Link

Make machines work for humans

What the article covers

This article is written by Daryl Budiman, co-founder of Andoria AI, which describes itself as “an AI web agent that generates in-app walkthroughs for software companies.”

The article explores the question of

How do we redesign the way humans interact with the Internet?

It starts by breaking down the interactions of humans on the Internet as

1. ”extracting information"
2. "adding information"
3. "executing on information”

The article then makes a significant point

Historically, humans have always been “forced” to learn how to use software

This point sets up the rest of the article to talk about how AI (and probably AI Agents, given the writer’s background and current company) is setting up an inflection point in which the system should work around the human rather than the human working around the system.

Overall, the article asks us to consider how AI can/will enable truly human-centric design.

My Thoughts

Overall takeaway

Historically, market power in businesses has accrued to those who invested the most time in being feature-complete for a certain set of users, regardless of how complex the software’s interfaces were.

AI and AI agents are about to upend this dynamic completely.

Pattern Recognition

With every technological leap, from command lines to GUIs, power has concentrated among those who master complex interfaces.

AI and AI Agents are about to democratize this power structure.

I agree with the article that we are at a critical inflection point in how humans interact with the Internet’s technology.

Historically, (not only with the Internet), the general pattern has been:

1. New technology emerges
2. Complex interfaces are built
3. Humans invest time learning these interfaces
4. Repeat

As the article stated, “humans have always been “forced” to learn how to use software,” and then gives the example of Excel.

Excel’s pivot tables exemplify this pattern in that hundreds (thousands???) of tutorials exist to help humans adapt to this one feature.

The power dynamic this created meant influence accrued to those who mastered these complex interfaces.

Now, with AI and AI Agents, the technical challenge isn’t just building better Graphic User Interfaces (GUIs); it’s fundamentally redesigning the system to work for humans.

This redesigning of how humans use the system requires:

• Dynamic interface generation based on user context
• Adaptive learning systems that model user behavior
• Natural language processing at the interface level
• Context-aware response systems
• Focusing on human needs rather than technical constraints

The increased fluidity of AI and AI Agents empowers more humans and lessens each human’s mental load to achieve a certain goal.

As many AI Agents built today focus on business-to-business (B2B) use cases, this points to a future with significant business advantages for thinking through how best to adapt to humans.

B2B / Business Competitive Analysis

The shift from feature-centric to adaptation-centric development will likely force a complete reimagining of traditional B2B software competitive advantages.

Companies that reduce user adaptation time from weeks to days will likely capture 2-3x market share compared to competitors stuck in the traditional training model.

There are some interesting implications for product development and market dynamics.

1. First-Mover Advantage: Companies that successfully implement human-centric AI interfaces will likely capture a disproportionate market share
2. Cost Structure: Reducing the human adaptation curve could significantly lower training and adoption costs
3. Product Development: The focus shifts from feature completeness to interaction fluidity
4. Competition: The competitive moat moves from technical capabilities to user adaptation speed

Technical Teams (adaptive interface design) should be working on the following:

• Developing expertise in adaptive interface design
• Focusing on building systems that learn from user behavior
• Reducing the cognitive load of the human users rather than adding features

Product Teams (success metrics reformation) should be working on the following:

• Rethinking success metrics around human adaptation rather than feature adoption
• Developing new testing methodologies for adaptive interfaces
• Preparing for more extended development cycles with higher initial investment

Strategic Planning Teams (competitive advantage positioning) should be working on the following:

• Considering interface design as a core competitive advantage
• Planning for increased investment in user behavior analysis
• Preparing for rapid iteration as AI capabilities evolve

The other functional parts of the organization should also consider this shift and what it will mean to them.

Strategic Insight

An interesting aspect of this shift in “Making machines work for humans” is timing.

We’re at a unique moment where:

1. AI capabilities are mature enough to handle complex interaction patterns
2. Computing power is sufficient for real-time adaptation
3. User expectations are evolving beyond traditional interfaces

This convergence creates a rare opportunity to redefine human-computer interaction.

The winners in this space will not be those with the most features or the cleanest interfaces but those who best adapt their systems to human behavior.

Implementation Framework

For teams looking to implement these insights, I would recommend starting with:

1. Measuring Success

   • Adaptation speed metrics
   • User cognitive load reduction
   • Business impact correlation
   • Time-to-proficiency (target: 80% reduction)
   • User retention at key friction points
   • Revenue/efficiency

2. Auditing Current Interfaces

   • Map out where users spend time learning your system
   • Identify friction points in user adaptation
   • Look where users are dropping out of funnels to identify where too much mental work was necessary

3. Developing Adaptation Strategy

   • Start with the highest friction areas
   • Build progressive intelligence into interfaces
   • Measure reduction in user adaptation time

4. Planning Technical Architecture

   • Design for flexibility and learning
   • Build in user behavior analytics
   • Create feedback loops for system adaptation

The key implementation strategy is to move from static to adaptive systems while maintaining reliability and predictability where it matters.

While implementing this change, the focus should be on creating tight measurement loops between user behavior, system adaptation, and business impact.

Each interface change should directly correlate to reduced cognitive load and improved business metrics.

This shift isn’t just about better interfaces – it’s about fundamentally changing how humans interact with technology.

The opportunity and responsibility to shape this future is significant.

Teams implementing AI-driven interfaces and AI Agents must start auditing their highest-friction interfaces today.

Organizations have 12-18 months before this shift becomes a competitive necessity.

Organizations that wait to adapt their software to AI-driven interfaces and AI Agents risk permanent market share loss.