Beyond Screens: A New Computing Paradigm?

OpenAI is no longer content with just powering AI experiences; they aim to be the experience. This strategic consolidation with Ive’s team signals a deliberate pivot to craft what many are dubbing the “third essential device”, an ambient, perhaps screen-less, companion designed to seamlessly integrate AI into our lives.

This raises a profound question: Could our decades-long reliance on screens, keyboards, and mice—the very interfaces that unlocked the digital age—ultimately be viewed as an extended, albeit necessary, fad? A bridge to something far more intuitive? The goal here seems to be nothing short of a “second brain,” an AI that augments our reality in ways current form factors can’t.

Learning from the Past: Hardware is Hard

The path to AI hardware is littered with ambitious attempts that haven’t quite hit the mark. We’ve seen recent examples like the Humane Ai Pin, which, despite its sleek design, faced criticism for usability issues, overheating, and a lack of compelling real-world application, ultimately leading to its discontinuation. [3, 4] Similarly, the Rabbit R1 generated initial buzz but struggled to prove its indispensable value over existing smartphones, with prominent reviewer MKBHD finding it “barely reviewable” in its early state. [5] These ventures underscore the immense difficulty of not just creating novel hardware, but of delivering a truly revolutionary and reliable user experience from day one.

Why This Time Could Be Different

Despite past stumbles in the sector, the OpenAI-Ive alliance is uniquely positioned. With Jony Ive’s unparalleled track record in creating category-defining, intuitive consumer electronics (from the iMac to the iPhone and Apple Watch) and OpenAI’s undisputed leadership in foundational AI models, this partnership possesses a rare synergy. If truly integrated AI hardware is to succeed, this combination arguably has the strongest potential to crack the code. A successful venture could:

• Revolutionize Human-Computer Interaction: Shifting from deliberate screen-based commands to intuitive, ambient intelligence.
• Redefine Personal Computing: Moving beyond the current device ecosystem dominated by a few giants.
• Unlock Unprecedented Personalization: Gaining deep, contextual first-party data to tailor AI in ways previously unimaginable.

The Multi-Billion Dollar Question: Execution is Everything

The vision is grand, the talent is undeniable, but the challenge is monumental. Success will demand more than just cutting-edge AI and beautiful design. It will require flawless execution, a robust ecosystem, and a product that solves real problems so elegantly that users can’t imagine their lives without it. This is about creating an entirely new experience, one that feels like a natural extension of ourselves.

References

• [1] NPR (2025, May). OpenAI buys io, former Apple designer Jony Ive’s device startup. 
• [2] AppleInsider (2025, May). OpenAI & Jony Ive’s AI necklace rumored to have iPod shuffle form factor.
• [3] Wikipedia (Accessed May 26, 2025). Humane Inc..
• [4] ComplexDiscovery (Accessed May 26, 2025). The End of Humane AI Pin: HP’s Strategic Shift Toward AI Integration.
• [5] YouTube – MKBHD (Accessed May 26, 2025). Rabbit R1: Barely Reviewable.

But this vision, however inspiring, is deeply misleading. It pulls attention away from the hard realities of building AI that works in the real-world. In guiding dozens of AI initiatives, I’ve seen the same five traps repeatedly sabotage progress—five persistent illusions that skew judgment and derail execution.

What follows is a breakdown of each illusion, how it manifests in real-world projects, and how to counter it. To help you assess your own perspectives and strategies, each illusion is followed by a “ Reality Check” section containing key questions you should ask yourself. These are designed to help ground your thinking and ensure your AI initiatives are built on a solid foundation, not on “Holy Grail” aspirations.

If we want AI to succeed—technically, ethically, and operationally—we need to dismantle the myth of the Holy Grail. And it starts by seeing these illusions for what they really are.

Illusion 1: The All-Knowing Oracle

“Just throw all your data at it, and AI will magically solve everything.”

One of the most pervasive illusions in today’s AI landscape is the myth of the All-Knowing Oracle. This misconception suggests that feeding larger and larger volumes of data into AI will automatically yield groundbreaking insights and effortlessly solve complex problems without deliberate guidance or carefully defined metrics. This simplistic “we have the data, AI will handle the rest” mindset overlooks the necessity for clean, contextually relevant, and purposefully structured data inputs. It also overlooks the essential human judgment required to frame meaningful questions, interpret nuanced outputs, and effectively apply insights in real-world scenarios.

The data reveals the truth: a staggering 85% of AI projects never make it to production, largely due to poor data quality and fuzzy objectives (Dynatrace). Furthermore, bad data isn’t just an inconvenience; it drags down operating revenue by about 6% per firm, equivalent to about $406 million a year on average (Fivetran). To avoid suffering the same fate, leaders must demand upfront data diligence before ever letting the conversation get to model selection and deployment.

Reality Check:

• Are we feeding large, unrefined datasets into AI models hoping for unspecified “valuable insights”?
• Is there a lack of clearly defined business needs or Key Performance Indicators (KPIs) for our AI projects?
• Have we skipped a rigorous data-quality audit and preparation phase?

Illusion 2: The Human Replica

“AI can fully replace human capabilities, empathy, and judgement.”

Next is The Human Replica: the belief that AI can fully replicate, or even replace, nuanced human traits like empathy, complex judgment, and reasoning. A common pitfall is attempting to automate roles that rely heavily on contextual awareness. Teams often discover, sometimes painfully, that current AI systems cannot reliably handle tasks steeped in ambiguity or subtlety, such as interpreting complex legalese or performing duties that require years of training and experiential learning.

When imperfect Human Replica’s are deployed, they tend to produce output that seems right, but inevitably fail to capture true human nuance, often resulting in very poor user interactions. The stakes couldn’t be higher: some 70% of customers say they will switch brands after a single bad AI interaction (Unity Communications), and poor customer experiences already endanger $3.8 trillion in global sales every year (Qualtrics). Babylon Health, for example, rode an AI-triage narrative to a $4 billion valuation, then collapsed into bankruptcy, leaving a £22 million funding hole for one NHS authority and wiping out shareholder equity (WIRED).

When AI is considered for roles requiring profound human judgment, like palliative care counseling, the fix isn’t wholesale replacement, but rather defining clear boundaries for human accountability and oversight. Only then should you begin creating the AI “kernels” that will help automate the small, modular tasks that AI is actually suited for.

Reality Check:

• Are there discussions or plans to replace entire teams or roles requiring significant human empathy, complex judgment, or ethical decision-making with AI?
• Do our AI applications lack clearly defined boundaries for human oversight and ultimate accountability, especially in sensitive areas?
• Is AI being considered for tasks where nuanced human interaction is critical, rather than for augmentation or handling routine tasks?

Illusion 3: The False Launchpad

“AI works on simple tasks, it’ll easily handle complex problems too.”

Then we encounter the illusion of The False Launchpad—the mistaken belief that early wins on simple AI tasks, often achieved in controlled or demo environments, signal readiness for tackling far more complex, high-stakes problems. This overconfidence obscures the need for a corresponding leap in expertise, data rigor, and system architecture when scaling to real-world deployments.

The most vivid case of this illusion is Zillow’s short‑lived venture into direct home buying and flipping, branded Zillow Offers. The firm’s automated Zestimate tool looked competent on the low‑stakes task of giving homeowners an instant price estimate, so leadership convinced itself the same modelling philosophy could safely bankroll thousands of property trades across the United States. Within three years, the model’s blind spots collided with a chaotic pandemic housing market, wiping hundreds of millions from earnings, billions from market value, and roughly a quarter of the workforce (WIRED, Axios, Architectural Digest, Development Corporate). This case vividly illustrates the danger of mistaking a low-orbit success for a launchpad to scalable impact. Without reengineering the underlying approach, what looked like a lift-off quickly turned into a costly crash.

To avoid costly failures like these, organizations must rigorously assess problem complexity and ensure their AI techniques, infrastructure, and depth of talent are genuinely aligned with the demands of scaled deployment.

Reality Check:

• Are we assuming an early AI win or flashy demo will readily scale to much harder, high-stakes problems? 
• Was our initial impressive demo built by AI experts, or by enthusiasts? Is that same level of expertise truly sufficient for this more complex challenge?
• Do our current AI talent, tools, and data strategy genuinely match the leap in complexity for this scaled-up problem?
• Are we defaulting to an initial AI approach because it worked on a simpler task, without rigorously checking its fit for a more demanding one?

Illusion 4: The Instant Revolution

“One powerful AI tool will transform our organization overnight.”

Another alluring myth is that of The Instant Revolution—the belief that a single powerful AI model or platform can catalyze sweeping organizational change almost immediately. This illusion is especially seductive in boardrooms, where hype cycles and polished vendor demos can create a false sense of inevitability and readiness. But organizations are not blank slates. They are deeply entangled systems of people, processes, incentives, and legacy infrastructure that rarely yield to sudden transformation.

IBM’s experience with Watson Health illustrates this danger. After investing approximately $4 billion in developing the platform, the division was sold off for around $1 billion when the product failed to meet safety and adoption benchmarks (ISG, Radiology Business). The fallout erased more than $3 billion dollars in shareholder value and forced IBM to recalibrate its healthcare ambitions. The core issue was both technical and organizational. Leadership overestimated both the maturity of the product and the institution’s ability to absorb and operationalize it.

Too often, organizations underestimate the friction of real change: integration pain, stakeholder buy-in, regulatory hurdles, and frontline usability. Instead of betting everything on a singular AI moonshot, they should invest in iterative learning via phased experiments, focused sprints, and pilot programs tied to clear, measurable outcomes. This approach not only builds institutional muscle but also reduces risk while steadily compounding capability.

Reality Check:

• Do our AI project timelines aim for large-scale, enterprise-wide deployment without initial pilot programs or phased rollouts?
• Is there an absence of iterative feedback loops and opportunities to learn and adjust during the AI implementation process?
• Are we expecting an AI model to overhaul major organizational functions almost immediately after deployment?

Illusion 5: Shiny Toy Syndrome

“AI is amazing—let’s buy it, then figure out where to use it!”

Finally, we have Shiny Toy Syndrome. The intense hype surrounding each new AI development is undeniably captivating. This allure can lead organizations down one of two paths: some are tempted to adopt AI tools without a clear, pre-existing problem, essentially acquiring a “solution” and then searching for a problem to apply it to. Others, reacting to the same hype, might prematurely dismiss the technology’s potential altogether. This illusion specifically refers to the first scenario—where adoption is driven purely by enthusiasm for the technology itself, without a clearly defined business need or problem-solving objective.

The consequences can be damaging to both credibility and the bottom line. For instance, CNET quietly published dozens of AI-generated finance articles without adequate oversight. After readers flagged basic math errors, the outlet was forced to issue corrections on 41 out of 77 stories (The Verge), leading to a public backlash and a drop in its editorial trustworthiness score. What was intended to be a cutting-edge content strategy quickly became a cautionary tale about deploying AI with no clear accountability framework.

The antidote to this illusion is to mandate a problem-first approach. Start with a specific business need and evaluate whether AI is genuinely the best tool to solve it. If it is, build from that foundation using metrics, constraints, and clear success criteria to guide development.

A quick caveat: some exploratory efforts are necessary—pilots, proofs of concept, and sandbox experiments can be invaluable for understanding what’s possible. But those should still be framed by intentional discovery, not by blind faith in the latest shiny toy.

Reality Check:

• Do strategy discussions frequently start with “How can we use this new AI?” rather than “What problems are we experiencing that AI might address?”
• Are we investing in AI tools or platforms before clearly identifying and validating the specific problems they will solve?
• Have we fully evaluated if simpler, non-AI, or less costly solutions could effectively address the identified business need?

Conclusion

To use Generative AI effectively, focus on strategy and execution, not hype cycles. Recognize these five illusions for what they are: traps that mislead teams into overconfidence, misalignment, and waste. To build a grounded culture, ensure you have clear problem statements, realistic capability assessments, and disciplined experimentation. Before launching any AI initiative, ask: What exactly are we trying to solve? Is AI the right tool? How will we know if it’s working?

Progress in AI demands patience, not panic. Treat transformation as a long game. Celebrate small wins, adapt quickly, and don’t hesitate to pivot when reality challenges your assumptions. The goal isn’t to “do AI”, it’s to improve outcomes. The real value of AI doesn’t come from flashy tools or moonshot ambitions, but from applying it deliberately and insightfully to solve real problems with real impact.

The previous parts of this series (part 1 & part 2) laid the groundwork for Vibe Marketing, from its strategic importance, to the tools and companies embracing it. This final installment delivers an actionable playbook for you and your team to harness Vibe Marketing effectively, sidestep potential issues, and prepare for what’s next.

Vibe Marketing for Individuals and Small Teams: The Agility Advantage

Vibe Marketing empowers individuals and small teams to achieve results previously requiring larger operations. By leveraging AI as a force multiplier, the “Vibe” methodology enables lean teams to be exceptionally agile and impactful. A single marketer with a smart AI stack can accomplish tasks that would normally require a team of 10 or more. In this approach, AI manages execution at scale, like content generation, automated distribution, and data analysis, while the human marketer directs strategy, curates the “vibe,” and ensures narrative quality and authenticity.¹

This method empowers small teams to quickly test ideas, launch content, or roll out full campaigns within hours or days—significantly faster than traditional marketing cycles.² This agility enables them to operate “faster than competitors can plan,” providing a crucial edge in dynamic markets. Further, research shows that AI can cut labor costs by 40–60%, allowing individual marketers to create hundreds of ad creatives in just two hours. This efficiency leads to a remarkable 220% boost in Return on Ad Spend (ROAS).⁴

Emphasis moves from manual execution to strategic system design, intelligent curation, and continuous optimization…

AI tools empower small teams to conduct rapid, relentless experimentation. They can instantly generate and deploy hundreds of variations, allowing data and algorithms to identify the most effective approaches with their audience.¹ This also enables automated multi-channel distribution and shifts focus from one-off campaigns to sustainable, AI-powered marketing systems. Consequently, emphasis moves from manual execution to strategic system design, intelligent curation, and continuous optimization.

Traditional Marketing vs. Vibe Marketing for Small Teams

The transformative impact of Vibe Marketing on smaller operations becomes clearer when contrasted with traditional approaches:

This comparison illustrates the stark efficiency, cost-effectiveness, and agility gains that Vibe Marketing can offer to individuals and small teams. It directly highlights the benefits for those operating with limited resources by showcasing a pathway to significantly heightened impact.

Getting Started with Vibe Marketing: An Actionable Roadmap

To effectively adopt Vibe Marketing, begin by strategically leveraging AI in manageable steps, rather than attempting a complete overhaul overnight. The first step of this approach is to define your brand’s “vibe”—its essential human element. Before utilizing any AI tools, clearly articulate your brand’s core emotional essence, which will guide all subsequent AI-assisted execution.

Consider what feelings you want your customers to associate with your brand, its potential theme song, and three words that best describe the company culture and personality. Documenting this in a brand voice chart or a “vibe profile” can be immensely helpful.³ Then, analyze your customer communications such as emails, support chats, social media comments, and reviews to understand how your audience perceives and discusses your brand. From that analysis, distill three to five brand tone rules and provide concrete examples of “on-vibe” and “off-vibe” phrasing. These will serve as “system prompts” that will feed into most of your downstream AI interactions.

The second step involves auditing for bottlenecks and uncovering potential AI use-cases. To do this, identify recurring tasks within your marketing workflows that are time-consuming or resource-intensive. A good starting point is to ask your team (or reflect yourself, if operating solo) about which regular tasks are the most boring, repetitive, or frustrating, or what consistently slows progress. These identified pain points often represent ideal opportunities for AI automation or augmentation. Begin by choosing just one or two repeatable workflows to enhance with AI, such as drafting emails, creating visual assets for social media, or transforming long-form content into multiple shorter pieces.⁶ The key to AI use-case development is iteration, so don’t get discouraged when the first few attempts don’t work as expected. Make sure you refer to a quality prompt engineering guide to pick up on how to “talk” to the models for best effect.

In the third step, begin experimenting with ‘easy builder’ and automation tools. Commit to regular, even if short, sessions for hands-on learning with relevant AI tools—perhaps 15-30 minutes daily. Take advantage of the many AI platforms offering free trials or generous free tiers, which permit considerable exploration and learning without requiring a significant initial investment. To build your skills and comfort, start with simple, achievable tasks, like using an AI writing tool to condense an email or develop social media post ideas. Direct your early experiments toward tools for quick prototyping and interface building (such as Replit and Bolt), workflow orchestration platforms (like n8n, Gumloop, and Make), and multi-purpose AI assistants (such as Claude and ChatGPT).

Step four centers on embracing relentless, rapid experimentation. This requires a mindset shift from testing only a few variations of a campaign element to exploring 50 or even 100 automatically generated alternatives. The Vibe Marketing approach advocates for launching numerous variants quickly, then allowing data and algorithmic feedback to reveal which angles, messages, or visuals achieve the most traction. Cultivate a “build once, distribute widely” or “build once, distribute forever” mentality for your core content and assets.¹ Additionally, consider dedicating a specific period each week for rapid idea generation, AI-assisted creation, and the launch of multiple small experiments, prioritizing volume, variation, and swift feedback over perfection.

AI can generate content at scale. What it can’t do is decide what’s actually good. That’s where the vibe marketer comes in…

Finally, the fifth step is to cultivate your “Vibe Marketer” mindset and skills. An ideal Vibe Marketer exhibits a unique combination of qualities: discerning taste and a distinct point of view, a systems-thinking methodology, the capacity to produce high-signal/low-fluff content, a talent for instantly capturing attention, and the ability to steer AI in human-centric storytelling. For practical development, concentrate on honing essential skills like effective prompt engineering, critical thinking, and problem-solving. Furthermore, a solid grasp of fundamental marketing and even software development principles, alongside a dedication to continuous learning in the swiftly changing AI landscape, is crucial. This transforms the role into that of an orchestrator: guiding AI, verifying its output, and weaving its capabilities into a broader strategic framework.⁷

Adopting Vibe Marketing doesn’t necessitate an immediate, complete overhaul. Instead, it involves working smarter, leveraging AI strategically, and beginning with manageable, focused steps.

Navigating the Pitfalls: The Critical Realities of Vibe Marketing

While Vibe Marketing offers transformative potential, it’s essential to approach it with a clear understanding of its inherent risks and limitations. Acknowledging these challenges is crucial for responsible and effective implementation.

• Generic, Soulless Content: One of the most significant risks is that AI, if not carefully guided and its outputs meticulously personalized by humans, can produce content that feels bland, generic, or lacks a distinct personality. Audiences are becoming adept at detecting the “smell of ChatGPT” or other AI-generated text that lacks genuine human warmth or unique insight.⁸
• Over-Automation & Loss of Human Touch: An excessive reliance on technology can strip the human element from marketing communications, which is often critical for building trust and rapport. AI should ideally serve to generate a first draft or provide options, not the final, unreviewed product.
• Authenticity & Overpromising: The “vibe” projected by marketing efforts must align with the actual product reality and brand values. If there’s a disconnect, Vibe Marketing can quickly devolve into what consumers perceive as a “marketing scam”.² It’s vital to avoid misleading claims about “set it and forget it” AI solutions that supposedly require zero human oversight.⁹
• Security, Compliance & Ethical Blind Spots: AI systems might inadvertently generate claims that are inaccurate, non-compliant with industry regulations, or ethically questionable. There’s also the risk of perpetuating biases present in the training data, leading to skewed messaging or unfair targeting. Data privacy and security are also paramount concerns when using AI, especially with customer data.¹⁰
• Technical Debt & Maintenance (A Lesson from Vibe Coding): If the AI systems or the content and workflows they generate are not well understood by the human team, they can become opaque black boxes. This can lead to difficulties in maintenance, adaptation, or troubleshooting down the line, accumulating a form of technical debt.
• Skill Erosion & Shallow Understanding: An over-reliance on AI tools, particularly for foundational tasks, could potentially hinder the development of fundamental marketing knowledge, critical thinking abilities, and problem-solving skills, especially among junior professionals.¹

A significant, nuanced limitation of current AI in the context of Vibe Marketing is its inherent inability to replicate human “taste.” Taste encompasses a complex blend of cultural understanding, aesthetic judgment, intuition, and experience-based discernment regarding what is truly good, resonant, beautiful, or culturally appropriate in a given context. AI models, trained on vast datasets, can identify patterns, mimic styles, and generate content that is often technically proficient. However, they lack the subjective, often deeply personal, and contextually sensitive faculty of taste. As noted, “AI can generate content at scale. What it can’t do is decide what’s actually good. That’s where the vibe marketer comes in.”¹ Similarly, AI cannot perfectly capture a nuanced brand voice without explicit training and ongoing human refinement.⁹ This implies that the human role in Vibe Marketing extends beyond the purely strategic; it is also deeply aesthetic and qualitative. The Vibe Marketer acts as the ultimate arbiter, curating and elevating AI-generated outputs to ensure they genuinely align with the intended “vibe” and meet the brand’s standards of quality and appropriateness.

To successfully navigate these pitfalls, a proactive and vigilant approach is essential. This involves several key strategies, including continuous human oversight, which means skilled human marketers regularly review and refine all AI-generated content and AI-driven processes. Robust review processes are also crucial, implementing multi-stage checks for accuracy, tone, brand alignment, and compliance. Furthermore, it’s important to fact-check and verify any claims or data presented in AI-generated content. Establishing and adhering to clear ethical guidelines for AI use in marketing is another key strategy. Additionally, training AI on the specific brand’s existing content helps ensure consistency in brand voice. Finally, fostering critical thinking within teams encourages them to question, analyze, and understand AI outputs, rather than blindly accepting them.

The Future is a Human-AI Vibe: Beyond 2025

Vibe Marketing is not an end state but part of a larger, ongoing evolution in how humans and AI collaborate in creative and strategic endeavors.

As AI systems continue to grow in capability, the nature of human-AI interaction is poised to shift further towards more deeply collaborative, intuitive, and dynamic partnerships.¹¹ It is anticipated that AI will take on increasingly complex tasks, thereby freeing human professionals to concentrate on higher-order strategic thinking, novel creative ideation, and complex problem-solving.¹² We may witness the emergence of more “experiential organisms”—marketing initiatives and platforms that can sense and adapt in real-time to audience emotional cues and contextual shifts, creating truly dynamic and personalized brand interactions.¹³

Throughout this technological transformation, the intrinsic value of human creativity, ingenuity, storytelling prowess, emotional intelligence, and ethical judgment will not diminish. On the contrary, these uniquely human attributes are likely to become more critical differentiators in a world increasingly saturated with AI-generated content.¹⁴ AI can streamline processes and scale execution, but the spark of true innovation, the depth of authentic emotional connection, and the wisdom of ethical consideration will remain firmly in the human domain.

Conclusion and Recommendations

Vibe Marketing, born from the same innovative spirit as Vibe Coding, represents a significant evolution in marketing strategy and execution, supercharged by the capabilities of modern AI. It offers a pathway for businesses, particularly agile individuals and small teams, to achieve unprecedented speed, scale, and personalization in their efforts to connect with audiences on a meaningful, emotional level.

The core of Vibe Marketing lies in a symbiotic human-AI partnership: humans define the strategic “vibe,” provide creative direction, and ensure ethical conduct, while AI tools handle much of the executional heavy lifting, from content generation and workflow automation to data analysis and personalization.

Key Takeaways:

1. Strategic Shift, Not Just Tools: Vibe Marketing is a mindset and a strategic approach that redefines roles and processes, rather than just being about adopting new AI tools.
2. Human Centrality: Despite AI’s power, human strategy, creativity, taste, and ethical oversight are more crucial than ever to guide AI and ensure authentic, resonant marketing.
3. Agility for All: Vibe Marketing principles and accessible AI tools are democratizing advanced marketing capabilities, allowing smaller players to compete effectively with larger entities.
4. Continuous Learning is Non-Negotiable: The AI landscape is evolving rapidly. Marketers must commit to ongoing learning and experimentation to stay ahead.
5. Ethical Vigilance is Paramount: The power to connect emotionally comes with the responsibility to do so ethically, transparently, and authentically.

Recommendations for Readers:

• Embrace the “Vibe” Mindset: Start thinking about your brand’s emotional essence and how AI can help you express it more effectively and efficiently.
• Begin Experimenting: Don’t wait for perfection. Start exploring relevant AI tools, leveraging free tiers, and tackling small, manageable automation projects to build familiarity and confidence.
• Focus on System Design: Shift from thinking about one-off campaigns to building repeatable, AI-assisted marketing systems.
• Invest in Human Skills: Cultivate skills in strategic thinking, prompt engineering, critical analysis of AI outputs, and ethical AI governance.
• Stay Informed and Adaptable: The field is dynamic. Follow thought leaders, join communities, and be prepared to adapt your strategies and toolsets as AI technology matures.

The journey into Vibe Marketing is an invitation to innovate, to connect more deeply with audiences, and to redefine what’s possible in the age of AI. It’s an opportunity not to be replaced by technology, but to be profoundly amplified by it. The future of marketing will undoubtedly be a collaborative “vibe” between human ingenuity and artificial intelligence.

References

1. The Old Way of Marketing Is Dying — Welcome to Vibe Marketing
2. What is Vibe Marketing? (Examples + AI Tools to Automate It)
3. Vibe Marketing Explained: Real Examples, Tools, and How to Build Your Stack | YourGPT
4. 25 Best Vibe Marketing Tools You Can’t Miss In 2025
5. AI Marketing: Revolutionize Your Campaigns with AI & Vibe Marketing
6. What the hell is vibe marketing?
7. The Future of AI-Assisted Software Development: Vibe Coding …
8. Don’t use AI in your marketing. If you value your brand. – Reddit
9. Vibe Marketing: The AI Driven Future of Strategic Brand Management – MPG ONE
10. AI in Product Design: A Strategic Guide for Agencies to Stand Out – DesignRush
11. Andrej Karpathy’s Vibe Coding and AI Reading Vision: A Paradigm …
12. AI in Marketing in 2025: Smart Automation and Brave Brand Building – CMS Wire
13. Architects of the future: how AI is tearing up the experiential marketing rulebook | The Drum
14. Vibe Marketing Is Here – But Human Creativity Still Sets the Tone …

The first part of this series deconstructed the “vibe” phenomenon, tracing its origins from “Vibe Coding” to the emergence of “Vibe Marketing.” Now, the focus shifts to the strategic imperatives driving this new approach, the AI-powered toolkit enabling it, and real-world examples of businesses successfully riding the wave.

The Strategic Imperative of Vibe Marketing in the AI Era

The current moment is ripe for Vibe Marketing due to two primary factors. Firstly, AI capabilities, particularly in areas like natural language understanding, content generation, and even emotional sentiment analysis, are maturing at an astonishing rate.¹ AI can now handle tasks that were once the exclusive domain of human marketers, from drafting copy to generating visuals and personalizing messages at scale.

Secondly, this technological leap coincides with a growing consumer demand for more genuine, personal, and emotionally resonant connections with brands.¹ In an increasingly noisy and often impersonal digital world, audiences are fatigued by generic messaging and intrusive advertising. Traditional marketing methodologies, often characterized by long development timelines and broad-strokes campaigns, are proving less effective in capturing attention and fostering loyalty.² Vibe Marketing offers a pathway to bridge this gap by leveraging AI’s efficiency to deliver the personalized and authentic experiences consumers crave, with the speed required by today’s market.

The Irreplaceable Human Element: Strategy, Creativity, Ethics

A critical point to underscore is that Vibe Marketing does not attempt to replace human marketers with AI. Instead, it attempts to redefine and elevate the role of humans by complimenting their natural strengths. AI, for all its advancements, lacks genuine emotion, nuanced cultural understanding, ethical judgment, and true, out-of-the-box originality.³ These uniquely human attributes become even more critical in a Vibe Marketing paradigm, where human roles evolve to focus on several key areas.

Chess grandmaster Garry Kasparov noted that human-AI teams consistently outperformed both human-only and AI-only players…

For instance, human marketers are crucial for setting strategic directions by defining “core vibes,” establishing overarching campaign goals, gaining a deep understanding of the target audience’s needs and emotional drivers, and making high-level decisions about brand positioning. Furthermore, in the realm of creation and curation, while AI can generate options for ad copy and visual concepts, human insight and good taste are still needed for breakthrough concepts. Human marketers need to inject genuine creativity, curate the best AI-generated outputs, and ensure content aligns with the brand’s taste and quality standards.² Ethical oversight is also paramount; human marketers are responsible for ensuring that AI-generated content is fair, unbiased, accurate, compliant with regulations, and does not mislead or manipulate consumers, thereby acting as the ethical compass for the brand’s AI-assisted communications.⁴

This dynamic positions AI as a powerful amplifier of human capability rather than a wholesale substitute. The Vibe Marketing approach fosters what can be described as a “centaur” model for marketers, drawing an analogy from chess grandmaster Garry Kasparov, who noted that human-AI teams (centaurs) consistently outperformed both human-only and AI-only players. In this model, the human marketer provides the strategic brain, emotional intelligence, ethical compass, and creative vision, while AI offers the speed, scale, and capacity for execution. This enhances the human role by shifting the focus away from repetitive execution of tasks to higher-order strategic thinking, sophisticated “vibes engineering”, and vigilant governance. The evolution has profound implications for skill development within the marketing profession, emphasizing the need for adaptability and a new human-machine literacy.

Transformative Potential for Business

When effectively implemented, Vibe Marketing has significant potential to transform business operations and competitiveness. It can manifest in several key ways, leading to increased agility, for instance, by allowing businesses to respond more rapidly to market changes, competitor moves, and emerging trends. Moreover, AI enables hyper-personalization at scale, facilitating the delivery of tailored messages and experiences to individual consumers or highly specific micro-segments, a feat previously impossible to achieve efficiently.⁵ Vibe Marketing also opens new avenues for customer engagement, making concepts like adaptive experiences—where marketing interactions change in real-time based on user input or emotional cues—feasible.⁶ Additionally, the increasing accessibility and affordability of sophisticated AI tools contribute to the democratization of advanced marketing, empowering smaller businesses and startups to deploy strategies once reserved for large corporations with extensive resources.

Ultimately, businesses that master the art and science of Vibe Marketing are likely to gain a significant competitive advantage by being more responsive, relevant, efficient, and emotionally connected to their customers.

Powering the Vibe: The AI Toolkit for Modern Marketers

Effectively executing a “vibe campaign” relies on leveraging a suite of modern AI tools; what some people are calling the “Vibe Marketing Stack”.² The power lies not just in using individual tools in isolation, but in orchestrating them to create seamless, intelligent workflows.

Categories of Tools

The Vibe Marketing toolkit can be broadly categorized as follows:

Content Generation (Text, Image, Video): These tools dramatically reduce the time, cost, and complexity associated with content production, enabling the rapid iteration and experimentation that are central to Vibe Marketing.

• Text: Tools like OpenAI’s ChatGPT, Anthropic’s Claude, Jasper, and Writesonic are instrumental for drafting a wide array of textual content, including emails, ad copy, blog posts, and social media updates. A key advantage is their ability to rapidly generate multiple versions of a message and facilitate A/B testing and optimization.⁷
• Image/Visuals: Platforms such as Midjourney, OpenArt, DALL-E (integrated into tools like ChatGPT), Canva, Kittl, and Designs.ai empower marketers to create custom visuals, unique ads, and brand-aligned imagery quickly, reducing reliance on stock photography or lengthy design cycles.
• Video: For voiceovers, tools like ElevenLabs offer realistic AI-generated voices. For video creation and editing, platforms like CapCut, Veed.io, Runway, Pika, and Synthesia provide capabilities ranging from auto-captioning and template-based creation to generating short-form videos from prompts.

Automation & Workflow Orchestration: They are the “engine room” of Vibe Marketing. They empower even small teams to design and manage intricate campaign flows and operational processes that previously demanded significant manual effort or much larger teams, thus freeing up human marketers to focus on strategy and creativity.

• Tools: Platforms like n8n, Make (formerly Integromat), Zapier, Digital First AI, Relay, and Taskade serve as the connective tissue in the Vibe Marketing stack.
• Function: These tools enable marketers to connect disparate applications, automate repetitive tasks (such as social media posting, email sequences, and data transfers between systems), and build sophisticated multi-step AI agents that can execute complex workflows.

Personalization & Customer Journey Management: AI enables personalization at a scale and depth previously unimaginable, allowing for each customer interaction to be potentially unique, more relevant, and consequently, more effective.

• Tools: Examples include YourGPT for creating product-aware chatbots, CRM platforms with embedded AI like HubSpot CRM and Salesforce Einstein, and specialized services like Amazon Personalize.⁷
• Function: These tools facilitate the tailoring of content, product recommendations, and interactive experiences based on individual user data, past behavior, demographic information, and real-time feedback.

Emotional Resonance & Sentiment Analysis: This is where the “vibe” in Vibe Marketing becomes data-informed and empirically measurable, moving beyond intuition alone to systematically understand and optimize for genuine emotional connection with the audience.

• Tools: This category includes text analysis platforms like MonkeyLearn, Lexalytics, Aylien, IBM Watson, and Amazon Comprehend.⁸ More advanced tools like Dragonfly AI offer “neuromarketing” insights by analyzing visual attention ⁹, while platforms like Affectiva and APIs from Google Cloud AI enable the analysis of facial expressions and vocal tone.¹⁰
• Function: These tools are designed to gauge emotional responses to content, understand overall customer sentiment towards a brand or product, and identify specific emotional triggers by analyzing text, voice, and even visual cues.
• Ethical Considerations: The capacity of AI to analyze and even predict emotional responses represents a significant leap in marketing technology. Tools that can interpret facial expressions, vocal inflections, and the sentiment embedded in text allow marketers to tailor experiences designed to evoke specific feelings, such as trust or excitement. However, this power comes with profound ethical responsibilities. If audiences perceive these sophisticated techniques as manipulative rather than genuinely enhancing their experience, it can lead to a severe erosion of brand trust and a backlash against the brand. The line between creating an atmosphere of trust and exploiting emotional vulnerabilities is exceptionally thin. Therefore, a robust ethical framework, transparent practices, and continuous human oversight are not just advisable but absolutely paramount when deploying AI tools focused on emotional resonance. This risk of perceived manipulation is a critical consideration, perhaps uniquely pronounced in the “emotional” dimension of Vibe Marketing.

Brand Voice Consistency: This is crucial for preserving brand identity and avoiding a generic, “AI-written” feel when scaling content creation with multiple AI tools. It ensures that all communications, regardless of their automated components, sound authentically like the brand.

• Tools: Solutions like Semji (with its AI+ Brand Voice feature), AIM Fluence, and TryLeap AI are designed to help maintain a consistent brand identity in AI-generated content.¹¹
• Function: These platforms can be trained on a brand’s existing content (e.g., website copy, past campaigns, style guides) to learn and replicate its unique tone, style, vocabulary, and overall personality.

Who’s Riding the Vibe Wave? Companies Leading the Charge

While Vibe Marketing is an emerging concept, several companies are already employing strategies and AI tools that align closely with its core principles. Their initiatives offer valuable insights into its practical application.

For example, Coca-Cola’s “Create Real Magic” campaign illustrates this by utilizing DALL-E 2, an AI image generator, to empower fans to create their own AI-generated artwork inspired by Coca-Cola’s iconic branding. This campaign masterfully blended AI capabilities with human creativity, resulting in over 120,000 submissions and 7 million social impressions.³ This initiative tapped into the “vibe” of co-creation and nostalgia, using AI as a tool for mass fan engagement, fostering an authentic emotional connection.

Similarly, Heinz executed its “A.I. Ketchup” campaign by cleverly using DALL-E, prompting it to generate images of “ketchup.” When the AI predominantly produced images resembling Heinz bottles, the company turned this into a humorous, brand-hacking campaign with the tagline, “Even AI knows ketchup = Heinz.”³ This went viral, garnering over 15 million organic impressions and a reported 15% sales increase, exemplifying the speed, cultural relevance, and playful “vibe” that AI can facilitate.

The music streaming juggernaut Spotify introduced an “AI DJ” that uses voice cloning of a human DJ to curate personalized playlists interspersed with commentary. The system analyzes user listening habits for real-time customization, leading to high user engagement, with reports of 40% of users engaging weekly and experiencing 30% longer session times.³ This is a prime example of data-driven emotional connection and personalization at scale.

Nutella also tapped into marketing vibes with its unique AI-generated jars. The popular spread brand used a generative algorithm to create 7 million unique, vibrant, and colorful labels for its jars in Italy. No two jars were the same, turning the product packaging into a collectible and highly shareable experience. All 7 million jars sold out, and the campaign generated over 10,000 social media posts from consumers, showcasing AI’s ability to drive novelty, customer engagement, and a unique product “vibe.”¹²

In the fashion sector, Farfetch utilized AI-optimized emails. The luxury fashion platform employed AI copywriting tools to fine-tune its email marketing messaging, including subject lines and personalization for different email types like wishlist alerts and abandoned cart reminders. This resulted in a 31% surge in average open rates and a 38% increase in average click rates, all while maintaining Farfetch’s sophisticated, luxe brand tone, highlighting AI’s role as a powerful behind-the-scenes optimizer.¹³

Finally, Meta’s “AI Sandbox” initiative, announced in May 2023, involves testing a platform for advertisers on Facebook. This platform allows advertisers to experiment with AI-generated ad variations, including text and image generation, aiming to create more successfully targeted and effective ads, underscoring the industry focus on leveraging AI for scaled ad creation and optimization.¹⁴

These examples, while often from large corporations with significant resources, showcase the principles of Vibe Marketing in action: leveraging AI for speed, scale, personalization, and creative augmentation, all while aiming for a stronger emotional connection with the audience. However, it’s important to note that the underlying AI tools and strategic approaches are becoming increasingly accessible. The democratization of these tools means that the core tenets of Vibe Marketing—speed, AI-assisted creation, and emotional targeting—are within reach for businesses of all sizes, including individuals and small teams. The key differentiator is less about financial resources and more about the strategic vision, creative application of AI, and the agility to experiment and adapt.

(Continue to Part 3: Catching the Vibe – Your Playbook for Success)

References

1. Vibe Marketing Is Here – But Human Creativity Still Sets the Tone …
2. The Old Way of Marketing Is Dying — Welcome to Vibe Marketing
3. What is Vibe Marketing? (Examples + AI Tools to Automate It)
4. Karpathy’s ‘Vibe Coding’ Movement Considered Harmful | N’s Blog
5. Generative AI: The Next Evolution In Product Design And Marketing – Forbes
6. Architects of the future: how AI is tearing up the experiential marketing rulebook | The Drum
7. Vibe Marketing Explained: Real Examples, Tools, and How to Build Your Stack | YourGPT
8. Best Tools for Emotional Sentiment Insights Using AI Agents – Insight7
9. Effective Emotional Resonance in CPG Advertising – Dragonfly AI
10. Emotional AI Examples That Are Redefining User Experiences in 2025 – Litslink
11. Scale Smart: Maintain Brand Voice Consistency with AI – Interrupt Media
12. AI-Powered Marketing in 2024: A Benchmarking Report for 2025 Planning – Solveo
13. The 10 Most Inspiring AI Marketing Campaigns for 2025 – Madgicx
14. 11 Best AI Advertising Examples of 2025 – DataFeedWatch

The business and marketing landscape is in a perpetual state of transformation, a pace dramatically accelerated by the relentless advancements in Artificial Intelligence (AI). Amidst this whirlwind of innovation, new concepts emerge, often met with a mix of excitement and skepticism. “Vibe Marketing” is one such term gaining traction. However, to dismiss it as merely another fleeting buzzword would be to overlook the fundamental strategic shift it represents—a new era of human-AI collaboration designed to connect with audiences on a more intuitive and authentic level. The conceptual underpinnings of Vibe Marketing can be traced to the world of software development, specifically to a notion called “Vibe Coding,” with AI serving as the powerful engine driving its practical application in the marketing sphere.

There appears to be a collective aspiration to humanize our interactions with increasingly sophisticated AI systems, seeking more intuitive and less rigidly structured methods…

The very emergence of terms like “Vibe Marketing” and “Vibe Coding” points to a broader current in professional and societal attitudes toward technology. There appears to be a collective aspiration to humanize our interactions with increasingly sophisticated AI systems, seeking more intuitive and less rigidly structured methods of engagement. This can be seen as a response to the often cold, purely logical perception of AI. An attempt to infuse human intuition, feeling, and a sense of collaborative ease into processes that might otherwise feel alienating.

The rapid succession of AI breakthroughs can be overwhelming for many professionals. “Vibe” terminology offers a more approachable, less intimidating entry point, particularly for those who are not deep AI experts. It suggests a partnership with AI rather than a replacement by it, a framing that is psychologically more palatable and reflects a more realistic pathway for integrating AI into creative and strategic fields. This drive towards intuitive interaction is not isolated; it’s a macro-trend evident in user interface and user experience (UI/UX) design evolution and the widespread adoption of natural language interfaces across a multitude of technologies.

Deconstructing the “Vibe”: Understanding Vibe Coding – The Precursor

To fully appreciate Vibe Marketing, one must first understand its conceptual cousin, “Vibe Coding.” This term was thrust into the tech lexicon by Andrej Karpathy, a prominent figure in AI and co-founder of OpenAI.¹

The Genesis: Andrej Karpathy’s Vision and Its Nuances

Karpathy initially described “vibe coding” as an approach where a programmer would “fully give in to the vibes, embrace exponentials, and forget that the code even exists”.² This method involved leveraging advanced Large Language Models (LLMs) through tools like Cursor, often using voice commands to direct the AI, minimizing direct keyboard interaction. The core idea was to accept AI-generated code suggestions readily, sometimes without a deep dive into the intricacies of every line, to facilitate rapid prototyping and development, particularly for what Karpathy termed “throwaway weekend projects”.³

This evolution from an almost laissez-faire interaction to a meticulously managed collaboration is significant. It reflects a maturation of thought, moving from initial exuberance about AI capabilities to a more pragmatic and responsible framework for human-AI partnership…

This initial articulation of vibe coding captured the burgeoning excitement around the rapidly advancing capabilities of LLMs and their potential to unlock exponential productivity gains in software development. It was about lowering the barriers to creation and prioritizing the creative outcome—the functional software—over the meticulous, often laborious, process of manual coding.¹ The focus was on seeing an idea, expressing it to the AI, running the result, and iterating quickly: “see stuff, say stuff, run stuff, and copy paste stuff, and it mostly works”.³

The Evolution: From “Forgetting Code” to a “Tight Leash”

Interestingly, Karpathy’s perspective on vibe coding evolved. He later advocated for a more cautious and controlled approach, advising developers to keep a “tight leash” on their AI coding assistants. He memorably likened these AI tools to a “new over-eager junior intern savant with encyclopedic knowledge of software, but who also bullshit you all the time, has an over-abundance of courage and shows little to no taste for good code”.⁴

This refined vision emphasizes a methodical workflow:

1. Comprehensive Contextual Input: Providing the AI with all relevant project information. Karpathy even offered a command-line example for packaging project files: ‘files-to-prompt. -e ts -e tsx -e css -e md –cxml –ignore node_modules -o prompt.xml’.⁴
2. High-Level Approach Discussion: Describing the desired incremental change and asking the AI for several high-level approaches with pros and cons before requesting code generation.
3. Manual Review and Learning: Critically examining the AI’s first draft code, pulling up API documentation for unfamiliar functions, asking the AI for explanations, and being prepared to iterate or try different approaches.
4. Iterative Testing and Committing: Thoroughly testing the generated code and committing changes through version control.

This evolution from an almost laissez-faire interaction to a meticulously managed collaboration is significant. It reflects a maturation of thought, moving from initial exuberance about AI capabilities to a more pragmatic and responsible framework for human-AI partnership in coding. The “tight leash” philosophy underscores that human expertise, critical evaluation, and continuous learning remain indispensable, even with highly capable AI assistants. Karpathy himself expressed some reservations about the term “vibe coding,” suggesting that “AI assisted coding” might be more accurate, though less evocative.⁴

The technology ceases to be viewed as a magic wand and is instead recognized as a powerful tool that requires skilled handling, strategic application, and robust oversight…

Karpathy’s journey with the concept of “Vibe Coding” mirrors a common pattern observed in the adoption lifecycle of many new technologies. Initially, there’s a wave of excitement, often accompanied by bold, almost utopian, claims about transformative potential. As users begin to engage more deeply with the technology in real-world scenarios, practical challenges and limitations inevitably surface. In the case of AI-driven coding, these might include AI-generated errors, suboptimal code quality, security vulnerabilities, or difficulties in maintaining AI-produced codebases. This encounter with reality forces a recalibration. The technology ceases to be viewed as a magic wand and is instead recognized as a powerful tool that requires skilled handling, strategic application, and robust oversight.

Karpathy’s shift in emphasis from “forget that the code even exists” to advocating for a “tight leash” and a “slow, defensive, careful, paranoid” approach perfectly illustrates this progression from hype to pragmatism. This pattern is crucial for professionals to recognize, as it suggests that Vibe Marketing, drawing inspiration from Vibe Coding, will likely follow a similar trajectory. Initial visions of AI completely automating marketing execution will probably give way to more nuanced approaches where AI capably assists skilled human marketers in specific, well-defined, and carefully managed ways.

Core Principles & Impact of Vibe Coding

Despite its evolution, the core principles that initially defined vibe coding remain influential:

• Embracing LLMs: Fully leveraging the advanced capabilities of language models for code generation, iteration, and debugging.
• Intuitive Interaction: Engaging with the coding environment through natural language prompts, reducing reliance on direct, line-by-line manual coding.
• (Initial) Trust in Automation: A willingness to accept AI-generated suggestions and fixes, sometimes with less exhaustive manual review, to accelerate the process.
• Iterative Trial-and-Error: Relying on the LLM to help resolve errors and bugs, sometimes through seemingly indirect prompts or workarounds.
• Focus on the Outcome: Prioritizing the functionality and rapid completion of the project, sometimes over a granular understanding of every piece of generated code.

The impact of this approach has been notable, signaling the potential for exponential productivity increases, significantly lowering entry barriers for coding newcomers, and fundamentally shifting the role of the developer from a pure scribe to more of an architect or prompter.⁵ Vibe Coding, in essence, established a precedent by demonstrating a novel paradigm of human-AI interaction within a complex, creative discipline, thereby making the conceptual leap to “Vibe Marketing” a logical and anticipated next step.

Enter Vibe Marketing: Intuition, Authenticity, and AI-Powered Agility

Drawing inspiration from the philosophical underpinnings of Vibe Coding, “Vibe Marketing” emerges as a parallel concept tailored to the world of brand communication, customer engagement, and campaign execution.

Defining Vibe Marketing: More Than Just AI Automation

Vibe Marketing can be defined as an approach where human marketers establish the overarching strategic “vibe”—the emotional direction, the core essence of the brand, the desired customer feeling—while AI agents and tools are tasked with handling significant portions of the executional workload.⁶ It’s characterized by conversational interactions between marketers and their AI tools, moving beyond simply automating old marketing processes to fostering a new, more agile and intuitive way of operating.⁷

The crucial element is the synergy between human and machine.⁸ AI provides the scale, speed, and data-processing power, while humans contribute strategic oversight, emotional intelligence, creative spark, and ethical judgment. It’s not about abdicating responsibility to AI, but about leveraging AI to amplify human capabilities.

The Core Tenets of Vibe Marketing

Several core tenets define this emerging approach:

• Emotional Resonance: At its heart, Vibe Marketing is about crafting brand experiences and communications that connect with audiences on a deeper, more emotional level. The goal is to make the brand feel personal, natural, and memorable; fostering a genuine sense of connection by understanding and amplifying the brand’s unique emotional essence.⁸
• Speed and Agility: This approach champions the rapid testing of ideas and the swift launch of campaigns, often in hours or days rather than the traditional weeks or months.⁷ This agility allows businesses to respond to cultural shifts and market dynamics with unprecedented nimbleness, effectively moving at the “speed of culture”.⁸
• Scalability: AI empowers marketers to test hundreds of ad variations, personalize messages for vast audiences, and repurpose content across multiple channels with remarkable efficiency.
• Data-Driven Creativity: While human taste and intuition remain paramount in guiding creative direction, AI plays a vital role in analyzing vast datasets to inform these creative choices, identify patterns, and predict potential engagement.

These tenets illustrate Vibe Marketing as a dynamic, responsive, and adaptive strategy, uniquely suited to the current fast-paced, information-saturated, and authenticity-craving market environment.

Vibe Coding vs. Vibe Marketing: Key Distinctions and Parallels

While Vibe Marketing draws inspiration from Vibe Coding, they are distinct in their application, tools, and primary challenges. A comparative overview helps clarify these differences and shared philosophies:

This comparison highlights that while both approaches leverage AI for accelerated and more intuitive creation, their domains, the specifics of human oversight, and the nature of their outputs differ significantly. Understanding Vibe Coding provides a valuable contextual backdrop for appreciating the innovative potential and distinct characteristics of Vibe Marketing.

(Continue to Part 2: Mastering the Vibe – Strategy, Tools, and Real-World Impact)

References

1. Vibe Coding 101: How AI “Vibes” Are Ushering in a New Era of Programming
2. What is Vibe Coding? | IBM
3. ‘Vibe coding’ is a revolution for optimistic creatives – Blog – Gitpod
4. Vibe Coding Pioneer Advises ‘Tight Leash’ to Rein In AI BS – Visual Studio Magazine
5. The Future of AI-Assisted Software Development: Vibe Coding …
6. Let’s talk about Vibe Marketing, an AI revolution transforming the $250B marketing industry
7. What is Vibe Marketing? (Examples + AI Tools to Automate It)
8. The Old Way of Marketing Is Dying — Welcome to Vibe Marketing
9. Vibe Marketing Is Here – But Human Creativity Still Sets the Tone …

The temptation to rely solely on familiar processes is understandable. Yet, AI operates with characteristics that differ significantly from traditional software. As research from institutions and firms like Google, Statsig, Evidently AI, and Lumenova AI increasingly shows, AI behaves more like an evolving ecosystem than a static, packaged product. While our foundational software engineering principles provide stability, failing to adapt our approaches for AI-specific challenges can cause us to miss out on AI’s full potential.

This article explores how we can build upon existing strengths while also identifying those areas that need updating, and those that need to be written from scratch. We’ll talk about how to gear up to put AI systems into production, and how to responsibly use demos and prototypes to build trust. But first, we’ll need to investigate why AI demands a rewrite to our existing playbooks.

Why AI is a Different Beast Entirely

Why does AI demand such a radical departure? It begins with The Stability Myth. Modern software methodologies seek to provide durable and steady systems, services, and applications. Further, traditional software is (ideally) deterministic, meaning the same inputs typically yield the same outputs. On the other hand, AI has variability and randomization baked in; as they say, “it’s a feature, not a bug.” This means that identical inputs can produce wildly different outputs, even under identical starting conditions. It also means that standard test suites—those that insist on a single “correct” answer—will no longer suffice. A deterministic yardstick simply cannot measure a probabilistic engine.

Beyond its probabilistic nature, many advanced AI models present an interpretability challenge. While traditional software logic can be methodically traced, complex AI systems often operate as “black boxes.” Unpacking the precise reasoning behind a specific AI output can be incredibly difficult, if not impossible. This creates a demand for new tools and techniques that offer interpretability and explanation (Explainable AI), not just for debugging, but for building user trust, meeting regulatory requirements, and ensuring the system aligns with intended ethical goals, especially in critical applications.

Given these challenges—non-determinism and limited interpretability—our approach to quality assurance (QA) and release management must change. Processes and procedures for testing and deployment need to change; for instance, by measuring performance with confidence intervals rather than absolutes, or using shadow deployments to evaluate against live interactions. This necessary vigilance expands traditional CI/CD into what is termed Continuous Evaluation (CE). For teams managing their own models, CE might involve retraining models, or employing reinforcement learning with human feedback (RLHF). However, with the increasing reliance on vendor models and API’s, CE becomes a process of benchmarking model versions against domain-specific data, dealing with rate limits, and fine-tuning with proprietary data sets.

These new requirements turn AI development into what can be described as pipelines without a finish line, where the traditional idea of a stable build no longer applies. Such pipelines acknowledge that an AI system is never truly “done”; instead they are living system that require constant nurturing, monitoring, and adaptation throughout the entire lifecycle. Whether you fine‑tune in‑house or track a vendor’s changelog, success hinges on updating to this new way of thinking about AI-powered software systems.

Hiding uncertainty fosters overconfidence in AI outputs, potentially leading users to accept hallucinations as fact. We must learn to leverage ambiguity, not bury it.

The commitment to perpetual improvement shifts focus from code assets to data assets. Test harnesses, datasets, and eval dashboards must be versioned alongside source code because even small data changes can alter system behavior. Crucially, this deep reliance on data introduces unique ethical dimensions and the specter of bias. AI models learn from the data they are fed; if this data reflects historical or societal biases, the model can inadvertently perpetuate, and even amplify these harms. This demands proactive assessment and mitigation strategies throughout the AI lifecycle. Consequently, ethics reviews and data governance become critical components of the Software Development Lifecycle (SDLC).

Finally, when “maybe” and “I don’t know” are both reasonable answers, the need for an updated User Experience (UX) becomes critical. Hiding uncertainty can foster overconfidence in AI outputs, potentially leading users to accept hallucinations as fact. As such, we must learn to leverage ambiguity, not bury it. This means being deliberate about employing what is known as “affordance” (e.g. including visible probability scores or ranked alternatives along with model outputs). Tooltips, color-coded badges, and quick-feedback widgets become essential so that ambiguous outputs can be flagged and routed through feedback channels. Indeed, feedback loops become critical product infrastructure—a thumbs-up or correction from a user is QA gold.

While these new ways of looking at the AI-SDLC are important to consider, it is not the only factor when judging the success of AI endeavors. One of the biggest roadblocks AI projects experience are, in fact, not technical at all. Rather, they come from predominantly human factors, including the way we talk about and manage our AI efforts.

The Perils of Mismanagement

Forcing AI into traditional software frameworks can lead to predictable and damaging outcomes—stifled innovation, significant wasted resources, and misaligned processes that frustrate skilled teams and risk talent attrition. More insidiously, it can create significant security, governance, and compliance challenges. In particular, with AI, the threat surface widens considerably. Prompt injection, data-poisoning, and model-extraction attacks are all mainstream techniques for exploiting AI systems. The detection and remediation of these attacks extend far beyond the capabilities of standard software security tools. This makes new governance playbooks, like NIST’s AI Risk Management Framework, absolutely essential. This also means that leadership must adopt and adapt to the ever growing list of requirements conducive to successful AI strategies.

Your first AI deployments need to be as close to “bulletproof” as possible. A misstep can set an organization’s AI ambitions back significantly, poisoning the well for future, more ambitious projects.

Leadership also needs to understand that AI deployments are often make or break, especially in the early days. The current climate is thick with AI hype; many are (understandably) skeptical and actively looking for examples to confirm their biases and disbelief. If early AI initiatives falter because they’re managed like conventional software projects, trust evaporates, and regaining it can be an arduous battle. This means your first AI deployments need to be as close to “bulletproof” as possible. A misstep can set an organization’s AI ambitions back significantly, poisoning the well for future, more ambitious projects. The pressure is immense, and it underscores the need for a tailored approach from day one.

Compounding this risk is the simple fact that AI systems are, effectively, unbounded input machines; no test suite can truly enumerate the combinatorial space of prompts a model will face in the wild, meaning bugs often surface in front of users first. The now infamous Google AI Overviews suggesting people glue cheese to pizza, or Microsoft’s Tay chatbot devolving into hate speech within sixteen hours, were not sophisticated attacks. They were ordinary edge cases and trolling that slipped past traditional QA, because no one had envisioned the need to account for prompt injections, “leet-speak“, or Twitter-fueled red-teaming attacks.

These glaring, user-facing, issues demand a rethink on how we manage our AI systems, and therefore a change in how we talk about them. The old paradigms of QA and Verification & Validation (V&V) no longer suffice. What we really need is a new strategic framework, one that takes the complexities and uncertainty of AI systems into account.

Charting a New Course: A Strategic Framework for Enterprise AI

So, how do we navigate this complex landscape and achieve those critical early wins? It’s all about augmenting our IT wisdom with AI-specific thinking.

Navigating enterprise AI should start with a discovery process built for probabilistic systems, not deterministic software with guarantees. OpenAI’s Identifying and Scaling AI Use Cases guide lays out a pragmatic recipe. Begin with a focused sprint where each domain team inventories tasks that are tedious, blocked by scarce expertise, or steeped in ambiguity; exactly the conditions where AI excels, and traditional software comes up short (otherwise, we would have already created a solution, right?). Map every candidate task to one of six capability buckets: content creation, automation, research, coding, data analysis, or strategic ideation. This taxonomy exposes reusable patterns and keeps excitement grounded in concrete capabilities.

Position your first deployments as trust‑building probes. Skip the ‘holy grail’ fantasies.

With the use-case catalog in hand, run each item through an Impact vs. Effort matrix alongside a preliminary Risk and Ethical Consideration review. This helps ensure that even ‘quick wins’ align with responsible AI principles. Green-light the high‑impact, low‑effort, and ethically sound quick wins, and consciously park the high‑effort moonshots for later cycles. Treat the matrix as a living backlog that gets refreshed quarterly, because model upgrades often turn tomorrow’s stretch goals into today’s easy victories.

Once the list is prioritized, position your first deployments as trust‑building probes. Skip the ‘holy grail’ fantasies. Instead, partner with forward‑looking teams and select low‑risk, high‑value proofs that can survive public scrutiny. The objective is twofold: prove what is possible and ship something undeniably useful. Success should be strategically planned to resonate with different internal audiences: providing tangible, undeniable results to convert the skeptics; offering clear ROI and practical benefits to validate the pragmatists; and showcasing innovative potential, even in smaller applications, to satisfy and harness the energy of enthusiasts.

Keep feedback loops open, publish what you learn, and treat models as living products rather than frozen code.

A recent anecdote from inside NASA Jet Propulsion Laboratory (JPL) illustrates the above approach. After a string of California wildfires that led to the loss of thousands of homes, I led a small team that built a Slack‑based assistant to answer natural‑language questions about FEMA processes, insurance claims, wildfire preparedness, and more. The underlying datasets were curated by a team of volunteers to ensure quality and accuracy. We incorporated prompt logging, provided a feedback funnel for bad answers, and ran regular evaluations (a core tenet of Continuous Evaluation) so model or data changes would not break canonical queries. Fixes shipped within hours. Feedback poured in from colleagues directly affected by the fires, many describing the bot as a genuine lifeline. An internal write‑up of the project quickly turned into Exhibit A for why GenAI deserves further investment.

A modest, month‑long project unlocked broad executive sponsorship and sparked a flurry of interest, demand, and buy-in for similar initiatives across the lab. Success stories like these prove the value of starting focused, proving results, and building trust. To keep that momentum, give every use-case a clear owner and sponsor, track accuracy and latency targets, and watch for drift with lightweight monitoring. Keep feedback loops open, publish what you learn, and treat models as living products rather than frozen code. Sustained, disciplined iteration turns today’s spark into an enterprise capability that can reshape the future.

Conclusion – Leading the Charge into the AI Frontier

AI runs on probabilities, not certainties. For organizations steeped in deterministic software disciplines, this demands a profound shift in how success is measured, monitored, and secured. Treating AI as just another software update is a recipe for mediocrity and a high-stakes gamble with organizational trust.

To truly leverage AI’s power, we must acknowledge its unique, living-system nature and adapt our organizational DNA – our processes, structures, and mindset. This means fostering a culture of curiosity, strategic experimentation, and continuous learning, all while diligently managing risk and perception. It requires leadership to champion this shift, ensure those initial deployments are meticulously planned for success, and invest in the new literacies required to elucidate high-gain use-cases.

The organizations that adapt their pipelines, roles, and interfaces to embrace variability will not only ship better AI products faster, building on a foundation of earned trust, but also unlock new avenues of innovation and redefine what’s possible within their industries. Those who delay, clinging to the elusive stability of old frameworks, will find themselves struggling to catch up.

What are your thoughts? How is your organization adapting its approach to deploy AI successfully and build lasting trust?

Research has found that humans can’t really efficiently multitask at all—instead, our brains hop rapidly from one task to another, losing concentration every time we shift our attention.

Colin Ainsworth, Mental Floss, “Yes, You Have Too Many Tabs Open“

While I was happy to see the Google Chrome team develop something to relieve “tab hell”, it was too little, too late. The tab groups were clunky and, more importantly, they did nothing to actually reduce the number of tabs I had open. Alas, “tab hell” just got a fresh coat of paint, and I was still responsible for context-switching as I went about my day. Then came the never-ending tools and apps like OmniFocus, ClickUp, Notion, Teams, Slack, Discord, WebEx, Jabber, SalesForce, Trello, and so on. These tools undoubtedly made things more manageable but still had plenty of short-falls, like when a DNS issue caused many Notion users to lose access to their critical data for hours-on-end. Not to mention, this was just “tab hell” all over again. Each new app came with a new set of notifications, clutter and context-switching.

For the reasons laid out above, I decided to build a local-first app that would let users manage digital resources in whatever structure they felt comfortable with. For instance, with school stuff, I like to keep things organized at the course level, where each course has its own sections for homework, required reading, and online resources. For research and hobbies, I like to organize by topics. And for work stuff at JPL, I prefer to keep things organized at the individual project level. That’s a lot to keep track of! So I knew I needed a system that was flexible, yet still provided the benefits of custom structures like those above. Further, I wanted to reduce redundancy and search time by linking documents with similar characteristics (e.g., if two documents have the same author, discuss similar topics, or any number of arbitrary relationships, they would be connected in some way that would let me exploit those relationships for easier learning and research).

Honestly, a simple description of the system won’t do it justice. Lucky for me (and you), I have a video instead! In fact, this isn’t just any video. I pitched Knowledge Canvas as part of a Product Development contest at UCLA this past summer (2021), and it won first place! The video below is a recording of the pitch my team and I delivered to the judges (a Senior Product Manager at Google, CEO of NuLeep, and Lead Investment Analyst at LGB Capital). Keep in mind that this video was recorded after having only 6 weeks to work on everything, including business plan, market research, and development. As such, the app is dramatically different now and basically looks nothing like the original version. Check the link above to see Knowledge Canvas in its current incarnation.

This post is intended to serve strictly as background info on how Knowledge Canvas came to be (and a bit of bragging rights for winning the contest). I will be releasing an official post on all of the features that have since been baked into Knowledge Canvas and how you can find out more. Thanks for reading, and don’t forget to like the YouTube video and follow subscribe to this site for future development news and updates. Stay tuned!

Rob.Royce

I’ve been thinking a lot recently about what I learned over the last two-and-a-half years, and I think my degree capstone sums it up quite nicely. In this post, I describe how I used 2 FPGA boards, various peripherals like Bluetooth and microphone, TensorFlow, and the “iRobot” system to develop a speech controlled robot with wireless communication.

Note: This is the first of three articles. Part I provides an introduction to the project and lists hardware/software requirements and constraints. Part II is all about the architecture design (including how to configure MicroBlaze, the AXI peripheral bus, and more). Finally, Part III discusses how to use the DeepSpeech model via laptop “co-processor,” followed by a wrap-up and reflection on lessons learned.

Intro

To start things off, I should mention that my capstone project was actually the final project of a 10-week class (CS152B). The first three projects dealt with (relatively) basic concepts, such as FPGA design/development, finite state machines, FPGA peripherals, and using the MicroBlaze soft-processor. The only topic I had not been exposed to prior to this class was MicroBlaze, which is why I will spend a bit of time explaining how that works, what difficulties I had with it, and how things ultimately turned out. I will also briefly describe the iRobot Create system, which is a hobbyist version of the cleaning robot, complete with an API for doing things like actuating the robot and scanning/mapping a room using various sensor modules.

The basic idea is illustrated below. One of the Basys3 FPGA boards will listen for speech commands. When a command is detected, it will transmit that command to the second Basys3 FPGA via Bluetooth, which will then send byte-code commands to the iRobot over UART.

One thing I learned early during my time at JPL is that good engineering starts with good requirements. So before diving into the details of our system, let’s put on our engineering hats and enumerate everything needed to make this project possible. It should be noted that my CS152B team and I built the project predicated on our use of the iRobot, since it was one of the few interesting devices available to us at the time. We also wanted to make the whole thing wireless, since there’s nothing fun about controlling a robot that can’t move more than 3 feet away from a computer. This meant we would need two FPGA boards, one for handling speech commands, and the other for physically controlling the robot.

Hardware Requirements

Since we used 2x Basys3 boards, it made sense to use off-the-shelf modules with existing libraries and drivers. The most practical setup we found was using Digilent PMOD modules. In particular, we used 2x PMOD BT2 modules for Bluetooth, a PMOD Mic3 for speech, and a PMOD RS232 for issuing iRobot commands. The BT2 module provides a nice abstraction in the form of UART. That is, for all intents and purposes, we could treat Bluetooth communication between the two FPGAs as a simple UART connection. Setting the JP2 jumper on both BT2s will cause them to auto-pair if powered on within a few seconds of each other. This made the wireless aspect extremely simple.

The Mic3, on the other hand, required a custom SPI controller. For that, we had to integrate a Digilent reference module into our MicroBlaze design and provide it a 50MHz reference clock, plus start and reset signals, all of which were implemented in C and will be explained later. Suffice to say, the microphone was actually more difficult to setup than bi-directional Bluetooth communication, which I found to be a bit surprising. Finally, the iRobot interface cable required either a RS232 or USB connection. Since we did not have a spare USB module, we opted to use RS232 instead. Finally, we needed to use a laptop to run our TensorFlow model on the speech waves from our mic. We used a spare laptop running Ubuntu 20.04 with 8GB RAM, a slightly outdated Intel i5 CPU, and no GPU. Here is a list of the hardware we ended up using:

• 2x FPGA boards (Digilent Basys3)
  • 2x Bluetooth radio (PMOD BT2)
  • Microphone (PMOD Mic3)
  • UART controller (PMOD RS232)
• iRobot Create (no longer available for retail)
  • Low-level command interface (provided with iRobot)
• Sony VAIO Laptop

The diagram in Figure 2 shows the hardware components and how they are connected to each other. Note that we did not have enough time to implement bi-directional communication between the two FPGAs, even though we would have liked to (since the iRobot has many different sensors and signals).

Software Requirements

In order to facilitate speech recognition, we opted to use the publicly available DeepSpeech model, which runs on top of TensorFlow and is implemented in Python. DeepSpeech was the only external requirement we had. The majority of our software was either developed from scratch or built on top of the libraries and drivers included with the PMODs and MicroBlaze. We created our own PMOD Mic3 driver to make sampling easier, and wrote custom wrappers around the low-level UartLite APIs provided by Xilinx. The software portion will be explained in-depth in Parts II and III.

MicroBlaze Soft-Processor

Working directly in Verilog and programming at the logic-level can be a very gratifying experience. However, the sheer complexity of this project made using only Verilog rather impractical. As mentioned earlier, we opted to use the Xilinx MicroBlaze soft-processor, which is an open-source CPU-core based on the RISC architecture, and which can be synthesized and implemented directly into the fabric of our FPGAs. Using MicroBlaze meant we could also use high-level programming languages like C/C++ to control the robot, sample the microphone, and communicate back-and-forth between the FPGA boards. This made things simpler at the software level, but it also introduced new complications that would end up taking most of our development work to resolve. The following are some of the issues we had to resolve before the whole system would work properly:

• How to generate correct control signals for the microphone in C
• How to sample the mic without exhausting memory or introducing significant lag between speech command and robot execution
  • 16-bits per audio sample at 16,000Hz = 32KB/s
  • Basys3 has 128KB RAM, less memory reserved for instructions, memory-mapped I/O, etc.
• How to debug a system at both the hardware and software level
• How to connect peripherals to the MicroBlaze AXI-bus system
• How to facilitate/utilize memory-mapped I/O with our peripherals
• How to use DeepSpeech on a laptop with limited UART capabilities
  • UART at 115200 Baud, 1 stop-bit, and no parity gives a theoretical max transfer rate of 11.52KB/s

As we shall see, some of these problems required elaborate solutions, whereas others demanded a more brute-force, trial-and-error approach. These solutions will be detailed in Part II of this article, so stay tuned!

Quick Note: The original post overlooked a significant detail. In order for this process to work, you must flash the Nano with Jetpack 4.5 on the microSD card before you attempt to boot from USB. This is because the board will not allow boot from USB until the QSPI chip has been updated with new instructions, but that can only happen if you update to Jetpack 4.5 or later. The easiest and quickest way is to use Nvidia’s SDK Manager. You can follow this guide on Nvidia’s website to flash a microSD card with 4.5 using SDK Manager. The rest of this post depends on you having flashed a microSD with 4.5.

Requirements

I will assume you have the following components, all of which are necessary to complete the process:

• A PC running Ubuntu 18.04 (16.xx and 20.xx were not tested, but they may still work)
• A USB 3.0 flash drive (USB 2.0 is fine too, but you won’t be getting the best bang for your buck) with at least 16GB of space, preferably larger (I will use a 128GB drive in this guide)
• A Jetson Nano powered via barrel jack connector (this won’t work if you use the mini-USB port to power the board, since we need to use the mini-USB for data transfer during the flashing process)
• A jumper wire to put your Nano into recovery mode
• You must flash a microSD card with Jetpack 4.5 or later and go through the system setup (user, password, locality, etc) in order to update the QSPI chip which allows later booting from USB.

Note: this process has only been tested and verified on Nano’s with SD card slots and not on Nano’s with built-in eMMC modules.

Step 1: Install Host Machine Dependencies

Run the following commands on your host machine

sudo apt-get update
sudo apt-get install qemu-user-static

Step 2: Setup USB Drive

1. Run the following command twice, once before inserting the USB and once after inserting the USB. We run it twice to be 100% sure that we are wiping and writing to the correct device. In this example, the device we want to use is /dev/sdc, but it may be different on your host machine.

sudo lsblk -p -d | grep sd
>    /dev/sda       8:0    0 465.8G  0 disk
>    /dev/sdb       8:16   1    16M  1 disk

Insert the USB device…

sudo lsblk -p -d | grep sd
>    /dev/sda       8:0    0 465.8G  0 disk
>    /dev/sdb       8:16   1    16M  1 disk
>    /dev/sdc       8:32   1 114.6G  0 disk

2. Create environment variables to make the following snippets easier to copy/paste. Note that the USB_SIZE variable below is not the full size of the disk. You can choose whatever size you want, as long as it’s at least 16GB, but I left some space for an additional partition incase I need it later. EXTREMELY IMPORTANT: make sure you change sdX to the correct value from above in the following commands. YOU CAN DESTROY YOUR HOST SYSTEM IF YOU DO NOT DO THIS CORRECTLY sdX must be changed to point to the USB drive you intend to install Jetpack on. In the case above, we are using sdc and sdc1, but you may need to use sdb or something else entirely.

export SDX=sdX
export SDX_PART=sdX1
export USB_SIZE=96GB

3. We’re going to wipe the USB drive with the following commands. First, we need to make sure there are no existing partitions on the USB. If there are, we need to unmount them before proceeding.

sudo lsblk | grep sd

sda           8:0    0 465.8G  0 disk
├─sda1        8:1    0   512M  0 part /boot/efi
└─sda2        8:2    0 465.3G  0 part /
sdc           8:16   1 115.7G  0 disk
└─sdc1        8:17   1  89.4G  0 part /media/rob/0640288e...

• Note that, in my case, there is an entry under sdc called sdc1, and it is mounted at location /media/rob/0640288e.... We need to unmount that partition before continuing.

# --------------------------------------------------------
# Only required if you have a mounted partition from above
# --------------------------------------------------------

sudo umount <location>

# --------------------------------------------------------
# Where <location> in my case is equal to /media/rob/0640288e...
# --------------------------------------------------------

• Verify that the partition was successfully unmounted by running sudo lsblk | grep sd again. Once that is done, run the following command to wipe the USB Drive.

sudo dd if=/dev/zero of=/dev/${SDX} bs=1M count=16000 status=progress

• This command reads from a constant stream of zero’s located at /dev/zero and writes that to the drive 1MB at a time. Note that we are only writing about 16GB of data to the drive. You can choose a larger count, but it could take a very long time to complete. 16GB should be sufficient to create a boot drive.

4. Create a new GUID Partition Table (GPT)

sudo parted /dev/${SDX} mklabel gpt

> Warning: The existing disk label on /dev/sdc will be destroyed and all data on this disk will be lost. Do you want to continue?
Yes/No? yes

• Note: If you get an error like the following…

> Error: Partition(s) 1 on /dev/sdc have been written, but we have been unable to inform the kernel of the change, probably because it/they are in use.  
> As a result, the old partition(s) will remain in use.  You should reboot now before making further changes.
Ignore/Cancel? cancel

• Type cancel + Enter. This error means that the USB device is mounted and you must unmount it before continuing. See steps above.

5. Create a new partition on the USB drive and mount it.

sudo parted /dev/${SDX} mkpart APP 0GB ${USB_SIZE}
sudo mkfs.ext4 /dev/${SDX_PART}
sudo mount /dev/${SDX_PART} /mnt

• The USB Drive should now be ready for writing. Next, we prepare the the files that will be written to USB.

Step 3: Download and Prepare BSP and rootfs

cd ~/
wget https://developer.nvidia.com/embedded/L4T/r32_Release_v5.0/T210/Tegra210_Linux_R32.5.0_aarch64.tbz2
wget https://developer.nvidia.com/embedded/L4T/r32_Release_v5.0/T210/Tegra_Linux_Sample-Root-Filesystem_R32.5.0_aarch64.tbz2

Step 4: Setup Environment Variables

export L4T_RELEASE_PACKAGE=Tegra210_Linux_R32.5.0_aarch64.tbz2
export SAMPLE_FS_PACKAGE=Tegra_Linux_Sample-Root-Filesystem_R32.5.0_aarch64.tbz2
export BOARD=jetson-nano-devkit

Step 5: Extract and Apply

tar xf ${L4T_RELEASE_PACKAGE}
cd Linux_for_Tegra/rootfs/
sudo tar xpf ../../${SAMPLE_FS_PACKAGE}
cd ~/Linux_for_Tegra
sudo ./apply_binaries.sh

Note: The final line should say “Success!” is everything worked properly.

Step 6: Patch l4t-payload-updater

cd ~/Linux_for_Tegra/rootfs/usr/sbin
sudo rm l4t_payload_updater_t210
sudo wget https://developer.nvidia.com/l4t-payload-updater-t210 -O l4t_payload_updater_t210
sudo chmod +x l4t_payload_updater_t210

Step 7: Recovery Mode and Connect

1. Power down the Nano
2. Put the Nano into “recovery” mode.
   • For revision B01, place a jumper wire across pins 9 and 10 of the button header (J50, underneath the heatsink module)
   • For revision A02, place a jumper wire across pins 3 and 4 of the button header (J40, near camera header)
   • Correct configuration for revision B01 is shown in the image below.
3. Connect the mini-USB cable from the Nano to your host machine.
4. Apply power to turn the Nano on.
5. After 3 seconds, disconnect the jumper wires.
6. Verify that the Nano is in recovery mode by executing lsusb on your host. You should see something like Bus 001 Device 011: ID 0955:7f21 NVidia Corp.

Step 8: Mount, Copy, Unmount

• Note: We are not changing sda1 to ${SDX_PART} in the following code. You must keep it as sda1.

cd ~/Linux_for_Tegra
sudo BOOTDEV=sda1 ./flash.sh --no-flash ${BOARD} sda1
sudo mkdir tmp_system
sudo mount bootloader/system.img.raw ./tmp_system/
sudo rsync -axHAWX --numeric-ids --info=progress2 --exclude=/proc ./tmp_system/ /mnt
sudo umount /mnt
sudo umount ./tmp_system

Step 9: Jetson Nano Peripheral Setup

1) Power down the Jetson Nano. Make sure the jumper wires for recovery mode are removed.
2) Remove the USB Drive from your host machine.
3) Remove the SD Card (if any) from the Jetson Nano. This is an important step since, if an SD Card is present, the Nano will always boot from SD rather than USB.
4) Remove the mini-USB connection between your host machine and the Jetson Nano.
5) Insert the USB Drive into the Jetson Nano.
6) Ensure that any USB accessories (mouse, keyboard) and HDMI are plugged in before turning the power back on.

Step 10: Power On and Complete System Setup

• After the SD Card has been removed and the USB + peripherals have been inserted, apply power to the Jetson Nano. If everything worked correctly, you will briefly see some system dialog followed by the typical system setup process. Complete this process and the device will reboot. Next, we will want to install system libraries, SDK’s, and dependencies. I will be writing a guide on how to easily install these components using Nvidia’s SDK Manager as soon as possible. Check back soon!

This guide will provide the necessary steps to make generating and updating engine files super simple. We will be using Nvidia’s PeopleNet network, which utilizes ResNet34. Instructions on downloading the pre-built pruned version of the encrypted TLT for this model are given below.

Note: This process should work on any Jetson device, but we will use the Nano because it is the most restricted in terms of on-board graphics memory. The tool we will be using, tlt-converter, uses TensorRT to convert a pre-built .etlt file and hence requires sufficient memory.

It is assumed that you are running TensorRT version 7.1.x and CUDA 10.2, which is the versions included in Jetpack 4.4 and 4.5 as of this writing.

More information can be found on Nvidia’s Deploying to Deepstream guide.

Step 1: Download TLT-CONVERTER

While there are technically two methods for retrieving and using tlt-converter, this article will only focus on downloading the binary to be used directly on the Jetson Nano. The other method is to use nvidia-docker, but it requires an x86 host machine. If you prefer this method, which requires you to install TLT on your x86 host, you can find more information here.

Update (Feb 26, 20201): It appears the link to download tlt-converter is changed periodically by Nvidia. This was brought to my attention by a comment made by AnthonyJ. Unfortunately, that means the process will be made slightly more complicated, since you will need to navigate to Nvidia’s website to download the binary rather than using wget. You can find the download link here under the subheading “Instructions for Jetson”. Just make sure you save the zip file to your home directory (~/). You can either manually unzip it or use the unzip command below. Thanks again to AnthonyJ for pointing this out!

From Jetson Nano:

sudo apt-get update
sudo apt-get install libssl-dev

# Only if you want to use the unzip utility
sudo apt-get install unzip
unzip tlt-converter.zip -d ~/tlt-converter

cd tlt-converter
chmod +x tlt-converter
sudo mv tlt-converter /usr/bin

This installs (or updates) the unzip utility, as well as the SSL library that we will require later on when we run tlt-converter. It also downloads the zip file and extracts the Readme and tlt-converter binary. Further, it adds execution permissions so you can run the binary, and places it in the user’s binary folder which should be in your PATH. To verify this folder is in your PATH, run:

echo $PATH

You should see a colon-separated list with various paths, and /usr/bin should be one of them. At this point, you should be able to run the following command:

tlt-converter -h

If everything was successful, you will get a print out similar to the following:

usage: tlt-converter [-h] [-v] [-e ENGINE_FILE_PATH]
[-k ENCODE_KEY] [-c CACHE_FILE]
[-o OUTPUTS] [-d INPUT_DIMENSIONS]
[-b BATCH_SIZE] [-m MAX_BATCH_SIZE]
[-w MAX_WORKSPACE_SIZE] [-t DATA_TYPE]
[-i INPUT_ORDER]
input_file
...

If you get an error complaining about TensorRT or otherwise, it is quite possible that you do not have the correct version of TensorRT installed. Please ensure that you are using TensorRT 7.1.x and CUDA 10.2

Step 2: Download the encrypted tlt file

We need a sample file to work with, so for now let’s download a pre-built and pruned version of ResNet34 PeopleNet. You do not need to have DeepStream installed to run the resulting engine, since you can run it as a standalone TensorRT engine, so we will not assume that you have DeepStream installed at this point. Download the pre-built .etlt file and store it in your home directory:

wget https://api.ngc.nvidia.com/v2/models/nvidia/tlt_peoplenet/versions/pruned_v1.0/files/resnet34_peoplenet_pruned.etlt -O ~/resnet34_peoplenet_pruned.etlt

Note: If you do have DeepStream installed with the included samples, you can find much more information on the various pre-built encrypted TLT files in the README typically located at:

/opt/nvidia/deepstream/deepstream-5.0/samples/configs/tlt_pretrained_models/README

Step 3: environment variables

Until now, all of the steps would work on just about every Jetson device without trouble. However, there are a few key differences that set the Nano apart from devices like the TX2 or Xavier. The following environment variables reflect the Nano’s limitations in memory and processing power. Feel free to tune them to your needs, but these are the bare-bones defaults for converting on a Nano:

export TRT_LIB_PATH=”/usr/lib/aarch64-linux-gnu”
export TRT_INC_PATH=”/usr/include/aarch64-linux-gnu”
export INPUT_DIMENSIONS=3,544,960
export ENCODE_KEY=tlt_encode
export BATCH_SIZE=1
export ENGINE_FILE_PATH=resnet34_peoplenet_pruned.etlt_b1_gpu0_fp16.engine
export INPUT_ORDER=nhwc
export MAX_BATCH_SIZE=2
export OUTPUTS=output_bbox/BiasAdd,output_cov/Sigmoid
export DATA_TYPE=fp16
export MAX_WORKSPACE_SIZE=1610612736
export MODEL_IN=resnet34_peoplenet_pruned.etlt

Explanation:

• TRT_LIB_PATH and TRT_INC_PATH: Simply point to the directory where your TensorRT shared library files are located.
• INPUT_DIMENSIONS: Specify the input to ResNet34, in order of channels, height, width
• ENCODE_KEY: The key that was used to generate the .etlt file. In this case, it was provided by Nvidia.
• BATCH_SIZE: Calibration is a step performed by the builder when deciding suitable scale factors for inference. We set it to 1 because of memory considerations on the Nano. The default is 8. This is only relevant for converting the .etlt file to a TensorRT engine.
• ENGINE_FILE_PATH:The output file name. Note that we did not prepend anything to the filename, meaning it will be stored in the same location where you run tlt-converter from.
• INPUT_ORDER: Specifies the order in which INPUT_DIMENSIONS should be inferred. I have a post on Nvidia’s developer forums because, quite frankly, the INPUT_ORDER seems counter-intuitive to be at the time of this writing. I will update the post once I learn more. Suffice to say, nhwc works for this example.
• MAX_BATCH_SIZE: Specifies the maximum TesnorRT engine batch size with a default value of 16.
• OUTPUTS: Specifies the outputs expected for each node.
• DATA_TYPE: Specifies the data type used in the model.
• MAX_WORKSPACE_SIZE: Specifies the maximum TensorRT workspace size. This is probably the most significant setting for the Jetson Nano. I tried various values here, from the default of 1<<30=1073741824=1Gb to 1<<31=2147483648=2Gb and the value 1610612736=1.5Gb happens to work the best. The default value does not provide enough memory for TensorRT to employ various “tactics” and results in a warning, but it still technically works. The 2Gb value results in an out-of-memory exception. I found that using 1.5Gb will result in a workspace size warning about 15% of the time, but more often then not, it is sufficient to let TensorRT do its thing without complaining.
• MODEL_IN: Is simply the input .etlt file.

Step 4: Converting (and waiting)

With the environment variables setup, we should be good to go on converting the encrypted TLT file to a TensorRT engine. We will assume that you saved the resnet34_peoplenet_pruned.etlt file in your home directory (cd ~) and you have exported all of the environment variables from above.

tlt-converter \
    -d $INPUT_DIMENSIONS \
    -k $ENCODE_KEY \
    -b $BATCH_SIZE \
    -e $ENGINE_FILE_PATH \
    -i $INPUT_ORDER \
    -m $MAX_BATCH_SIZE \
    -o $OUTPUTS \
    -t $DATA_TYPE \
    -w $MAX_WORKSPACE_SIZE \
    $MODEL_IN

This could take anywhere for 1-5 minutes, depending on whether or not TensorRT complains about maximum workspace memory. I found the resetting the Nano typically resulted in faster conversion times, most likely because the GPU memory is wiped and not taken up by other processes.

The result should be a new TensorRT engine file in your /home/username directory. If you have DeepStream or some other TensorRT pipeline setup, feel free to try it out! Be sure to leave and questions or comments below and I will make my best attempt to reply. Make sure you subscribe to get additional quality content in the future! Otherwise, look for an upcoming post where I discuss configuring and running PeopleNet on the Nano!