Which Language Model Do You Prefer?

Our choices of “favored” language models will probably remain somewhat idiosyncratic for a while, until some winnowing of market leaders occurs and a stable structure emerges. 

Most casual users will probably simply rely on ChatGPT and likely have no way to evaluate nuances of different engines. Others might have some familiarity with a few different models, but have difficulty explaining their impressions of the differences between models.

Also, models can change over time. Very early on, I found using Gemini frustrating. Though it was among the best for importing results to Google productivity apps, the other models have gradually gotten easier to use, in that regard. But ease of use was not the key performance indicator, at least for me. 

Since I do lots of forecasting-type work, Gemini’s earlier versions were often frustrating for refusal to produce such content. That does not seem to be the case for the latest models, so I assume there were short-term guardrails put into place for essentially-regulatory or other business reasons (such as avoiding the embarrassment of nonsensical, libelous or dangerous answers). 

For casual, everyday uses, though, I increasingly rely on Google search, sometimes with its AI Mode enabled, but often not even bothering to do so, as the results will include such results anyhow. 

So much for language models “killing search.”

As Gemini’s performance has improved, I find it use it more, and ChatGPT less. Grok does seem to provide more punchy, interesting commentary, but Perplexity or Claude seem better when I need to document sources. 

As a non-coder, I never can evaluate that use case. 

But here’s another take on the strengths of various models. 

source: Special Situations Research, Seeking Alpha, Bret Jensen

And market share varies when looking at enterprise or consumer adoption. Anthropic (Claude) has become a leader in enterprise model spending, with an estimated 40 percent market share as of late 2025.

ChatGPT remains dominant in the consumer space, with 74 percent of the consumer LLM market as of May 2025, but declining into the 60-percent range by early 2026.

Google's Gemini is gaining share in both consumer and enterprise market segments.

I'm not sure what is happening with open source (Meta Llama, Mistral, Chinese model share), but should be growing among enterprises seeking to avoid vendor lock-in and manage data privacy, or for academic users. 

Walk for Peace Reaches Washington, D.C.

 The #WalkforPeace monks have reached Washington, D.C. 

Moving Towards Generative User Interface

There’s a reason enterprise software has taken a beating in financial markets recently: nobody is sure how much value language models are going to destroy.

We are moving toward Generative UI, where the interface doesn't exist until you ask for it. If you need a specific chart, the LLM generates that specific chart in the chat window, for example. 

There are going to be lots of business model changes for enterprise and consumer software. 

Once the task is done, the interface disappears. This "ephemeral" UI is far more efficient than static dashboards, posing a direct threat to any software whose main value is "organizing data into screens."

Instead of static UI components, Generative UI introduces self-evolving interfaces that dynamically respond to user needs, much like how Generative AI models produce text, images, or code on demand, generating the application’s interface on the fly based on user intent.

By 2026, this technology is shifting the power dynamic from software vendors (who dictate workflows) to users.

Industry	Traditional Barrier	GenUI Disruption
Customer Relationship Management	Manual data management & "Tab Fatigue."	Outcome-based workspaces that appear only when needed.
Enterprise Resource Planning	Extreme complexity & high training costs.	Natural language translation of business data into simple "Action Cards."
Creative	Technical skill & "Steep Learning Curves."	Intent-driven canvases where the AI handles technical execution.

In a traditional CRM, sales reps spend up to 70 percent of their time navigating tabs, logging calls, and updating pipeline stages. GenUI replaces the static "account page" with an ephemeral workspace: just ask a question about a customer account. 

When a sales manager asks "which deals are at risk due to lack of executive engagement," GenUI doesn't just list them; it builds a temporary interface showing a side-by-side comparison of email sentiment, a "ghost" organizational chart of the client, and a pre-drafted calendar invite for a "check-in" meeting.

The concept of "searching for a record" disappears, as “the UI is the search.”

You talk to the CRM, and the specific fields you need to edit materialize in front of you, then vanish when the task is done.

ERPs have been difficult to navigate. GenUI democratizes the ERP by acting as a translator between complex business logic and human intent.

A procurement officer sees a news alert about a port strike. Instead of digging through Oracle's supply chain module, they ask the GenUI to "visualize the impact on our Q3 inventory." 

The system instantly renders a custom map and a "what-if" slider tool that lets the user simulate different shipping routes—functionality that might have taken a developer weeks to build as a permanent feature.

For reconciliation or expense audits, instead of a spreadsheet of 10,000 rows, the interface generates a "review card" for the five most suspicious transactions, with integrated buttons to "Approve," "Flag," or "Ask Employee for Receipt."

Creative software such as Adobe can take years to master. In web or UI design (Adobe XD or Figma), a designer can say, "Create a high-fidelity checkout page for a luxury watch brand." The GenUI generates editable layers, buttons, and cascading style sheets. 

Industry	Traditional Barrier	GenUI Disruption
CRM	Manual data management & "Tab Fatigue."	Outcome-based workspaces that appear only when needed.
ERP	Extreme complexity & high training costs.	Natural language translation of business data into simple "Action Cards."
Creative	Technical skill & "Steep Learning Curves."	Intent-driven canvases where the AI handles technical execution.

But an AI interface can potentially deliver all these capabilities through natural language, collapsing the feature hierarchy that supported tiered pricing models. 

On the other hand, there are cost issues distinct from traditional software as a service, where serving additional users costs almost nothing.

A company providing AI-powered customer service might pay $0.50-$2.00 per complex interaction in application programming interface costs alone. This fundamentally changes unit economics, as costs scale with usage intensity, not just user count. 

When software products use similar underlying models (Claude, GPT-4 and others), differentiation also becomes an issue. Why pay for ten different AI-powered tools when they're all essentially wrappers around the same language model?

So revenue is challenged while costs grow. 

Software Category	Traditional Revenue Model	AI-Induced Challenge	Potential Adaptation
CRM Systems	Per-seat licensing plus tier-based features (Basic/Pro/Enterprise)	AI can deliver "Enterprise" insights to Basic users; computational costs scale with data analysis	Usage-based pricing on AI features; charge for proprietary data connections and workflows
Project Management	Tiered subscriptions based on team size and features	Natural language interface collapses feature differentiation between tiers	Shift to charging for outcomes (projects delivered, efficiency gains) rather than features
Legal Research	Flat subscription or per-search fees	General LLMs can perform basic legal research; commoditizes core product	Focus on verified, citation-quality results; charge premium for liability/accuracy guarantees
Business Intelligence	Per-user licenses and data volume tiers	AI democratizes analytics; hard to charge more for "advanced" users who just ask better questions	Charge for data integration complexity, governance features, and certified insights rather than analysis capability
Customer Support	Per-agent seat licenses	AI reduces headcount needs (fewer seats sold); usage costs rise with ticket volume	Shift to per-resolution or per-customer pricing; charge for AI training on company data
Writing Tools	Monthly subscription ($10-30)	Directly competes with ChatGPT/Claude at $20/month with broader capabilities	Specialize in specific domains (academic, technical); integrate tightly with existing workflows
Code Editors/IDEs	Freemium or one-time purchase	AI coding assistants add significant per-user compute costs	Usage-based pricing on AI features while keeping base editor affordable
Design Software	Perpetual license or subscription	AI generation features expensive to operate; threatens margins on traditional tools	Separate pricing for generative AI features; charge for commercial usage rights
HR/Recruiting	Per-job-posting or per-hire fees	AI can screen resumes and match candidates, but at compute cost per evaluation	Charge for quality of matches and time-to-hire improvement rather than volume
Email, Productivity	Bundled suite pricing	AI features (smart compose, summarization) add costs that vary dramatically by user	Tiered AI quotas; charge power users more for intensive AI feature usage

Enterprise customers may be more tolerant of usage-based pricing since they're accustomed to paying for value delivered. 

But consumer products face harsher constraints. Users expect fixed, predictable monthly fees and react negatively to usage limits.

The fundamental question remains: as AI capabilities become more uniform and accessible, how do software companies justify premium pricing? The answer likely involves some combination of specialized data, deep workflow integration, reliability guarantees, and human expertise.

But all that introduces new levels of uncertainty into the value and valuation of enterprise software companies.

Goldens in Golden

There's just something fun about the historical 2,000 to 3,000 mostly Golden Retrievers in one place, at one time, as they were Feb. 7, 2026 in Golden, Colorado, at the annual "Goldens in Golden" event. 

One estimate is that as many as 6,000 dogs attended this year, with perhaps 12,000 people. 

Lots of people are local, of course (from Colorado), but attendees come from all over the United States and apparently even some other countries. 

Some of the retrievers have their own social media accounts, so recorded the event using their own GoPros!

And there's the mandatory group shot. 

How Much Value Will Language Models Shift Away from Enterprise Software?

There’s a reason enterprise software has taken a beating in financial markets recently: nobody is sure how much value language models are going to destroy.

We are moving toward Generative UI, where the interface doesn't exist until you ask for it. If you need a specific chart, the LLM generates that specific chart in the chat window, for example. 

There are going to be lots of business model changes for enterprise and consumer software. 

Once the task is done, the interface disappears. This "ephemeral" UI is far more efficient than static dashboards, posing a direct threat to any software whose main value is "organizing data into screens."

Instead of static UI components, Generative UI introduces self-evolving interfaces that dynamically respond to user needs, much like how Generative AI models produce text, images, or code on demand, generating the application’s interface on the fly based on user intent.

By 2026, this technology is shifting the power dynamic from software vendors (who dictate workflows) to users.

Industry	Traditional Barrier	GenUI Disruption
Customer Relationship Management	Manual data management & "Tab Fatigue."	Outcome-based workspaces that appear only when needed.
Enterprise Resource Planning	Extreme complexity & high training costs.	Natural language translation of business data into simple "Action Cards."
Creative	Technical skill & "Steep Learning Curves."	Intent-driven canvases where the AI handles technical execution.

In a traditional CRM, sales reps spend up to 70 percent of their time navigating tabs, logging calls, and updating pipeline stages. GenUI replaces the static "account page" with an ephemeral workspace: just ask a question about a customer account. 

When a sales manager asks "which deals are at risk due to lack of executive engagement," GenUI doesn't just list them; it builds a temporary interface showing a side-by-side comparison of email sentiment, a "ghost" organizational chart of the client, and a pre-drafted calendar invite for a "check-in" meeting.

The concept of "searching for a record" disappears, as “the UI is the search.”

You talk to the CRM, and the specific fields you need to edit materialize in front of you, then vanish when the task is done.

ERPs have been difficult to navigate. GenUI democratizes the ERP by acting as a translator between complex business logic and human intent.

A procurement officer sees a news alert about a port strike. Instead of digging through Oracle's supply chain module, they ask the GenUI to "visualize the impact on our Q3 inventory." 

The system instantly renders a custom map and a "what-if" slider tool that lets the user simulate different shipping routes—functionality that might have taken a developer weeks to build as a permanent feature.

For reconciliation or expense audits, instead of a spreadsheet of 10,000 rows, the interface generates a "review card" for the five most suspicious transactions, with integrated buttons to "Approve," "Flag," or "Ask Employee for Receipt."

Creative software such as Adobe can take years to master. In web or UI design (Adobe XD or Figma), a designer can say, "Create a high-fidelity checkout page for a luxury watch brand." The GenUI generates editable layers, buttons, and cascading style sheets. 

Industry	Traditional Barrier	GenUI Disruption
CRM	Manual data management & "Tab Fatigue."	Outcome-based workspaces that appear only when needed.
ERP	Extreme complexity & high training costs.	Natural language translation of business data into simple "Action Cards."
Creative	Technical skill & "Steep Learning Curves."	Intent-driven canvases where the AI handles technical execution.

Traditional software companies built moats by accumulating features over years. But an AI interface can potentially deliver all these capabilities through natural language, collapsing the feature hierarchy that supported tiered pricing models. 

On the other hand, there are cost issues distinct from traditional software as a service, where serving additional users costs almost nothing.

A company providing AI-powered customer service might pay $0.50-$2.00 per complex interaction in application programming interface costs alone. This fundamentally changes unit economics.

Software companies face costs that scale with usage intensity, not just user count. Freemium models become harder to sustain when free users generate actual expenses.

When software products use similar underlying models (Claude, GPT-4 and others), differentiation becomes an issue. Why pay for ten different AI-powered tools when they're all essentially wrappers around the same language model?

So revenue is challenged while costs grow. 

A big question is how much enterprise software value language models can displace. 

As AI models become more capable, users can increasingly go directly to ChatGPT or Claude instead of using specialized vertical applications. 

Software Category	Traditional Revenue Model	AI-Induced Challenge	Potential Adaptation
CRM Systems	Per-seat licensing plus tier-based features (Basic/Pro/Enterprise)	AI can deliver "Enterprise" insights to Basic users; computational costs scale with data analysis	Usage-based pricing on AI features; charge for proprietary data connections and workflows
Project Management	Tiered subscriptions based on team size and features	Natural language interface collapses feature differentiation between tiers	Shift to charging for outcomes (projects delivered, efficiency gains) rather than features
Legal Research	Flat subscription or per-search fees	General LLMs can perform basic legal research; commoditizes core product	Focus on verified, citation-quality results; charge premium for liability/accuracy guarantees
Business Intelligence	Per-user licenses and data volume tiers	AI democratizes analytics; hard to charge more for "advanced" users who just ask better questions	Charge for data integration complexity, governance features, and certified insights rather than analysis capability
Customer Support	Per-agent seat licenses	AI reduces headcount needs (fewer seats sold); usage costs rise with ticket volume	Shift to per-resolution or per-customer pricing; charge for AI training on company data
Writing Tools	Monthly subscription ($10-30)	Directly competes with ChatGPT/Claude at $20/month with broader capabilities	Specialize in specific domains (academic, technical); integrate tightly with existing workflows
Code Editors/IDEs	Freemium or one-time purchase	AI coding assistants add significant per-user compute costs	Usage-based pricing on AI features while keeping base editor affordable
Design Software	Perpetual license or subscription	AI generation features expensive to operate; threatens margins on traditional tools	Separate pricing for generative AI features; charge for commercial usage rights
HR/Recruiting	Per-job-posting or per-hire fees	AI can screen resumes and match candidates, but at compute cost per evaluation	Charge for quality of matches and time-to-hire improvement rather than volume
Email/Productivity	Bundled suite pricing	AI features (smart compose, summarization) add costs that vary dramatically by user	Tiered AI quotas; charge power users more for intensive AI feature usage

Enterprise customers may be more tolerant of usage-based pricing since they're accustomed to paying for value delivered. 

Consumer products face harsher constraints. Users expect fixed, predictable monthly fees and react negatively to usage limits.

The fundamental question remains: as AI capabilities become more uniform and accessible, how do software companies justify premium pricing? The answer likely involves some combination of specialized data, deep workflow integration, reliability guarantees, and human expertise.

Still, these represent a narrower value proposition than the feature-rich software bundles that defined the previous era, some will argue.