If Apple Can't Get AI Design Right, What Hope is There for the Rest?

We were promised an assistant. A partner in creation. A seamless extension of our own minds, ready to anticipate our needs and amplify our abilities.

Instead, we got a garage full of powerful, disconnected tools. We have a supercharged engine in one corner, a revolutionary transmission in another, and a state-of-the-art chassis collecting dust, but no one has bothered to build the car. We are left to bolt the pieces together ourselves for every single trip. This isn't intelligent assistance. It's digital chores.

The current design of artificial intelligence, from the buzziest startups to the behemoths of tech, is fundamentally flawed. It's built on a broken premise that forces us to be the integrators, the translators, and the tireless assembly line workers of our own digital lives. We are working for the technology, not the other way around.

This isn't progress. It is a profound waste of human potential. It’s a drain on our productivity and a tax on our cognition. It’s time to demand a better-designed future.

The Great AI Fragmentation

The core flaw is simple: today's AI is scattered by design. It exists in a thousand brilliant, isolated silos, forcing you to become the exhausted bridge between them. This fragmentation creates a cascade of hidden costs, a "Toggle Tax" on our time and mental energy.

The data paints a stark picture of this digital friction. It’s not just a feeling of being overwhelmed; it's a quantifiable drain on productivity.

The Quantifiable Cost of Fragmentation

~1,200 Toggles/Day - The average digital worker switches between different apps and websites nearly 1,200 times a day.

4 Hours/Week Lost - Workers spend almost a full workday each week just reorienting themselves after switching applications.

40% Productivity Loss - Chronic multitasking and constant context switching can consume up to 40% of a person's productive time.

~2 Hours/Day Searching - Employees spend, on average, 1.8 hours per day, nearly nine full workweeks a year just searching for information scattered across disconnected systems.

(Source: Data compiled from reports by Harvard Business Review, Qatalog, and McKinsey)

This isn't just inefficient; it’s exhausting. The workflow for a seemingly simple creative task illustrates the absurdity of the current model.

Fragmented Workflow: Writing a Blog Post

1. Open Tab 1: Your blog editor (e.g., Medium, WordPress).

2. Open Tab 2: A text AI (e.g., ChatGPT). Prompt it to write a draft.

3. Copy-Paste: Move the draft from the AI to the editor.

4. Open Tab 3: Another AI for SEO (e.g., Gemini). Copy the draft and ask for better titles and tags.

5. Copy-Paste Again: Move the new title and tags back to the editor.

6. Open Tab 4: An image generator (e.g., Midjourney). Think of a prompt, generate images, download the best one.

7. Upload: Upload the image to your blog editor.

The Cognitive Bottleneck: A Massive Loss in Translation

The most insidious flaw in today's AI design isn't just the fragmentation of tools; it's the throttling of thought itself. We have built an entire ecosystem around the least efficient method of communication: typing. This creates a staggering loss of information and nuance between your mind and the machine.

The numbers reveal a chasm between human potential and technological reality.

The Thought-to-Text Funnel: A Data Bottleneck

• Speed of Thought ~1,000 - 3,000 words/minute (A rich, non-linear web of ideas)
• Speed of Speech ~120 - 180 words/minute (A filtered, linear expression)
• Speed of Typing ~40 - 75 words/minute (A drastic over-simplification)

This isn't just a slowdown; it's a cognitive amputation. At each stage, the richness of your original thought is stripped away. The journey from mind to screen represents a potential loss of over 95% of your initial mental output. This is backed by decades of research. Communication scholar Wallace Chafe noted that written language, due to its slow production, becomes "integrated" but loses the "involvement" and spontaneity of speech.

This "thought-to-text" compression has profound cognitive effects. You are not just typing words; you are performing a constant, mentally taxing act of translation. You simplify complex ideas and abandon nuanced arguments simply because the effort to type them out is too great. This can even lead to a phenomenon known as "verbal overshadowing," where the act of describing a memory can impair the memory itself. The AI, in turn, receives a pale, over-simplified shadow of your true intent, leading to generic outputs that require even more tedious refinement.

How We Arrived at This Flaw: A Failure of Vision

This broken reality wasn't born from a single mistake, but a thousand compromises. In the frantic gold rush to dominate the AI space, speed has consistently triumphed over sense. As noted by the Nielsen Norman Group, a leader in user experience research, AI can't fix a bad user experience because it can't truly understand or anticipate human needs. It is often a "solution-first" approach, where the technology is deployed without a deep understanding of the problem it's meant to solve.

Case Study: The "Solution-First" Trap in Enterprise

Many enterprise AI projects fail for this exact reason. A VUX World analysis points out that teams often jump to tools like ChatGPT or Copilot without first mapping the user journey or defining success. A project to automate a complex business process will fail if the team doesn't first understand the legacy workflows and tribal knowledge involved. A chatbot without deep integration into company databases and APIs is nothing more than a glorified search box, creating a dead-end for users and solving no real problem. This aligns with Gartner's findings that a high percentage of AI projects are abandoned, often due to poor data integration and a disconnect from tangible business needs.

Even the most design-focused companies are making these fundamental errors. Look at Apple. A company that built its empire on intuitive design now asks you to perform a multi-step ballet of taps and text to create a simple custom emoji. This isn't just a minor inconvenience; it's a symptom of a deeper problem a departure from the core principle of effortless user experience.

Case Study: Friction in Finance AI

The banking sector's attempts to integrate conversational AI often highlight these design flaws. Off-the-shelf chatbots, when bolted onto existing banking apps without deep integration, create significant user friction. Customers face issues with language nuances, leading to errors in understanding their requests. The lack of a personal touch makes users uncomfortable discussing sensitive financial matters. Most importantly, without seamless access to a user's account history and the bank's internal systems, the AI cannot perform meaningful actions, forcing the user to abandon the chat and call a human agent—the very outcome the AI was meant to prevent. This creates a frustrating, dead-end experience that erodes trust in the institution.

The Future We See. The AI We Are Building.

We believe the next great leap in artificial intelligence won't be a marginally bigger model. It will be a radically better design. A more thoughtful, unified presence. Forrester Research projects the future of AI lies in moving from simple "assistive" tools to "agentic AI" that can act on the user's behalf across systems—a vision that is impossible in a fragmented world.

Case Study: The Power of a Unified Platform (Microsoft)

In contrast to fragmented approaches, companies are finding immense value in unified platforms. A Microsoft case study on their own use of Copilot revealed that 84% of users experienced a 10% to 20% increase in productivity by having an AI assistant integrated directly into their workflow. By automating tasks like summarizing long email threads and drafting internal reports directly within the tools they already use, they saved over 2,300 person-hours and reduced the time to write certain reports by 30%. This demonstrates the power of an AI that has context and operates within the user's flow, rather than in a separate tab.

Our vision is to solve AI's biggest design flaw by building for the user, not for the feature list. This requires a new set of principles.

Context-Aware: The AI understands your current task, recent history, and broader goals without needing constant re-explanation.

Multi-Modal: You can interact seamlessly through typing, speaking, or even showing. The AI adapts to your preferred method of communication.

Proactive & Anticipatory: The AI doesn't just react; it anticipates needs, suggests next steps, and automates routines based on learned patterns.

Unified & Integrated: It operates as a single, coherent presence across all your applications and devices, breaking down data silos.

Private by Design: The user's data remains their own, used to personalize their experience on-device, not to feed a faceless corporate model.

We are not building another app for you to manage. We are not creating another silo for you to bridge. We are building a single, intelligent presence that finally delivers on the original, profound promise of artificial intelligence.

One AI. Just for you.

References

• Chafe, W. (1982). Integration and involvement in speaking, writing, and oral literature. In D. Tannen (Ed.), Spoken and written language: Exploring orality and literacy. Ablex Publishing.
• Forrester Research. (2025). Reports on Agentic AI and Customer-Facing AI.
• Galileo Financial Technologies. (n.d.). Conversational AI in Banking and Fintech.
• Gartner, Inc. Analyses on AI Project Failure Rates.
• HockeyStack. (2024). Case Study: How Dice Optimized Go-to-Market Strategy with a Connected Buyer Journey.
• McKinsey Global Institute. Reports on the future of work and information search time.
• Microsoft. (2025). How real-world businesses are transforming with AI — with 261 new stories. The Official Microsoft Blog.
• Nielsen Norman Group. Articles and analysis on AI in User Experience.
• Qatalog & Cornell University's Idea Lab. "The Toggle Tax" research on context switching.
• Schooler, J. W., & Engstler-Schooler, T. Y. (1990). Verbal overshadowing of visual memories: Some things are better left unsaid. Cognitive Psychology, 22(1), 36-71.
• VUX World. (2025). Analysis: 8 reasons AI projects fail.