The AI Product Gap

In the rapidly evolving landscape of artificial intelligence, we're witnessing a curious phenomenon: despite exponential advances in model capabilities, most AI products remain surprisingly disappointing. This isn't just my observation—it's a sentiment echoed across tech circles, from product designers to everyday users who were promised revolutionary tools but received something far less impressive.

The False Binary of Current AI Products

Today's AI market presents us with an unfortunate dichotomy:

Option A: Chaotic and Unreliable
On one end of the spectrum, we have flashy Twitter demos showcasing "groundbreaking" AI products that, once in users' hands, deliver an experience plagued by hallucinations and unpredictable outputs. These products prioritize capabilities over reliability, often ending up as interesting toys rather than dependable tools.

Option B: Overly Constrained and Unintelligent
On the opposite end, established companies entering the AI space implement so many guardrails and constraints that their models operate with severely limited cognitive abilities. In their quest to avoid criticism and liability, they create products that are safe but fundamentally unhelpful for complex tasks.

The middle ground—AI that is both powerful and reliable—remains largely unoccupied.

Beyond Model Improvements

The common refrain is predictable: "AI products will get better when models improve." While this statement isn't wrong, it's woefully incomplete.

Raw intelligence improvements in foundation models certainly help, but they don't address the core issue with AI products today. Even as models trend toward general intelligence on universal benchmarks, they remain generic tools without proper adaptation for specific users and contexts.

The question we should be asking isn't just "How do we make smarter models?" but rather:

"How do we apply model intelligence in ways that align with each unique user's expectations, preferences, and goals?"

The Low-Hanging Fruit: Orchestration and Design

I believe there's substantial untapped potential for improving AI products through better orchestration and thoughtful product design—areas that don't necessarily require waiting for the next breakthrough model. Here are two key directions that could transform today's AI applications:

1. Personalization Through Context and Memory

AI without context is intelligence without wisdom. Products that don't understand their users are fundamentally limited in their helpfulness, regardless of their raw capabilities.

Gathering Meaningful Context

Effective AI products need to understand:

• Who the user is and what they're trying to accomplish
• What decisions they regularly face
• The environments in which they operate
• Their preferences, habits, and patterns

This requires collecting both in-product information (actions, choices, history) and external context (calendar events, location data, connected services) to form a holistic understanding.

Real-world example: Financial AI products like Ramp have demonstrated how multi-dimensional context—combining transaction data with external signals like calendar events—can dramatically improve inference quality when generating memos or categorizing expenses.

Building Memory Systems

Beyond point-in-time context, truly personalized AI needs memory. This means systematically capturing, organizing, and referencing user information over time to build an evolving understanding of preferences and patterns.

Implementing a structured memory system allows an AI to:

• Remember key details about the user without repetitive explanations
• Notice and adapt to changing preferences over time
• Build a mental model of the user that informs all interactions
• Make increasingly accurate predictions about user needs

Such systems don't require sentience—just thoughtful data structures that distill interaction history into actionable insights for future inference.

2. User Control Through Steering and Takeover Mechanisms

Even perfectly designed AI will make mistakes. The difference between frustrating and empowering products often lies in how they handle these inevitable errors.

The Power of Steering

Rather than forcing users into binary accept/reject decisions for entire outputs, sophisticated AI products should implement granular steering mechanisms. This approach, borrowed from mechanistic interpretability research, allows users to:

• Guide the AI's thought process in specific directions
• Preserve useful portions of an output while redirecting problematic sections
• Shape generations incrementally rather than starting over repeatedly
• Apply their domain expertise precisely where it's needed

The user experience challenge is significant: How do we design interfaces that make steering intuitive for non-technical users? How can we visualize an AI's thinking in ways that make intervention natural?

Despite these challenges, pioneering work from companies like Anthropic and Tilde shows that production-ready steering is already possible and potentially transformative.

The Right to Take Control

Beyond steering, users need clear pathways to take over when automation fails. This means designing systems where:

• The transition between AI and human control is seamless
• Work isn't lost when switching modes
• Users can easily understand what the AI was attempting
• The system learns from manual corrections

The Personalization-Control Flywheel

Perhaps most exciting is the potential virtuous cycle between personalization and control. Each element strengthens the other:

1. Better context enables more personalized steering mechanisms
2. User steering provides rich data on preferences that improve memory systems
3. Enhanced memory creates more accurate context for future interactions
4. Improved interactions lead to more refined steering

Products that harness this flywheel can create genuinely differentiated user experiences that improve over time—potentially establishing moats that generic AI applications can't easily overcome.

Beyond the Binary: The Path Forward

Building well-designed non-deterministic systems is undeniably challenging. Most developers reasonably choose to avoid this complexity, but this avoidance is increasingly becoming a competitive liability.

The future belongs to products that can:

• Automate increasing portions of user workloads
• Maintain user control over these automations
• Learn from interactions to become more personalized
• Handle ambiguity and uncertainty gracefully

Ultimately, users aren't interested in AI for its own sake—they want to achieve their goals faster, cheaper, and better. The AI products that succeed will be those that understand this fundamental truth and design accordingly.

The gap between today's disappointing AI products and truly transformative tools isn't primarily about model capabilities—it's about product design that harnesses those capabilities in service of specific users with specific needs. When we bridge that gap, we'll finally begin to realize the promise of an AI-augmented future.