5. From Efficiency to Value-Centric Design:

   Every stage should be scoped by its contribution to business outcomes. That means asking not “Can we do it faster?” but “Should we do it at all and why?”

The Chain Is the Strategy

AI, by itself, cannot transform businesses. Systems can. And the most important system to get right in 2025 is your data–AI value chain.

The biggest risk is misalignment. When organizations fail to connect the dots between integration, trust, AI scaling, and value realization, they accumulate technical debt disguised as innovation. Worse, they amplify demand without control, accelerating costs without outcomes.

But when implemented holistically, with feedback loops, federated ownership, and rigorous value tracking, the Data–AI Value Chain becomes more than a framework. It becomes a strategy. One that’s ready the next AI model, and for the next ten.

Around that time, I came across this talk by Andy Raskin. If you’re not familiar with his work, Andy isis widely regarded for his thinking on strategic narrative in tech. In the talk, he explains how companies win by aligning around a “Promised Land”, a compelling vision of where the world is going and how their product helps customers get there.

That idea hit hard. I started digging into the commercial side of the business about how products were positioned, bought, adopted, and loved (or abandoned).

That’s how I found product marketing.

What drew me in was the blend of deep product context, customer insight, data fluency, and storytelling. What’s kept me here is the satisfaction of being close to both the why and the how of knowing the user, the problem, and the solution in equal measure.

The Rise of Product-Led Growth (PLG)

Product marketing has mostly been seen as understanding customer needs, crafting positioning, and enabling sales.. But the game has changed a lot in recent years. The rise of product-led growth (PLG) means marketers must collaborate deeply with product teams from the start. The number of martech tools jumped from only 150 in 2011 to over 14,000 in 2024. That’s a 9,300% increase in 13 years, visualized below.

Modern marketers are swimming in data dashboards, automation scripts, and AI-powered analytics, far from the gut-driven campaigns of the past.

During this shift emerged an interesting trend that many of today’s top marketing leaders hailed from technical backgrounds. One Forbes writer mentioned -

“most [tech industry CMOs] have an engineering or computer science background,”

Only a tiny fraction of chief marketers actually hold marketing degrees anymore (just 6%, according to Harvard Business Review) and studied fields like engineering instead.

Engineers Aren’t Supposed to Be Marketers. So Why Are They So Good at It?

The blend of these two worlds creates a powerhouse skillset and a distinctive advantage.

Technical Fluency and Credibility

Engineers-turned-marketers have the ability to understand the product deeply – how it's built, how it works, and what truly differentiates it. This technical fluency means they can translate complex features into clear customer benefits. It also earns them instant credibility with product development teams and with technical customers.

Instead of simply reciting a list of features, they can demo the product with genuine understanding or dive into a technical Q&A with confidence. In an era when B2B buyers are often engineers themselves, a marketer who "speaks engineer" is incredibly valuable. I've seen sales engineers and product managers respond with pleasant surprise (and respect) when a marketing colleague can engage on something like an API integration. That credibility greases the wheels between departments and ensures marketing promises align with the reality of the product.

Analytical and Systems Thinking

Engineering is fundamentally about problem-solving within constraints. Great engineers learn to view problems holistically as systems, identifying cause and effect. In marketing, this manifests as a talent for mapping out entire customer journeys and funnels, optimizing each piece while keeping the whole system in mind.

When I plan a go-to-market launch, I often find myself sketching flowcharts to think through how a prospect will flow from an ad click to the website to a trial signup to a paid conversion, and where the bottlenecks might be. This systems thinking leads to data-driven decision-making. Instead of relying on hunches, engineers in marketing lean on metrics, A/B tests, and empirical feedback.

The result is marketing strategies that are continually optimized like a well-written clean code, and a willingness to iterate quickly when the data suggests a better approach. The analytical approach cuts out wasted budget and removes much of the "black box" tag marketing once had. As Steve Blank put it, "[having] ex-engineers and domain experts makes one heck of a powerful marketing department," because they bring the customer insight and logic most marketing teams lack.

Customer Empathy Rooted in Problem-Solving

There's a stereotype that engineers are hyper-logical and aloof from emotions. But I think, the best engineers are intensely user-focused. They thrive on solving real problems for people. That focus on the end-user's pain points give engineer-marketers a strong sense of customer empathy.

Instead of seeing "the customer" as an abstract concept from a persona slide, they often visualize an actual person struggling with an actual problem. At my first job as a developer, I learned to ask: What problem are we trying to solve for the user? I carried that into marketing by constantly interviewing customers and using their feedback to shape narratives.

Engineers also tend to be avid listeners during those interactions which helps uncover the nuanced motivations and hesitations of customers. This empathy, combined with technical know-how, means they can connect the dots between what the product does and what the customer actually cares about at a deeper level.

Clarity in Communication

It might seem counterintuitive to praise engineers for communication. Either they’re too introvert or they only speak tech jargon. But effective engineers learn to simplify complexity. Many engineers have had to present project ideas or explain technical issues to non-technical stakeholders, developing their ability to distill complex ideas into clear, accurate explanations.

This skill translates perfectly to marketing. My work improves when I follow my instinct to strip away buzzwords and focus on simplicity and clarity. And as an engineer, you always ensure every claim is truthful and substantiated.

This skill works in two ways. This straightforward-no-BS communication style can become a brand strength. Audiences today look for authenticity, transparency and concrete information in a sea of hype. And with a bit of coaching and practice, many engineers learn to tell compelling stories around that information, effectively becoming "data-driven storytellers" who bridge logic and emotion.

Engineering Skills in Modern Marketing

These strengths are increasingly crucial as marketing becomes a high-tech, customer-centric discipline. Take product-led growth for example, instead of traditional marketing handing off leads to sales, PLG relies on the product experience to convert users. Marketing in a PLG company might involve tweaking in-app onboarding flows, analyzing usage telemetry, and running experiments in the product itself. It blurs the line between product management, growth engineering, and marketing. No surprise that nearly three-quarters of SaaS companies have now adopted some form of product-led growth motion.

In this setting, who better to mobilize these business efforts than someone comfortable with both the technical product details and the psychology of user experience? The engineer-marketer fits the bill perfectly.

Navigating the MarTech Stack

Thousands of tools from marketing automation to SEO analytics to CRM integrations. A product marketer today might need to configure a segment in a customer data platform in the morning, then interpret an attribution report in the afternoon. The learning curve can be steep for those without a technical bent. But for someone who's perhaps written scripts or built dashboards before, it's just another day at the virtual lab.

Leveraging AI in Marketing

The increasing role of AI in marketing is another factor. We now have AI tools that can personalize content, predict churn, and even generate marketing copy. To leverage these well, you need to understand how they work, their limitations and vulnerabilities. Engineers, already familiar with concepts like algorithms and data modeling, can adopt and troubleshoot AI marketing tools with relative ease. They're much less likely to be intimidated by new tech and often champion the next best tool out there.

The Engineering Mindset Advantage

Beyond the skill set, there's something to be said about mindset. Engineers are trained to experiment, learn from mistakes, and keep improving. When an engineer launches a marketing campaign, they don't see it as a one off task-to-be-completed. They see it as version 1.0, gather feedback, and iterate to create improved versions.

If something breaks, they do a root cause analysis to analyze the funnel step by step to find where the issue lies. This iterative, agile approach to marketing can significantly improve outcomes over time, especially compared to static annual plans of the past. It also encourages trying bold ideas in small bets, because failure is a learning opportunity, not a disaster.

At my previous company, the CMO (also engineer by training) used to push the team to test a new channel or message, as long as it was measurable. He treated marketing initiatives like experiments, testing ideas with minimal resources against clear metrics before scaling. And his catchphrase was, "Let's prototype this campaign!”

Real-World Engineer-Marketers in Action

To show how powerful this engineer-marketer combo can be, let's look at a few real-world people who inspire me. Their paths are different, but the through-line is the same. They bring product fluency, structured thinking, and an instinct for clarity.

Steve Blank

In the 1980s, Steve Blank disrupted traditional Silicon Valley marketing by hiring PhD engineers as product marketers. Faced with selling complex minicomputers to scientists, he realized the usual MBA marketing playbook wouldn’t cut it. Instead, Steve did something radical and hired PhD engineers and domain experts from those scientific fields and taught them marketing. Why? Because they understood the technology and the customer’s needs in a way no traditional marketer could.

It was controversial at the time, but it worked brilliantly. These ex-engineers-turned-marketers could speak to customers as peers and knew exactly which features mattered in “finite element analysis” or “computational fluid dynamics” because they had lived those problems.

Marcel LeBrun

Marcel LeBrun's career is a case study in blending engineering strengths with marketing leadership. He began as a software engineer in telecom and went on to found Radian6, a social media monitoring platform that gave marketers deep insights into online conversations. After Salesforce acquired Radian6 in 2011, Marcel transitioned into leading product marketing within Salesforce’s Marketing Cloud. His technical background was key. He understood exactly what the software could do, and he could articulate its value to CMOs in terms that mattered to them.

Marcel acted as a translator and strategic connector between product development and marketing. Under his leadership, Radian6's technical capabilities were integrated with savvy marketing narratives, helping Salesforce become "the trusted cloud provider of the CMO" in the social era.

Robin Saitz

Robin Saitz took a more systematic approach. She earned a mechanical engineering degree and spent her early career in technical roles. But she eventually moved into marketing and rose to become CMO at several tech companies.

By the time she became CMO of Brainshark and later Rockwell Automation's marketing chief, her engineering background was a celebrated part of her identity. When Robin was promoted to CMO at Rockwell, the press release highlighted that -

“Robin is an engineer turned marketer with a deep expertise in marketing across multiple industries.”

Robin applied her systematic thinking to modernize marketing operations through automation and analytics. Her engineering background was celebrated as a strategic asset, enabling seamless collaboration between technical teams and marketing. Colleagues noted that she could dive into product discussions with engineers one minute and customer segmentation the next.

Robin’s success highlights the advantage of an analytical mindset, reinforcing why STEM backgrounds increasingly define leadership roles in tech marketing today.

The Engineer-Marketer Advantage

These stories highlight one solid pattern - marketing teams gain a significant edge when they embrace technical talent. Engineers approach marketing challenges with a builder’s curiosity. They ask questions like “How does this work?” and “How can we make it better?”, beyond simply figuring out how to sell it.

In a time when trust and authenticity matter, having technically proficient marketers ensures the messaging isn't empty. It's grounded in real capabilities, building trust with skeptical audiences. Internally, their dual fluency promotes tighter cross-functional collaboration. They become translators, bridging the gap between product, engineering, sales, and marketing, aligning these teams around a unified strategy. Whether ensuring marketing collateral accurately represents the product or advocating for roadmap features based on customer insights, they help create cohesive and strategically aligned organizations.

But technical skill alone isn't enough. Effective product marketing demands storytelling finesse, emotional intelligence, and an intuitive grasp of customer psychology - areas engineers often need to intentionally develop. Crafting compelling narratives and emotionally resonant messages doesn't always come naturally compared to modeling data or debugging systems. Not every engineer will want this transition, and not all marketing roles benefit equally from technical expertise.

Forward-thinking companies are increasingly hiring marketers with engineering backgrounds precisely because they value this hybrid talent. Even traditional MBA programs are evolving, embedding data analytics, systems thinking, and technical fluency into their curriculum.

The most effective marketers of the future won’t be defined by either analytical rigor or storytelling alone. Instead, they'll thrive at the intersection, comfortable blending technical clarity with empathetic communication. This is exactly what the market demands today - marketers who can build bridges between product truth and customer trust.

The Edge: Equal Parts Tech and Narrative

Looking back, moving from engineering to product marketing was more of an evolution. I didn’t trade my engineering mindset for a new one, I brought it with me. What once helped me debug code now helps me dissect market signals. The curiosity that pushed me to learn frameworks now drives me to understand customers. The skills I thought I’d have to shed became my edge.

Analytical rigor, systems thinking, and a builder’s instinct elevated marketing for me. I’ve used them to create data-driven GTM strategies, run experiments with precision, and bring logic to creative execution. The result is an accountable form of art.

Zooming out, this isn’t just my story, this is where the industry is going. Marketing today is deeply technical. From PLG to AI tools to martech stacks, marketing now demands fluency in systems, data, and experimentation. The best marketers of the future will move fluidly between technical and creative domains.

If you’re an engineer considering this path, or already walking it: you’re not an outlier, you’re part of what’s next. Your instincts to question, optimize, and simplify are assets. Your creative skills don’t make you less of an engineer, they make you a more complete problem-solver.

So wear your hybrid background with pride. We’re doing more than marketing products, we’re engineering new ways to connect with people.

That’s the engineer’s edge. And it’s only getting sharper.

Picture the first 10 minutes of a Pixar film – the emotional sequence in Up or the opening of Finding Nemo. In those brief moments, you’re hooked. You care deeply about the characters and want to see their journey unfold. They make you feel something, fast. What if a product’s onboarding could do the same? In the first few minutes of getting in your product, can a user feel progress, clarity, or even delight? They should.

Most users decide whether a product is worth their time within minutes. Nearly half will abandon a new software during setup if it feels like a chore. That means the stakes are high, and onboarding is more than a UX problem, it's a product adoption problem. If your product is amazing, users should feel that immediately. Onboarding is your only shot to make that happen.

The secret is to stop treating your product as the hero and start making your user protagonist. Great onboarding shouldn’t feel like a feature tour or a checklist. It has to be a narrative that engages them, earns their trust and shows them they’re in control.

Just like Pixar captivates audiences by putting a relatable hero on an exciting path, successful products onboard users to solve their own problem. This is done without having to show off the features but by showing the user what’s now possible.

This is where storytelling and behavioral psychology become essential tools. Make the user the hero, and your product the guide. When done right, you earn trust, unlock motivation, and improve retention - all in a matter of minutes.

Why Storytelling in Onboarding Works

Something I keep repeating is humans are wired for stories. A report in the Journal of Consumer Behaviour shows that stories trigger mirror neurons in our brains – the same cells that fire when we perform an action or experience something ourselves. So when users encounter a story, they watch it unfold and feel certain emotions. That emotional simulation makes the experience more persuasive and more memorable than any static tutorial.

But a story only works if there’s a relatable character. In your product, that’s the user; not the interface, not the brand.

Your product isn’t the hero. It’s the Guide.

Take Slack for example. When a new user signs up, they’re dropped into a friendly chat with Slackbot. It welcomes you, asks a few easy questions, and replies with small tips, one at a time. The user leads. The bot helps. Even the line “I’m just a bot” lowers expectations and tension. The whole experience makes the user feel in control, not tested.

Slack’s product marketing director once said that new users begin “interested but skeptical.” The job of onboarding is to help them “discover whether [the product] does what you hope.” That discovery, the moment when skepticism turns into excitement, is the emotional payoff.

Slack’s onboarding tells a tiny story: a hesitant beginner becomes a confident communicator. That’s what good narrative does. It transforms the user’s self-perception, along with their understanding of the tool.

Designing the Hero’s Journey in Onboarding

So how do you actually turn your user into the hero of their own story? It’s simpler than it sounds - just follow the basic structure of a classic narrative arc.

Frameworks like Joseph Campbell’ Hero’s Journey and Dan Harmon’s Story Circle both follow the same fundamental pattern: a character leaves their comfort zone, pursues a goal, faces challenges, receives help, and returns transformed. It’s a formula humans intuitively understand, because we’ve been telling it for thousands of years.

I’ve adapted this pattern into a five-part onboarding structure that aligns with how people actually experience progress. It’s practical, repeatable, and rooted in how we process stories, emotionally and cognitively.

1. Hero (User) and Their Goal

Every great story starts with a character who wants something. In your product’s onboarding, that’s the user. They signed up because they have a goal - to organize projects more easily, learn Spanish before a trip, design a website without code. Your job is to recognize that goal early and make it feel achievable.

As positioning expert April Dunford puts it:

Customers don’t care about your unique features, they care what those features can do for them.

Your onboarding should answer that question immediately: “Will this help me do what I came here to do?” If users don’t feel that clarity, they’ll leave.

Set up the user’s mission right away. One effective tactic is to let users define their goal during sign-up.

Duolingo nails this. It begins by asking learners to set a daily goal (5, 10, or 15 minutes) and choose their reason for learning. This simple act personalizes the experience and reconnects users with their motivation. Now the product isn’t a teaching vocabulary, it’s helping someone prepare for travel, reconnect with family, or grow their career.

Notion takes a similar approach. In the sign-up flow, users select how they plan to use the tool (personal notes, team collaboration, etc.). The product then tailors example content accordingly. It’s a small shift with big impact. Instead of showing off features, the product mirrors the user's goal back to them.

And it works. Products that clearly align with a user’s intent are far more likely to earn engagement. 73% of customers expect companies to understand their needs and when that expectation is met from the start, trust builds.

So in this first chapter of onboarding: introduce the user as the hero. Show you understand what they’re here to do. Then give them a fast, motivating glimpse of success.

2. Conflict (Problem a User is Trying to Solve)

No hero’s journey is complete without conflict. In onboarding, that conflict is the friction between the user's intent and the product's learning curve. Will they feel empowered or overwhelmed?

As a builder, you might be tempted to remove all friction in the name of “seamless UX.” But here’s the thing: zero friction doesn’t mean zero drop-off. In fact, a bit of well-designed friction can make users more engaged, not less. The goal shouldn’t be to eliminate all conflict, it’s to help users overcome it.

A poor onboarding experience drives people away. 74% of users will abandon a product if setup is confusing. Research suggests that when users invest some effort early on, they’re more likely to stick around. Why? Because making progress through friction creates ownership. Struggle, then clarity - that’s how belief forms.

The key is purposeful friction. Not random hurdles, but small, empowering challenges that teach by doing.

Take Notion. New users land on a blank page with a checklist like: “Type ‘/’ to add something.” When you follow that tip, a command menu opens and suddenly, you’ve unlocked a core mechanic. No tutorial, just trial. That’s not friction that frustrates. It’s friction that rewards. Duolingo does something similar. After sign-up, you’re dropped right into a lesson - match a word, translate a sentence. You try, you guess, you get feedback. You’re not reading about language learning, you’re already learning it. That early activity is designed to trigger the goal-gradient effect, when people see themselves making progress, they become more motivated to continue.

When users solve a small challenge, they feel pride. That feeling attaches to the product. This is active learning and it builds confidence. Users move from “What does this do?” to “I figured this out,” and that creates emotional momentum.

There’s another layer here, the IKEA effect. People value things more when they’ve helped build them. In onboarding, even tiny efforts like customizing a profile or completing a checklist can increase attachment. But it only works when the task is meaningful and achievable.

And if you want users to stick with onboarding, consider leaving a few tasks unfinished. The Zeigarnik Effect explains that humans are more likely to return to incomplete tasks because our brains hate open loops. A checklist with 75% completion nudges users back in. It’s the same psychological itch that cliffhangers exploit because our brains crave resolution.

The best kind of friction doesn’t block users, it pulls them forward.

Let users struggle just enough to feel capable.

3. Guide (Product)

In every hero’s journey, when the going gets tough, a guide appears like Gandalf did for Frodo. The guide helps the hero succeed. In onboarding, your product plays this role. It should offer support and guidance to reach the goal.

A great product doesn’t flood the user with advice. It offers the right nudge, at the right moment, in the right way.

One way to do that is through contextual coaching. Tooltips, prompts, and micro-tutorials can be triggered by user behavior, showing up exactly when they’re needed. In Figma, as you explore the interface, brief tooltips appear like “Press R to draw a rectangle” paired with small animations that show the action. This type of guidance is effective for two reasons:

1. It adapts to different learning styles (reading, seeing, doing).

2. It respects user autonomy. You can follow the tip, skip it, or move at your own pace.

Personalization is another key trait of a great guide. The best products shape the path based on who the user is and what they want.

Notion does this during onboarding. If you say you’re in marketing, the first templates you see are campaign trackers, not student notes or journals. That’s a nice touch: “We know what you need. Start here.” It reduces decision fatigue and accelerates the path to value.

Great guides also provide feedback and encouragement. Small moments of acknowledgment like a checkmark, a progress bar, a “nice work!” tap into feedback loops that reinforce learning. Behavioral Science tells us that immediate feedback makes people more likely to keep going. It turns effort into momentum.

And the guiding role doesn’t have to stop after the first session. Email sequences, embedded tutorials, and contextual help can all extend the guide’s presence. As long as they’re useful and timely. Irrelevant advice feels like spam. But the right message at the right moment can re-engage a user who got stuck or distracted. Especially if it’s paired with social proof or a clear next step.

Your product should feel like a helpful companion. Always available, never in the way. The user should think: “I’ve got this. But if I don’t, help is right there.”

Show the way, but let the user walk it.

4. Climax (Aha Moment)

Every hero’s journey builds toward a turning point, the moment where everything clicks. In onboarding, that moment is the user’s “Aha!” It’s when they first feel the value your product promised. That flash of recognition is behavioral and it drives retention.

Users who hit that insight early are far more likely to stick. Your job is to engineer onboarding so this moment happens fast and hits hard.

Let’s look at what that looks like in real products: In Trello, dragging a card to “Done” and realizing, “My workflow is now visual and under control.” Using a template or linked database in Notion and thinking, “This can replace three other tools.” With Figma you click through a prototype and think, “Design and testing are happening in one place.” In each case, the user completes an action and feels something powerful. It’s clarity, confidence and momentum. That ties directly to their original goal.

So how do you make it happen?

Start by identifying your product’s core value action, the thing users need to do to truly “get it.” Then remove anything that delays it. If the ‘aha’ is creating a 5-task project board, don’t ask for billing info first. Don’t show a settings tour. Just get them to that outcome.

And when they reach it? Make sure they notice. Don’t assume users always realize when they’ve done something meaningful. A budgeting app, for example, might say, “In 60 seconds, you auto-categorized 100 transactions and spotted $200 in savings.”
That’s clarity. That’s emotional payoff.

Now, not every product can deliver real value instantly. Some tools only shine over time or with more data like analytics dashboards or social platforms. In those cases, simulate the aha. Show sample data. Preload a template. Let users see the payoff before they’ve earned it.

Sprout Social did this by loading demo content so users could explore reporting features without connecting their real accounts. That glimpse of future value builds anticipation: “Ah, this is what I’ll get once I’m set up.”

Another way to trigger aha? Show users they’re not alone. For products with a community layer, onboarding should include cues that signal activity and belonging. A fitness app might say, “Your friend Alice just finished a 5K” or “Join 5,000 others in this month’s challenge.” That moment of recognition “people like me succeed here” can be just as motivating as a core feature.

By the end of this stage, the user should have achieved something (no matter how small) and felt the emotional lift of progress.

The hero has crossed the threshold. They’ve tasted success. Now they want more.

5. Resolution (First Win + Next Step)

The hero’s journey doesn’t end at the climax. It ends with resolution. A return, a shift, or a next mission. In onboarding, this means locking in the user’s first success and guiding them into meaningful, ongoing use. The “Here’s what’s next.” phase begins.

Start by celebrating the win. This isn’t fluff, it actually taps into the brain’s reward circuitry and reinforces habit. When a product makes users feel capable, they’re more likely to return. It’s classical conditioning. Pair the product usage with positive outcomes, and users will come back for more.

But celebration isn’t enough. After success, users naturally think: “Now what?”
Without clear next steps, they stall. So use this moment to project the next chapter. Just like stories often end with a glimpse of what’s ahead (“…and the heroes set off on more adventures”), onboarding should suggest the next action. For example: Trello, after showing a sample board, prompts users to create their own and Notion encourages users to import real content or try a new template. These nudges say, “You’ve got the basics. Now build something that matters to you.”

And while you’re doing that, reinforce the value they’ve already received. A quick recap like “In 5 minutes, you did X, achieved Y” helps users recognize their progress. That reflection can come via a modal, a sidebar message, or even a well-timed email: “You’re all set up! Here’s what you accomplished and here’s what to try next.”

This is also the right time to softly introduce any conversion path. A hard paywall here often backfires. But a gentle, contextual prompt can work well especially if it’s tied to the user’s goal. For example, “Want to bring your team in? Upgrade to Pro for unlimited collaborators.” That message is obviously selling a plan. But it’s perceived as offering a new capability. It feels like a continuation, not a disruption.

It’s also very important to extend the sense of support. Onboarding might be done, but the user journey isn’t. Let them know where to go for help, community, or deeper learning: “Need help? Visit our Knowledge Base.”, “Join our next webinar.”, “Talk to an expert.”

Even if they don’t act immediately, knowing support is nearby builds trust. Some products go further with personal outreach like a “how’s it going?” email or pairing new users with a customer success rep. These touches create a sense of partnership with a rapport.

In a world where most onboarding feels rushed or generic, support at this stage becomes a differentiator.

By now, the user should feel confident, supported, and already successful, however small that success might be. And when that happens, something powerful kicks in: they begin telling others.

Their journey continues. They may not be a user but they’re an advocate now.

The hero becomes the guide.

Checklist for Product Onboarding

Crafting narrative-driven onboarding might sound abstract, so here’s a practical summary to anchor your approach:

• Make the customer the hero, your product the guide
  Frame all messaging around the user’s goals. Avoid centering the story on your features, instead, map them to what they unlock for the user.

• Hook them fast with value
  Like a Pixar film, grab attention early. Provide a quick win or a clear glimpse of the end benefit in the first few minutes. Answer their unspoken question: “What’s in it for me?”

• Keep it simple
  Minimize cognitive load. Use plain language. Make the next step always obvious.

• Use progress to build momentum
  Break onboarding into small steps. Celebrate each one. Show progress visually. This taps into the goal-gradient effect.

• Design for the ‘Aha’ moment
  Identify the one action that best delivers your core value. Drive users there quickly, and make sure they recognize it when it happens.

• Add social proof
  Show success stories, real user activity, or community moments. People feel more confident when they see others succeeding.

• Test and evolve the story
  Collect qualitative feedback. Study where users drop off. Improve clarity, adjust friction, and refine your flow like it’s a narrative in beta.

By approaching onboarding as a storytelling exercise underpinned by basic human psychology, you do more than train users. You engage them, inspire them, and set them up as the hero ready to conquer their goals with your product.

When users feel seen, capable, and guided, they stick around to succeed. And in the end, that’s the real win-win: your users achieve success, and your product achieves adoption.

But when I saw that, I remembered Hinge (the app). By then, I had already deleted Hinge, following its infamous tagline - “Designed to be Deleted.” But even after that, the app was a part of my subconscious. It was branding baked into my memory. That moment was weirdly nostalgic.

Emotions dramatically influence memory retention and loyalty. Neuro-imaging studies show that emotionally charged marketing campaigns activate the amygdala, the brain’s emotional center. When you emotionally resonate with a brand - you become a loyal customer, an unpaid brand ambassador, so to speak. I may not be a customer anymore but I’m definitely a success story.

How does Hinge stand out if it’s basically just another chat app for singles?

The Economics of Hope

Let’s be honest, most dating apps are barely-functional-dollar-stores disguised as “connection platforms.” The stats are bleak. Industry-wide, only about 10% of users find a long-term partner. Hinge? Roughly 5% according to a survey done by Shane Co. That’s worse than your odds at a Vegas table (a good 35%-50%). But still, people flock to it. Why? Because marketing sells possibility, not probability. It sells the dream of being in the lucky minority.

Hinge doesn’t succeed because it matches people better, it succeeds because it markets hope better. And this hope is addictive. That’s the essence of positioning - it creates meaning where none inherently exists.

And, based on the latest financial data, Hinge is making some serious money. In 2024, it generated an impressive $550 million in revenue, marking a 39% increase from 2023. With 30 million active users and 1.5 million paying subscribers (a 23% year-over-year increase), the app has made hope into a lucrative business model. Each paying user generates an average of $29.94 in revenue, contributing to an adjusted operating income of $166 million, a healthy 30% profit margin.

Based on Hinge’s 2024 actuals, upcoming global launch of the revamped algorithm, and expanded market reach, it’s reasonable to project that in 2025, Hinge could reach 1.8–1.9 million payers at an average revenue of about $31–32 per user, driving direct revenue up to roughly $680–700 million. All this from an app with a <5% success rate.

Positioning in a Sea of Sameness

Hinge’s tagline is brilliantly simple: "Designed to be Deleted." In this competitive market of apps that want to maximize screen time, Hinge positions itself as the app that wants users to succeed and leave. It’s so original and aligns directly with the user’s goal - finding someone meaningful enough to delete the app for.

Talk about competition, Tinder thrives on hookup culture with gamified swiping mechanics, Bumble empowers women to initiate conversation. But Hinge openly acknowledges the user’s ultimate desire. While Tinder and Bumble operate on swipe, match, ghost, repeat, Hinge sells itself as a solution to the problem that others created. It’s like an antidote of the dating-app fatigue. Its core functionality (profiles, photos, messaging) is fundamentally similar, but the positioning completely flips the perception. It speaks in calm, nurturing tones, the kind that tricks you into thinking you’re making rational choices. Its interface is clean, minimalist. No neon-colored signs, just soft colors, personality prompts, and the promise that you are a smart, relationship-minded adult rather than a dopamine-addicted cat in heat.

Hinge effectively signals that it values long-term happiness over short-term engagement, which makes people more willing to believe in the brand​. The company doubles down on this theme across all its messaging, even creating a mascot ‘Hingie’ that celebrates its own deletion with cute self-destruction. The dark humor just reinforces Hinge’s authenticity and by daring to say “success means we disappear,” Hinge has set itself apart from the pack and strengthened its brand equity.

Marketing Creates Meaning

Hinge isn’t the only one that focuses on creating emotional loyalty.

• De Beers’ “A Diamond is Forever” stands as the longest running marketing campaign of all time, having started in 1947. It’s successfully linked diamonds with eternal love and is responsible for making diamond engagement rings a culture.

• Apple’s iconic silhouette iPod campaigns never mentioned storage capacity or battery life. They sold the feeling of dancing to your own soundtrack.

• Nike’s “Just Do It” goes beyond shoes, becoming your best self, overcoming limits.

These campaigns work because they tap into something fundamentally human. They understand that people don’t make decisions based on features or specifications, alone. They make decisions based on how something makes them feel.

The Human Element That Can’t Be Engineered

Despite innovative products, countless tech startups remain bland and interchangeable in the public eye. The reason? They focus on what they do, not why they do it. Tech companies pour resources into engineering and user acquisition, but treat branding as an afterthought – maybe a logo here, a quirky Twitter (now X) voice there. Technical solutions solve functional problems but they’re not equipped to make people care. This leads to weak or inconsistent identities. A user might be unable to distinguish one SaaS tool from another because neither communicates a clear personality or mission. In fact, a lack of meaningful differentiation is a leading cause of startup failure.

In my career so far, I’ve seen a handful of brilliant products fail. They didn’t lack any technical excellence, but because nobody cared, they didn’t work. This highlights the common blindspot in startup culture. Founders and investors obsess over scalability and total addressable markets but rarely ask: “Will someone genuinely care enough to use this?”

Humans are wired for stories. Facts and features might pique interest, but stories stick in our minds and stir our emotions. In the battle for user engagement, storytelling is the secret weapon tech brands often underutilize. A compelling brand story gives users something to believe in and belong to. Consider how Hinge’s story (“we exist to help you find real love”) elevates it from the rest. Users who resonate with that mission feel a sense of alignment. That’s a far deeper engagement than one driven by habit or convenience. As one PR expert said, “people don’t want to be sold to; they want to hear a story” – a narrative that connects with their own needs and dreams​.

When brands deploy storytelling, whether through their origin story, customer success stories, or even the way they frame problem-solution, they activate emotions that drive action. Research has shown that while information makes us aware, emotion is often what motivates decision-making and loyalty.

Lessons from Hinge

After all of this ruminating, this is what I know:

• Align with a real human problem: Find the emotional pain point in your user’s life and make addressing it your rallying cry.

• Dare to be different (even counterintuitive): Don’t be afraid of a value prop that seems to undermine your own usage metrics if it earns user trust.

• Bake brand values into the product: Every touchpoint should echo your core story.

• Harness social proof and stories: While humanizes the brand, it also provides evidence that you deliver on your promises.

• Stay culturally tuned-in: By engaging with the zeitgeist (whether it’s pop culture or addressing societal shifts, like remote work), you ensure your brand feels relevant.

Full Circle Moment

That SATC moment felt full circle: a five-letter word for ‘to bring together’ had, indeed, brought me to the person I’d marry. That word, that brand - Hinge - will live rent-free in my head for years. It created a connection that went beyond the functionality of the product itself.

So, to all product builders out there: Build something that works. But more importantly - build something people care about. As Bryan Eisenberg says:

Facts tell, but stories sell.

I was one coffee, one protein shake and one coconut water deep, fingers twitching over the keyboard, when the dreaded thought struck me - was I even needed anymore?

(Disclosure: I write for work and I love doing it. I love reading, analyzing, getting into the rabbit holes of minor details. The whole process of comprehending and creating something meaningful. And so, yes, I use AI (extensively). I use it for research, for generating ideas and brainstorming. It’s phenomenal at those.)

Content Writing has been hijacked. Not by the usual suspects (no Mad Men execs in suits whispering about ad-spend over whiskeys, but by something colder and stealthier. An AI that never sleeps, never questions (unless asked to) and never drinks. Initially, everyone hoped for it to be an assistant but we all know it’s become a relentless content-churning beast.

In 2022, CIFS expert Timothy Shoup estimated that 99% to 99.9% percent of the internet's content will be AI-generated by 2025 to 2030. That raises a question: even if we could verify what’s human-made (say, with blockchain certificates), will future audiences even want human created content?

Imagine you want to watch The Silence of the Lambs but this time it’s starring Jack Nicholson. Which, by the way, would be awesome. But think about it, today with tools like Sora, Alibaba’s newest thing, if you can create a video from scratch, it’s only time when you can “remaster” the existing media in ways unimaginable.

So the question was this: Should AI be an assistant or a creator? Or is it already too late to decide?

The Promise of the Machine

At first, it seemed harmless enough. Tech leaders said that the AI promises efficiency - the marketing world's most sacred buzzword. It would take the grunt work off our hands, they said. Just feed it a few keywords, and it would spit out a blog post, a Twitter thread, a perfectly optimized SEO article.

I tested it myself, as we all do, one way or the other.

“Write a product launch blogpost for an AI-powered note-taking app.”

In flat 4 seconds, the machine spat out 1281 words of corporate enthusiasm so lifeless it made a rom-com feel like a Kubrick.

But it worked. It was technically coherent and definitely fast. It was as if it had phrases from every generic press release ever written. And oh, the buzzwords and clichés! The draft was full of overused and hacky lines (“game-changer”, “revolutionize”, “disaster waiting to happen”, etc.) and bland metaphors.

It had no personality.

The content was on-topic, grammatically correct, and required no coffee, no salary. In late 2024, eMarketer reported 81% of B2B marketing teams now use generative AI tools, up from 72% just a year before. It’s easy to see why companies find this appealing. Why pay a human writer for days of work when an AI can produce passable text in seconds? This is the great lure of AI in content creation: insane speed and volume. Entire marketing departments are made to do more with less - more posts, more ads, more blogs but much fewer people.

But Does sheer volume mean anything if the content lacks depth and originality? AI-written prose often uses certain phrases over and over to the point of monotony. It tends to stick to a formula. The templated AI content is quite redundant and repetitive, you can spot it when you see it as something you’ve seen a hundred times before​.

The Great Marketing Hoax

Chasing that promise of more content for less cost, businesses fell for what I’d call a marketing hoax. In their quest to squeeze more productivity out of fewer people, companies started to treat AI as the panacea. The goal is to feed the bottom line. We’ve seen content teams downsized and budgets slashed. Content Writers are gutted.

AI doesn’t research. It scrapes. It doesn’t analyze. It assembles. It doesn’t form opinions. It regurgitates them. Yes, it can scan millions of articles and stitch together a summary, but can it have an original viewpoint? Can it connect the dots between a niche market trend and a cultural shift? Can it reveal an unspoken pain point before the audience even realizes it themselves?

No. Because it doesn’t think, it just processes. All that requires human judgment, the kind that comes from real-life experiences and intuition that no algorithm has (yet). AI, in its vacuum of mechanical processing, cannot (so far) replicate the originality born of a human mind.

Also, AI doesn’t know truth from falsehood. It can sound authoritative, but it has no conscience or real-world awareness behind its words. This has led to some embarrassing and harmful mistakes. Just a month ago, a BBC experiment showed the scope of this issue when journalists posed 100 factual questions to top AI chatbots, 51% of the AI’s answers had significant issues, and 19% contained outright factual errors​. The bots even made up information, fabricating quotes or details that sounded plausible but were completely false. In 13% of responses, quotes attributed to BBC sources were altered or nonexistent.

Tech outlet CNET tried using AI to write financial explainers, only to find errors in more than half of them. They ended up issuing corrections on 41 of 77 AI-generated articles. Worse, some of those passages weren’t entirely original, the AI had plagiarized by piecing together phrasing from its dataset​. The incident was a black eye for CNET, underscoring how unchecked AI content can wreck a publication’s credibility.

So, volume isn’t value. Companies are mistaking prolific production for actual business value. When every company blog and social feed produces the same banal AI-crafted lines, the audience stops listening. The end-user is a human and on a human level, the only content that matters is the kind that has something worth saying.

And no machine so far has the intelligence to know what that is.

The Thin Line Between Assistant and Overlord

AI isn’t the enemy. Misusing AI is.

For all its flaws, AI is an extraordinary tool. In the right hands, it’s an enabler. I I think it’s fantastic for research, capable of scanning huge amounts of data and discovering connections and nuances that can take a lot of human hours if not days. It can summarize dense reports, pull together industry trends, and help see a bigger picture faster than ever before. For writing, it’s great for sparking ideas, generating fresh angles and narratives. It can brainstorm without bias, proofread like a seasoned editor, and help tighten up language.

But, it’s still a tool, not a thinker.

The real danger isn’t the tech itself. It’s the human willingness to give up authenticity in favor of convenience. The moment we let the machine dictate what we write, it changes how we think. As of last year, 57% of all web-based text has been AI generated or translated through an AI algorithm. If this interests you, check out the Dead Internet Theory if you haven’t already.

We’ve become editors of machine output. We’re detached, passive, and watching as our work becomes indistinguishable from the algorithmic gunk coming out of a thousand other AI-powered content mills. I guess the key here is balance. For example, the Associated Press has “strict” guidelines - they allow AI for helping draft or research, but forbid publishing AI-written news without human vetting. AI can accelerate the process, but a human must remain responsible for the final product.

It’s also becoming clear that audiences want authenticity. A recent survey in 2025 found that 62% trust news from human journalists while 48% trust AI-generated news. That’s a big trust gap. People might enjoy playing with ChatGPT, but when it comes to information that matters, they still prefer a human behind it. Customers (like you and me) don’t want to feel like they’re“talking to a machine”and they’ll quickly wonder if a company that serves up cookie-cutter AI content is equally indifferent about its products and customers​.

Then there’s transparency and ethics. Should readers know when content is AI-generated? The answer is still evolving. Some disclose it, others don’t. CNET’s 2023 fiasco proved that the real damage came from secrecy. They published AI content without disclosure, only admitting it when exposed. Trust was broken, and they scrambled to add disclaimers to regain credibility. The takeaway? Be upfront. Maybe you don’t need a giant AI label on every blog post, but your team and clients should know where AI was used. But in journalism, finance, or any high-stakes industries, disclosure isn’t optional.

So, how do we ensure AI remains an assistant rather than becoming an overlord of content?

• Always Fact-Check– No matter how slick the output reads, verify every factual claim. AI models have a habit of “hallucinating”.

• Infuse Human Insight and Voice – Don’t let the AI have the last word. Treat the AI’s draft as a rough cut. Then add your own examples, insights, and personality in the edit. Give it your distinctive voice and perspective.

• Optimize for Speed – Leverage it for what it does great - generating quick drafts, outlines, or filling in basic content structures.

Use AI as a facilitator, not the one in charge.

But, Can AI Truly Create?

For AI to be a true creator, it would need something beyond computation, beyond pattern recognition, beyond predictive analytics. It would need a reckless, unfiltered, beautifully flawed human instinct - the kind that makes Paul Graham write a 1200-word rant at probably 2 a.m. about how hoarding stuff ties to societal attitudes toward material goods and how this shift affects personal well-being.

It would need emotion. It would need doubt. It would need curiosity that turns a simple question into an all-nighter of research. It would need to go through the process of wrestling with an idea, reshaping it, abandoning it, resurrecting it, and then obsessing over whether it was ever good in the first place.

It would need unhinged originality.

AI doesn’t have human drives. It doesn’t lie awake thinking about why a story matters. It doesn’t feel the thrill of inspiration or the regret when something doesn’t feel true. It doesn’t get writer’s block, but it also doesn’t get writer’s bliss. It never experiences the world, never feels joy or sorrow. It simply executes.

But it does not create.

Are We Feeding the Beast?

And yet, even as I type this, I know the ugly truth. AI isn’t stopping. In fact, it’s accelerating.

Companies will keep pushing for more automation, more efficiency, more content, all at scale. Every brand voice flattened into an artificial monotone. Every marketing campaign a slightly tweaked clone of the last. Every bold idea scrubbed clean of risk, spontaneity, and anything remotely human.

Maybe the real question isn’t whether AI should be an assistant or a creator. Maybe the real question is: How long until it doesn’t need us at all?

And when that day comes - Will anyone notice?

I want to talk about the four key biases dominating consumer decision-making, especially in the digital product world: anchoring, confirmation bias, loss aversion, and social proof. For each, I’ll explore a real-world example, unpack the psychology behind it, and extract the actionables for product marketers and business leaders on how to ethically apply or counteract these biases.

Anchoring – Setting the Price (and Mindset)

When Apple released the iPhone X with a $999 price tag, even loyal fans did a double-take. A thousand dollars for a phone?! The steep price made headlines and drew skepticism. But here’s the punchline: the iPhone X became Apple’s top-selling model each week in the quarter after its launch​. By the time Apple rolled out the next models (XS, XR), that $999 had primed consumers’ expectations. Suddenly the $749 price on the iPhone XR felt like a deal, “affordable” by comparison​. Apple had "anchored" us to a high number, resetting our internal idea of what a flagship phone should cost.

Anchoring is our tendency to rely heavily on the first piece of information we get (the anchor) when making decisions​. In the context of pricing, the first number we see lays down a reference point in our mind. Subsequent prices will be judged relative to that anchor, not on their own merit. Behavioral economists demonstrated this dramatically in experiments: give people a random number (say, the last digits of their phone number) and then ask how much they’d pay for an item, and those with higher random numbers willingly pay substantially more than those with lower numbers​. We’re THAT suggestible. So in Apple’s case, $999 served as a high anchor that made anything less look reasonable.

From a product strategy view, anchoring can be a double-edged sword. Here’s how tech companies leverage it or guard against it:

• Set a smart anchor for value: If you offer a premium plan or high-end product, presenting it first can anchor users’ expectations. For example, a SaaS platform might showcase a $199/month “Pro” plan as the first option, making the $99 “Standard” plan look comparatively budget-friendly. The key is that the premium option must have real added value. Anchoring works best when it’s believable, not seen as a pricing trick.

• Use comparisons to re-anchor: If customers are anchored on an outdated reference (say, a competitor’s lower price), combat it with context. Highlight differences in quality or total cost of ownership to reframe the comparison. For instance, Apple justified the $999 anchor by positioning the iPhone X as a leap forward in technology (OLED screen, Face ID). It was more than just a new smartphone, it was a new paradigm, helping consumers accept a new price norm.

• Beware of deceptive anchors: Ethically, avoid anchoring customers on false information (like an artificially high regular price just to show a big discount). It's a shady practice and savvy consumers easily see through it, eroding trust. Anchors should be honest reference points that help users make a choice, not pressure them. Product marketers, managers or anyone who is involved in 'Pricing and Packaging' should ask, “Am I anchoring users to a helpful benchmark, or simply manipulating perception?” Being on the right side of that line builds credibility long-term.

Confirmation Bias – Hearing What We Want to Hear

Ever notice how some die-hard tech fans seem to live in a different reality? Show an Apple loyalist a dozen positive iPhone reviews and one critique - they’ll zero in on the positives and dismiss that lone negative as nonsense (guilty as charged). But an Android buff will do the exact opposite. In the digital age, it’s easier than ever for consumers to wrap themselves in feedback that feels right to them, while tuning out the rest. A customer might literally ignore negative reviews and focus only on positive feedback, reinforcing their decision to stick with that brand​, product or service. They’ll say, “See? All these people love it!” and rationalize away any complaints as outliers or haters. This is confirmation bias on full display.

Confirmation bias is our built-in filter that favors information confirming our existing beliefs​. We seek, interpret, and remember details that uphold what we want to be true, and we discount anything that conflicts. Psychologically, it’s a comfort mechanism, admitting we might be wrong is hard, so we skew the evidence until it fits our views. Online, this bias gets amplified. Algorithms feed us more of what we’ve liked or clicked in the past, creating echo chambers. Over time, your news feeds, recommendations, and even search results start to reflect only the worldview you’ve implicitly chosen. You end up in a “filter bubble”, seeing facts and opinions that reinforce your biases and rarely challenge them. One UX expert mentioned that consuming information aligned only with your view leads to a snowballing confirmation bias, you get more of the same, which further amplifies your one-sided perspective​.

For marketers, confirmation bias is tricky: on one hand, you benefit from armies of brand fans who will defend you and promote you because it validates their own choices. On the other, new customers doing research might only look for reasons to justify buying the competitor they already favor. How to handle this bias ethically and effectively:

• Provide balanced information: Transparency can counter confirmation bias. Instead of hiding negative reviews or critical information, acknowledge and address it. Smart companies like Amazon have their products display both 5-star and 1-star reviews prominently, which can paradoxically build trust. A prospective buyer who sees a well-handled critical review (“Yes, our battery life was subpar in early units, but we’ve since fixed it…”) may actually become more confident in the purchase. Don’t just feed the hype, give the full picture and let customers make an informed decision.

• Encourage exploration: To gently disrupt echo chambers, savvy product platforms might introduce, say, a “People who considered this also looked at…” with diverse options. If someone is researching your software, show them honest comparisons (even if via third parties or guides). This isn’t an invitation to send customers to competitors, it’s a chance to earn trust by saying “we believe in our value, here’s how we stack up.” Guiding users to compare features objectively can snap them out of knee-jerk, biased judgments and make your pitch more credible.

• Leverage bias ethically in marketing: Confirmation bias isn’t all bad, it also means satisfied customers will actively seek confirmation that they made the right choice with your product. Help them out! Highlight user success stories, case studies, community forums with happy discussions. This creates a post-purchase content ecosystem where customers can reassure themselves (and others) that sticking with your brand is smart. But never cross into misinformation. The goal is to support genuine positivity and loyalty, not to create a disinformation bubble.

Product leaders should also turn the mirror on themselves: internal team “groupthink” is just confirmation bias on a company level. Are you only paying attention to user research that praises your design and ignoring complaints? Intentionally seek out disconfirming evidence. It will hurt less to find and fix bias blind spots early than to have them blow up into crises later.

Loss Aversion – The FOMO

Few things motivate a person more than the threat of loss. In fact, we humans feel the pain of loss about twice as intensely as the pleasure of a similar gain​. Behavioral scientists call this loss aversion. Tech companies have become masters at triggering it, often in clever, win-win ways that nudge you to click “Subscribe” or “Buy Now” before you even realize it.

A classic example is the free trial strategy. Ever wonder why Spotify, Netflix, and virtually every SaaS platform happily give away a free month of premium service? It’s not charity, it's definitely not prosociality, it’s age-old psychology. They let you bask in the ad-free, feature-rich version of their product, knowing full well that when the trial ends, you’ll feel you’re losing something you’ve come to enjoy. Sure enough, a high proportion of trial users convert to paying customers, precisely because giving up those premium perks feels like a loss they want to avoid​. You’ve been endowed with a benefit, and now the thought of losing it hurts. This is sometimes called the endowment effect, once we have something (even briefly), we value it more highly​ and struggle to part with it. In the context of a free trial, the product has effectively become yours, and the company is banking on you not wanting to give it up.

Loss aversion marketing isn’t limited to trials. Amazon’s Lightning Deals are a great example of tapping our FOMO. Visit Amazon’s deals page and you’ll see items with a bold timer ticking down and a bar that says “74% claimed” in blazing orange. The message is clear: "If you don’t grab this deal now, you’ll lose the chance at these savings". That anxiety you feel as the clock winds down is loss aversion in action, the potential loss of a good deal compels you to act fast. Consumers often jump on these time-limited offers not necessarily because they needed the item right then, but because the thought of missing the bargain feels like a loss they can’t accept. The same principle is at work when you see “Only 2 left in stock!”.

Let’s break down the psychology: loss aversion means that losing something (or even the chance to have something) cuts deeper than gaining something of equal value. Nobel laureate Daniel Kahneman’s research quantified this imbalance, showing that, psychologically, the pain of losing $100 is about twice as strong as the joy of winning $100​. In our brains, a looming loss is a fire alarm, whereas an opportunity for gain is more like a nice-to-have suggestion. Tech products use this insight by framing choices in terms of what you stand to lose. “Cancel anytime” sounds liberating, but the reason it works is that after a free month, canceling means forfeiting benefits you’ve gotten used to. It flips the decision from “Do I want to pay $9.99?” (a question about a potential gain of premium service) to “Do I want to lose ad-free music?” (a question about a definite loss if I don’t pay). We’re wired to avoid the loss.

Harnessing loss aversion can be powerful, but it must be done with care for an ethical user experience. Some tactics and tips:

• Let them try before they buy… and make it easy to love: Offering a free trial or freemium model shouldn't be a sampling tactic. It must be used as a way to create a sense of ownership. When the trial period ends, users should feel, “This is now a part of my life or workflow, losing it would hurt.” Ensure your onboarding during the free phase really helps users experience the full value. If they barely use the trial, they won’t mind letting it go. The goal is to have them truly adopt the product so that paying to keep it feels like a no-brainer​. This is an ethical win-win, the user only pays if they genuinely find value they don’t want to lose.

• Frame choices around loss (carefully): How you word an offer can leverage loss aversion. For example, instead of “Gain 100GB extra by upgrading,” a cloud storage service might say “Don’t lose your files. Upgrade to keep 100GB backed up safely.” The latter phrasing reminds users what they risk by not upgrading (the loss of security or data). Scarcity messages (“Offer ends tonight!” or “Limited slots available”) also play on this bias by implying a lost opportunity. These tactics do spur action​ but use them honestly. A false sense of urgency (“ONLY 5 seats left!” when it’s not true) can backfire with savvy customers and harm your brand.

• Soften unavoidable losses: Sometimes product changes will inherently create a sense of loss (like a feature removal or price increase). To handle this, acknowledge the loss and offer reassurance or alternatives. When a popular app had to kill a free tier, the company that handled it best by framing it as, “We know it’s hard to lose X feature, as a thank you for being with us, here’s a discount on the first year of the new plan.” They mitigated the sting of loss with a gain. If Amazon Prime’s benefits change, Amazon often adds something new (like expanding the video library) to offset what members might feel they’re losing. Product leaders should always empathize with what users might perceive as a loss and address it head-on, rather than hoping they won’t notice.

Make the user feel safe. Either they fear no loss because you’ve made staying easy and beneficial (trials that transition seamlessly, with reminders rather than sneaky charges), or if a loss is inevitable, they feel you’re on their side in minimizing it. Loss aversion can drive engagement and conversion, but it should never leave a customer feeling tricked or short-changed when the dust settles.

Social Proof – Herd Behavior in the Digital Wild

News cameras roll, pedestrians stare, and a thought naturally forms in observers’ minds: “Wow, that new iPhone must really be something if it draws a crowd like this.” This precisely is social proof. We tend to assume that if so many others want a product, it must be REALLY good. The crowd itself becomes a marketing message. Apple doesn’t explicitly advertise “We have huge lines every year!” because it doesn’t need to. The buzz and visibility of the crowd create a feedback loop: the line makes the product look irresistible, which in turn brings more people into the line (or convinces online fence-sitters to hit the “Buy” button).

Social proof is the psychological phenomenon where people look to others’ actions to decide their own​. In uncertain situations, we figure “if everyone else wants this, or everyone else is doing this, maybe they know something I don’t.” Psychologist Robert Cialdini identified social proof as one of his six key principles of persuasion for good reason. It’s incredibly persuasive, especially in the age of digital connectivity. Today, the tech equivalent of seeing a long queue is seeing metrics and testimonials on a screen; the 5-star average rating with thousands of reviews, the ticker that says “Join 5 million users,” the list of big-name clients on a SaaS homepage. It’s all sending the signal that people trust this product, so maybe you should too. And it works. According to consumer research, an overwhelming majority of people rely on online reviews to guide their decisions, and positive ratings serve as compelling social proof that a product is worth it (“This has great reviews, so I’m buying it”)​. In one survey, 92% of consumers aged 18-34 said they trust a brand more if it has good reviews. Quantity matters as well. A hotel or app with 1,000 reviews averaging 4.5 stars simply feels more reliable than one with 5 reviews at 5 stars, because the large number of others who have tried it reinforces that the rating is legitimate. We even see social proof spill over into behavior. Highly-reviewed restaurants tend to keep getting more reviews as new diners feel inclined to 'go with the popular choice', and their experience is already biased to be positive (“so many people liked it, I probably will too”)​.

For marketers and growth teams, social proof is a low-hanging fruit, if you’ve earned it, flaunt it. But know this - authenticity is king. Here are ways to capitalize on social proof while keeping it real:

• Showcase user love prominently: Make it easy for prospective customers to see how others have benefited from your product. This could mean featuring testimonials, case studies, or star ratings on your landing pages. For instance, many SaaS companies put a rotating carousel of customer quotes (“This software boosted our team’s productivity by 50%!”) front and center. E-commerce sites like Amazon highlight products as “Best Seller” or show “#1 in [Category]” badges, leveraging the wisdom of the crowd. These cues tap into herd mentality. If a lot of people choose it, new customers feel safer choosing it too​.

• Leverage statistics and milestones: If you have impressive numbers, share them. “Over 10 million downloads,” “Used by 70% of Fortune 500 companies,” “Join 100,000 subscribers”, these figures may seem like vanity but they reassure users that they’re not alone in trusting you. Social proof can also be real-time, think of the notification pop-ups on some websites that say, “Someone in Toronto just purchased this item” or “5 people are viewing this product right now.” There’s a fine line between motivating and pressuring, so use sparingly.

• Cultivate community and conversation: Social proof can come from community buzz. Encourage users to leave reviews, ratings, and to share their experiences on social media. Companies often gently prompt happy customers with, “Enjoying our app? Leave us a review!” More creatively, campaigns like hashtag challenges or referral programs turn customers into ambassadors. When people see peers (friends, family, influencers) endorsing a tech product, it’s even more powerful than anonymous crowds. A recommendation from a friend is social proof on steroids because it carries personal trust. Tech leaders should invest in user communities, whether it’s forums, Discord groups, or subreddits. A community provides support while signaling newcomers that “people like me engage with this product.”

• Keep it genuine: This should go without saying, but do not fabricate social proof. Astroturfing (planting fake positive reviews or buying followers) is unethical and, if discovered, can devastate your credibility. Modern consumers are skilled at sniffing out inauthentic behavior. A sudden jump to 500 glowing reviews all written in similar language will raise eyebrows and may violate platform policies. It’s far better to have fewer, REAL endorsements than a glut of phony ones. Also, be transparent. If you pay influencers or offer an incentive for a review, make sure that’s disclosed per guidelines. Authenticity is the currency of trust. Social proof is effective when it’s a true signal, not noise.

At its core, social proof should be about letting your happy users speak for you. It aligns the interests of the business and customers. You succeed by making your customers so satisfied that they voluntarily influence others to join in. And you, as a business, have tapped into word-of-mouth marketing. When done right, it doesn’t feel like marketing. It feels like momentum, consensus, and community.

Rethink and Reshape the Consumer Experience

The psychological levers of anchoring, confirmation bias, loss aversion, and social proof can boost conversion rates, engagement, and loyalty. But they can also deteriorate consumer trust if done wrong. The difference lies in intentions and ethics.

Rather than exploiting biases in a dark, hidden way, make the process transparent and user-centric. Product marketers and business strategists should be intentional and ask themselves: “Which cognitive bias might I be triggering here? Is that the experience I want for my user?” By designing with these psychological forces in mind, you can craft journeys that guide users ethically. It could be as simple as rephrasing copy to be more encouraging than scare tactic, or as deep as redesigning a freemium model to ensure a positive user experience whether or not the user converts.

Take a hard look at your product’s touchpoints through the lens of these biases. Do an audit; where are we anchoring, where might users be falling prey to confirmation bias, are we instilling FOMO appropriately, are we highlighting community feedback? Then decide if we're happy with the approach or it needs an overhaul? The goal isn’t to strip away all influence (that’s impossible and not even desirable), it's to align influence with value.

The psychology behind consumer decision-making in tech is both a craft and a responsibility. Yes, our users’ brains can be predictably irrational (like our own), but they are also trusting us to build products and campaigns that respect them. Let’s justify that trust and be more intentional in shaping consumer experiences.

In a world increasingly driven by AI recommendations, algorithmic feeds, and growth hacking, the human touch of ethical psychology in product design can be a true competitive advantage.

So next time you plan a new feature rollout or marketing push, remember the invisible forces at play in your users’ minds. Design your product pages and user flows with empathy for those mental quirks. By doing so, you’ll build a brand that users feel good about engaging with. Knowing that behind the slick interface, real thought went into respecting their psychology and their humanity. Your customers (and your conscience) will thank you for it.​

More than a decade has passed since Apple introduced a completely new product category that wasn't an accessory to the iPhone. In 2010, it was the iPad, a device that Steve Jobs declared as a revolutionary tool for browsing the web, reading, and more. Fast forward to today, and it's Tim Cook presenting the Vision Pro, a glimpse into a future where digital and physical realities bleed into each other, changing how we interact with technology, each other, and the world around us.

From Lisa to the iPad

To appreciate the significance of the Vision Pro, it's crucial to rewind and revisit Apple's storied history of innovation. The company's knack for disrupting the status quo dates back to the early '80s with the launch of the Apple Lisa. It was the first to introduce the world to a graphical user interface - a technology that seemed almost magical at the time, fundamentally changing how humans interact with computers. Then in 2010, we witnessed the introduction of the iPad, another groundbreaking device that Steve Jobs promised would fill the gap between laptops and smartphones. Each of these product launches was about unveiling new possibilities, opening doors to ways of interacting with technology that were previously unimagined.

Like its predecessors, Vision Pro is a portal to new experiences. By integrating augmented reality into our daily lives, Apple is adding to the roster of gadgets we own while proposing a new way of life. This tradition of pioneering is part of Apple's business model; it's woven into the fabric of its identity. Each product, from the Lisa to the iPad, and now the Vision Pro, essentially underscores Apple's belief in the transformative power of technology.

A Closer Look at the Vision Pro

At its core, the Vision Pro is an augmented reality headset, but to leave its description at that would be an understatement. This device combines cutting-edge hardware with sophisticated software to create an immersive experience that blurs the lines between the digital and physical worlds. With its array of cameras, sensors, and microphones, the Vision Pro is designed to augment reality in a way that feels both natural and intuitive, offering a glimpse into a future where our digital interactions are seamlessly integrated into our physical environment.

Beyond the technical marvels, the Vision Pro's astronomical price tag of three and a half thousand dollars is a deliberate choice by Apple. This isn't just to cover the cost of premium materials or the latest technology; it's positioning the Vision Pro as a luxury item, akin to all things Apple. This strategy is similar to the first iPhone's launch - priced significantly higher than its competitors, yet it changed the smartphone market forever. Apple's approach with the Vision Pro is to create a new category.

Apple's Master Plan?

The launch of the Vision Pro is a chess move in Apple's grand strategy of innovation. This device embodies a broader vision, one that Apple has been meticulously crafting for years. The Vision Pro's introduction is less about the immediate impact on the market and more about planting a flag in the future of digital interaction. Apple is selling a dream of what the world could look like.

This approach is so typical of Apple's historical ethos of not just participating in the market but creating new ones. Just as the iPad carved out a niche that many didn't even know needed filling, the Vision Pro aims to expand the boundaries of how we conceive of augmented reality. It showcases that Apple is once again ahead of the curve, anticipating the future needs and desires of consumers. The strategic release of such an advanced piece of technology, despite its current limitations and high cost, signals to competitors, consumers, and investors alike that Apple continues to lead the charge toward the next frontier of tech, shaping the future with each product launch.

The Challenge of True Augmented Reality

The Vision Pro, while a marvel in its own right, is only the first step on this ambitious AR journey. The quest for AR that seamlessly integrates with our daily lives, enhancing our interaction with the world without intruding upon it, is fraught with technical, ethical, and social challenges. One of the most significant hurdles is developing technology that can overlay digital information onto the physical world in a way that is both intuitive and immersive, without compromising the user's ability to engage with their environment.

The hardware required for such an endeavor needs to be unobtrusive yet powerful, capable of processing vast amounts of data in real time to render digital objects with lifelike precision and minimal latency. The societal implications of widespread AR adoption - ranging from privacy concerns to the potential for digital isolation - pose additional challenges that must be addressed. Apple's Vision Pro, with its emphasis on high-quality augmented experiences, sets the stage for tackling these issues. However, achieving a version of AR that can be as transformative as the smartphone requires technological breakthroughs and careful consideration of how this technology fits into the fabric of our lives.

The road to true augmented reality is lined with obstacles, but also with unparalleled potential for transforming how we interact with technology and each other. The Vision Pro serves as a milestone, showing both the possibilities and the challenges that lie ahead. The journey will require innovation, collaboration, and, most importantly, a vision that extends beyond the limitations of current technology to embrace the profound impact AR could have on society.

Market Dynamics and Consumer Response

The introduction of Apple's Vision Pro into the market is like throwing a stone into a still pond, creating ripples in the tech industry. Apple is definitely analyzing the immediate reaction it garners and also the undercurrents it stirs in consumer behavior and competitive strategies. With its eye-watering price tag, the Vision Pro navigates a delicate balance between exclusivity and accessibility, aiming to allure early adopters and tech enthusiasts while setting a benchmark for quality and innovation in augmented reality. This is exactly how the first iPhone was launched - despite its high cost, it managed to redefine consumer expectations and reshape the smartphone market.

The consumer response to the Vision Pro will be a critical measure of the market's readiness for high-end AR experiences. Initial skepticism regarding the device's price and utility mirrors the reception of many groundbreaking technologies that eventually found their way into the mainstream. Apple's track record of converting luxury into necessity suggests that the Vision Pro could follow a similar trajectory, gradually expanding its appeal as the technology matures and becomes more integrated into everyday life. Moreover, the Vision Pro's introduction may spur competitors to accelerate their own AR developments, leading to a flurry of innovation and new entries in the market.

Understanding Vision Pro's market dynamics requires a look beyond initial sales figures to the broader implications of its launch. It's about assessing how this device influences consumer expectations for technology, how it prompts competitors to respond, and how it shapes the future landscape of the tech industry.

The Future of Augmented Reality

The potential for AR to enhance education, revolutionize industries, and transform entertainment is immense. Imagine students exploring the universe from their classrooms, engineers visualizing complex machinery in real-time, or artists creating immersive experiences that blend the digital and physical worlds. The possibilities are as boundless as they are exhilarating.

Yet, this vision of the future comes with its own set of challenges and questions. How do we navigate the blurring lines between digital augmentation and reality? What implications does this have for privacy, when every surface can become a screen, and every moment can be enhanced (or interrupted) by digital content? Moreover, as AR becomes more integrated into our lives, the digital divide could widen, leaving those without access to the latest technology at a disadvantage. These are the hurdles that society must clear as we march toward a future where AR is as ubiquitous as the smartphone is today.

The journey toward this future will be marked by innovation, debate, and, undoubtedly, surprises.

Succession won 13 Emmys and I honestly couldn’t wrap my head around why. From the first to the very last episode, I couldn’t see the appeal. To me, it seemed like a bunch of 14-year-olds stuck in corporate America. The plot lacks purpose and recycles the same conflicts, all the “big” twists feel lazy and the characters have little to no arc. The show never earns the narrative depth its awards suggest.

But here’s the thing - there's a branding genius in its mess. And before we get into that, let's first unpack why Succession might not have struck the right chord with everyone.

Not All That Glitters Is Gold

1. The Surface-Level Spectacle
   The show often uses its score and cinematography to highlight the lavish lifestyle of the Roy family. The slo-mo shots of private jets and penthouse lobbies just made me hit the 30-second skip. These glossy fillers don’t offer any solid ground into the lives of the ultra-wealthy but rather indulges us into their opulence repeatedly, which just felt tone-deaf and excessive.

2. Oversimplified Business Portrayals
   I think people with a moderate understanding of corporate operations would find the show's depiction of business dealings and strategy overly simplified and unnecessarily dramatized. While it did try to capture the cutthroat nature of boardroom politics, it lacks the technical nuance and accuracy of real-world business complexities, making it look very breezy. On the other hand, think 'The Sopranos' which is one of the more accurate TV depictions of mafia life. The cast had real life gangster connections. This level of commitment to authenticity offers a far more granular and believable portrayal of their subject matter.

3. Dramatization Over Substance
   Authenticity lends a story credibility and weight. Shows like ‘The Wire’ and its depiction of life in the projects or ‘Breaking Bad’ for its scientific grounding. While Succession is indeed a drama series at its core, I’d still argue that it prioritized dramatic effect over authentic storytelling. The constant backstabbing, plotting, and family feuding get boringly repetitive and obnoxious. The stakes blur, episodes blend and the series drifts into soap opera territory with big budget and weak backbone.

4. Dialogue Without Gravity
   The dialogue in Succession tries to sound profound but falls short of delivering genuine value or insight. The characters are relentless with their quips and burns, though sometimes entertaining, mostly seem to trying too hard to sound clever and edgy. What could’ve been surgical plot building lands as a snark on social-media Another factor that damps down the seriousness and complexity of the narrative. Great TV dramas use dialogue to explore tricky social realities, build immersive worlds, and provide sharp social commentary.

5. No Character Development
   The characters show no depth beyond their initial introduction. They are somewhat ambiguous and show minimal growth. While I don’t think character “likability” is a necessity for a compelling drama, complexity is. Acclaimed characters like Walter White and Tony Soprano are multi-dimensional, offering psychological depth beyond typical protagonists or antagonists. Their complexity makes them fascinating and unpredictable.

And this is where the story gets more interesting. Because the show’s greatest narrative weaknesses are, ironically, the very source of its branding genius.

The Branding Genius in the Mess

So, how does a show that feels hollow on a narrative level succeed so spectacularly? The answer lies in its unintentional brilliance as a case study in brand strategy. The very aspects I found frustrating are, through a different lens, powerful marketing tactics.

1. Packaging is the Pitch
   The extravagant and captivating visuals are the packaging. Those shots of private jets, sprawling estates, and massive boardrooms are the hook. This strategy of pure aspirational glamor is the value proposition. In marketing, catching the consumer's eye is half the battle. Succession proves that a premium, irresistible presentation can often be more important than the product's underlying substance.

2. Simplicity Sells, Complexity Fails
   Sure, Succession oversimplifies the business world but at the same time it’s making it more accessible to more people. The series distills complex concepts like hostile takeovers, proxy battles into digestible drama. It makes the world of a media-conglomerate interesting to an audience that doesn’t know the first thing about leveraged buyouts. The key is to simplify your product’s complex features into an easy-to-understand value. If Succession can make media empires seem straightforward, you can make your SaaS product seem accessible too.

3. Narratives Define Loyalty
   The power plays and betrayals might seem overdone. But they proved the point that retention is rooted in the story you build. The show created a simple, repeatable loop - who will win, who will lose, who will betray whom? That kept viewers locked in season after season. For businesses, creating a compelling narrative around your product’s journey is the growth driver.

All of the above prove that a product can be its own best marketing campaign. But the real magic happened when HBO took that foundation and built a cultural trend around it.

How did HBO Successfully Market Succession?

HBO’s marketing team understood their product perfectly and sold its vibe.

• Mirrored the Brand on Social Media
  HBO partnered with Digital Media Management (DMM) for a social media campaign that emphasized intentional and consistent storytelling. Together they created a curated voice that matched the show’s aesthetic. The creative assets were polished and understated, making the show feel like an exclusive club you wanted to join. It was ruthlessly brand-centric and pushed the show as the luxury it portrayed.

• Built and Exclusive Community

  By avoiding the typical fan-service content, the social channels for Succession felt different, they were more elevated, more posh. The strategy was to engage the audience with content that resonated with the show's core tone of power and opulence. They would react to fan sentiment and highlight iconic moments, but always through their high-quality, curated lens. This built a powerful sense of community around the brand, making viewers feel like insiders.

The power of social media doesn’t have to be in broader engagement. It works better when it’s disciplined curation. They sold a status symbol. Success lies in embracing the core DNA of your brand with confidence to shape your market's perception.

Key Takeaways

1. Positioning is Everything
   The Roy family's brand is ‘power.’ What's your core identity? Defining that singular position is essential for standing out.

2. Adapt or Die
   The show's constant power shifts are a lesson in agility. The moment a character gets comfortable, they lose. In a market that's always changing, your product strategies should be flexible and innovative.

3. Perception is Reality
   The Roys believed that how things appear is more important than how they are. Just as the show creates a perception of power and success, ensure your product's market perception aligns with its value proposition. Manage how your product’s perception is shaping customer’s reality.

4. Know Your Audience
   Succession knew it was for an audience that enjoys gossip, appreciates "rich people problems," and values sharp dialogue over profundity. It catered to them unabashedly. Know exactly who your product is for and tailor your efforts accordingly.

5. Drama Can Be Good
   Every story needs conflict. A little well-crafted drama like a rivalry with a competitor, a bold claim, a surprising pivot, can go a long way in making your brand memorable and interesting. Craft your product's story to be engaging and memorable.

Love it or hate it, the true legacy of Succession lies in the brands it inspires to be bolder, clearer, and far more strategic.

Consider Salesforce's strategy in 2023. They moved from just selling a CRM software and reshaped their narrative to address how their platform helps businesses in uncertain economic times. It was a lot more than just another recurring biannual business strategy restructure. It’s still a valid lesson in rebranding and market positioning. It demonstrated how to communicate your product's evolution in response to customer needs effectively.

As we go through these trends, I'm here to share the real-deal strategies that worked (and those that tanked), and how we can use these insights.

The Economic Climate

In 2023, with budgets tightening and decision cycles lengthening, the way businesses approach software investments shifted significantly. This change revelaed two non-negotiables in the software industry: value and efficiency.

• Adobe pivoted its marketing to emphasize the cost-effectiveness and productivity gains of its Creative Cloud suite. They successfully communicated the value proposition of their products as more than creative tools. They positioned them as investments in efficiency and innovation during tough economic times.

• In a climate where remote work tools have become essential, Slack's focus on showcasing its platform as an enabler of seamless, efficient collaboration during working challenges resonated well with its audience. Their approach catered to the immediate needs of businesses looking to maintain productivity in a distributed work environment.

The key is to align the marketing strategies with the current market realities. The messaging needs to focus on the economic sensibilities and constraints of our customers. It's about contextualizing that your product is a wise financial decision.

Identifying Purchase Motivators

In 2023, the drivers behind software investment decisions became more sophisticated, influenced heavily by digital cohesion and the integration of advanced technologies like AI and ML.

• A significant trend this year has been the push towards digital transformation and integration. Early this year, Microsoft offered a solution to a common pain point of various silos of systems and applications. It launched Microsoft Fabric with its seamless integration capabilities. By positioning Microsoft Fabric as a unifying solution, they effectively tapped into a key purchase motivator: the need for cohesive, streamlined operations across diverse digital environments.

• Zoom expanded its category by incorporating AI-driven features like automated meeting summaries (which are quite impressive). Zoom also saw the potential of its category and started integrating with other productivity tools like Slack, Coda, Miro and Trello. This strategic move emphasizes Zoom's ability to recognize and capitalize on evolving market needs.

These executions required nuanced marketing approach. It's about balancing the narrative between innovation and continuity. It's about being relevant to both the existing and new stakeholders.

The Buyer's Journey

The software buying journey in 2023 maintained its traditional funnel, but it's how we approach these stages has seen significant evolution.

Awareness Stage: Beyond Making Noise
At the awareness stage, the focus has shifted from mere visibility to cultivating relevance. Take GitHub, for instance, they've transformed their platform from a repository to a community where developers discover new tools through discussions. It's a space where peer recommendations and influencer endorsements carry weight. For marketers (especially in B2B Tech space), this means engaging with these platforms, ensuring your products are well-represented where it matters. Ensure that your product exists in communities and networks where potential buyers are actively seeking opinions and advice.

Consideration Stage: Interactivity and Personalization
In the consideration phase, personalization is key. HubSpot's strategy of customizing website content based on user interactions is a perfect example of how to make the buyer's journey gratifying and tailored. Salesforce exemplifies this with its AI-driven personalization capabilities. By leveraging data from customer interactions and behaviors, Salesforce tailors its marketing and sales pitches to address the specific challenges and goals of each potential customer.

Decision Stage: Ethics and Sustainability
When it comes to decision-making, traditional factors like cost and functionality are now coupled with ethics and sustainability, ESG. Salesforce with their 1-1-1 model of giving back (1% product, 1% equity, 1% employee time) is often highlighted in their messaging, resonating with buyers who value corporate social responsibility. Google Cloud with their green computing emphasize on the energy efficiency of their data centers and their commitment to running on renewable resources. For businesses concerned about their carbon footprint, this becomes a compelling factor in the decision-making process, as it aligns with their own sustainability goals.

Demonstrating a commitment to ethical and sustainable practices can be a decisive factor for modern, conscious buyers, enriching the traditional journey with innovative, responsive, and ethical marketing strategies.

The Role of Reviews and Recommendations

Platforms like G2 and Capterra have become pivotal in the decision-making process. Buyers increasingly rely on peer reviews and user testimonials to gauge the real-world effectiveness of software solutions.

Influencer marketing has also taken a stronghold in the software industry. Thought leaders and industry influencers can sway opinions and decisions significantly. These endorsements lend credibility and often reach potential buyers who may be skeptical of traditional marketing channels.

The impact of detailed case studies and success stories are more influential than ever. They help in illustrating your software’s capabilities while building a trust factor among potential buyers. By showcasing tangible benefits and the ROI achieved by other users, these case studies provide concrete evidence of your product's value.

Community-driven recommendations, particularly in forums and social media groups like Reddit, Twitter etc., have become invaluable. These organic, unbiased opinions and endorsements add a layer of authenticity to your product's reputation, influencing buyer decisions in a way that traditional marketing often cannot.

Recognize the power of authentic, third-party endorsements and community feedback to enhance credibility and fosters a sense of trust that resonates with modern buyers.

The Impact of Security Concerns

In 2023, security dominated software selection, reflecting the increasing sophistication of cyber threats. The ability to showcase compliance with global standards and provide a robust security mechanisms is determinant in decision-making.

• Consider how AWS leverages its case studies on security standard compliance. These case studies are more than just marketing tools; they provide real-world examples of how businesses can address and overcome complex security challenges using AWS's cloud services. This approach effectively communicates the robustness and reliability of their security measures.

• Similarly, IBM’s offerings, known for their seamless integration into existing security architectures, cater to businesses seeking to enhance their security without overhauling their current systems. This ability to blend into and fortify existing security protocols makes IBM’s solutions particularly appealing to buyers who prioritize both advancement and continuity.

Prioritize and emphasize the security aspects of your software in your marketing strategy. Reassure potential buyers of your commitment to security and its effectiveness in countering cyber threats.

Budget and ROI

The dynamics of any purchase (software, or not) are budget considerations and the pursuit of a strong return on investment (ROI).

• Emphasizing Cost-Efficiency and Long-Term Value
  Gone are the days when upfront costs were the sole focus. Now, businesses are looking for software that aligns with their current financial constraints while also promising sustained benefits. The emphasis has moved towards assessing the long-term value and cost-efficiency of software solutions, recognizing that the cheapest option upfront may not always offer the best value over time.

• Demonstrating Tangible ROI
  Businesses demand clear evidence of how a software investment will pay off. Showing tangible ROI has become a cornerstone in software marketing. Companies are expected to articulate the features and functionalities of their products but they're assessed on how those translate into measurable improvements in efficiency, productivity, and ultimately, the bottom line.

• Flexible Pricing Models
  The trend towards flexible pricing models has gained traction, reflecting a market that caters to a diverse range of business sizes and needs. Software providers that offer scalable pricing options allow businesses to select a plan that fits their budget while ensuring access to necessary features and functionalities. This flexibility is a key factor in making software solutions accessible and appealing to a broader market spectrum.

Develop a value-centric sales proposition, clearly communicating how your software offers cost-efficiency, aligns with different budgetary needs, and delivers a strong ROI.

Key Takeaways:

• Understanding Evolving Needs: Stay attuned to the changing needs of the audience, whether it's a shift towards more integrated, AI-enhanced solutions, or a growing emphasis on security and sustainability.

• Emphasizing Value and ROI: In a budget-conscious environment, highlighting the long-term value and ROI of software products is essential. It's about showing how an investment in your product translates into tangible benefits.

• Leveraging Reviews and Community Feedback: Positive reviews and community endorsements have become vital in influencing buying decisions. Engaging actively with these platforms can significantly boost credibility and trust.

• Adapting to Technological Advancements: Staying ahead of technological trends and incorporating them into your products and marketing strategies can give you a competitive edge.

In a market where change is the only constant, the ability to swiftly reshape your product's story to meet evolving customer needs is invaluable. This approach is about leading the conversation and positioning your product as a solution that understands and adapts to the current challenges.

I think the Touch Bar was Apple's ambitious attempt to reinvent a stagnant part of the keyboard. Instead of a fixed row of function keys, they rolled out a dynamic strip of glass that adapted to whatever you were doing. The idea was to give you relevant controls, right at your fingertips, for any given app.

In a way, this move was classic Steve Jobs-era thinking. It was a bet on essentialism, stripping away the static and replacing it with something fluid and contextual. What this really means is they traded a one-size-fits-all solution for a tool that could, in theory, be perfect for any specific task.

This obsessive focus on the user experience has always been an Apple hallmark. It’s in the details, from the precise etching of the logo to the meticulously organized circuit boards of the original Macintosh. That kind of attention to detail, even on things most people will never see, is what builds a deeper connection with a product.

Innovation Ahead of Its Time?

Let’s look at why the Touch Bar’s reception was so divided. While some people loved the idea, many others found it awkward or just plain unnecessary. This is a classic example of the gap between a cool innovation and what people actually find useful day-to-day.

A big part of the problem was the steep learning curve and the lack of deep customization. For a lot of users, the familiar, predictable function keys were simply more efficient than the Touch Bar's new-but-shallow advantages. It was a solution in search of a problem.

This is where you see the "crossing the chasm" concept in action.

The Touch Bar absolutely intrigued the tech enthusiasts, early adopters, and the people who are always excited about the new gadget. But it stumbled trying to win over the mainstream. That group, the early majority, are pragmatists. They need a compelling, practical reason to change their habits, and the Touch Bar didn't offer one. It felt more like a gimmick than a genuine improvement.

From there, the resistance only grew. The late majority and the skeptics, who are naturally wary of change, had no interest in giving up their reliable function keys for something so different (and even confusing).

Ultimately, despite the clever design, the Touch Bar just didn't prove its worth to enough people, which is why Apple quietly phased it out.

Market Misunderstanding or Missed Opportunity?

So, why didn't the Touch Bar stick?

First, you're fighting against decades of muscle memory. Most of us are wired to use physical keys. The Touch Bar demanded we unlearn that, trading the certainty of a physical keystroke for a glance at a digital strip. That’s a big ask, and it required a shift in how people fundamentally interact with their laptops.

Then there are user expectations. People want technology that is both simple and personal. The Touch Bar tried to do both but ended up in a weird middle ground. It offered some customization but sacrificed the straightforward simplicity of the function row. A successful feature needs to feel intuitive, not like a trade-off.

Apple's storytelling also played a role. For a new feature to catch on, you need to clearly and compellingly show people why they need it. The narrative for the Touch Bar never really landed. Apple never sold a story that made its benefits feel essential, so for most people, it remained a curiosity rather than a must-have tool.

And maybe, the timing was just off. The market might not have been ready for such a futuristic concept. In technology, being too far ahead of the curve can be just as problematic as being behind.

This may seem like a story of a feature that failed, but it's actually lesson in the sophisticated relationship between people and their tools. The Touch Bar challenged the line between touching and typing, and its quiet disappearance tells us that an innovation has to be seamlessly useful now.

When you think about it, the Touch Bar was a precursor to the truly adaptive interfaces that AI and LLMs now make possible. The original idea of an interface that understands your context and adapts was pretty sound. It was just waiting for the technology to become smart enough to make it feel effortless. Maybe the journey of the Touch Bar was a step toward that more fluid, adaptable future.