Etter Outline the Big Problem with AI Agents

HEADLINE

The Big Problem with AI Agents: Are We Too Early or Just Too Expensive?

SUBHEADLINE

Six months into the “agent era,” companies like Salesforce are struggling to convert hype into value. What’s really happening in the trenches of AI’s next frontier?

INTRODUCTION

• Opening hook: “We’re now six months into the agent era - a period that will likely be remembered as the infancy of a revolutionary technology. But infants stumble before they walk.”
• Establish the blurry definition between assistants and agents: “Assisted AI is using AI to do things, while agents are AI doing things for you”
• Frame the central tension: Despite massive corporate enthusiasm and investment, customer adoption is lagging
• Introduce Salesforce as our primary case study - the first major public company betting their future on agents
• End with thesis: “The challenges facing AI agents boil down to a fundamental paradox: we’re simultaneously too early and too expensive”

SECTION 1: THE STATE OF THE AGENT LANDSCAPE

• Define the current commercial “agent era” timeline (October 2023 - present)
• Historical context: How quickly we’ve transitioned from “automations” to “agents”
• Salesforce’s Agentforce as the bellwether for the industry
• Contrasting narratives:
  • Benioff’s enthusiasm: “2025 is the absolute year of Agentforce”
  • CFO’s reality check: “modest agent sales this year” and slowest growth ever (7-8%)
• Key metrics: 5,000 Agentforce deals closed, but only 3,000 paying customers

SECTION 2: THE MULTI-DIMENSIONAL PRICING CHALLENGE

• Problem 1: The conceptual pricing structure
  • Per-conversation model ($2.00/interaction) creates disincentives for usage
  • The paradox: Companies want adoption but are charging per use
  • Historical comparison: How this differs from traditional SaaS per-seat models
• Problem 2: The actual price point is prohibitive
  • Cost breakdown: $2.00 per conversation compared to alternatives
  • Example: 10 customer service interactions per hour approaches human labor costs
  • Competitive landscape: Intercom and others charging less
• Case study: Manus (the viral Chinese agent)
  • Also paying approximately $2.00 per task to Anthropic
  • Currently limiting users rather than charging them
  • Unsustainable economic model for long-term growth

SECTION 3: THE MARKET READINESS GAP

• The adoption timing problem
  • Companies still adapting to the assistant era, not ready to jump to agents
  • Resistance to the “digital labor replacement” framing
  • Quote the sales manager: “many customers aren’t ready to commit”
• Technical limitations creating adoption friction
  • Hallucination risks (more dangerous without human oversight)
  • Data connectivity challenges (especially for non-Salesforce ecosystems)
  • Hidden integration costs beyond the per-conversation fee
• Signs of desperation in sales tactics
  • Threatening price increases on other products unless customers adopt Agentforce
  • Offering to waive storage/license overage fees for Agentforce purchasers
  • Analysis of what these tactics reveal about the disconnect between strategy and market

SECTION 4: POTENTIAL CATALYSTS FOR CHANGE

• Impact of ongoing AI price wars
  • How falling intelligence costs could make agents more economically viable
  • The conflict between using cutting-edge models and controlling costs
  • Potential timeline for when economics might align with capabilities
• Strategic positioning considerations
  • Is Salesforce deliberately playing a long game?
  • Value of market education and early customer feedback
  • First-mover advantage despite being 6-18 months ahead of market readiness
• Product strategy refinements needed
  • Focusing on specific high-value use cases rather than broad deployment
  • Simplifying the offering to reduce confusion
  • Building better integration capabilities for cross-platform data access

SECTION 5: VISIONARY OUTLOOK - WHAT THE FUTURE HOLDS

• The agent era evolution timeline (12-36 months)
• Economic inflection point: When will agent ROI become undeniable?
• Prediction of upcoming consolidation and potential winners
• How new pricing models will emerge to balance adoption and profitability
• The secondary effects: How agents will reshape organization structures and workflows

CONCLUSION: THE PATH FORWARD

• Reframing the core question: It’s both timing and pricing, but in different proportions for different organizations
• Strategic advice for technology leaders navigating this transition period
• The imperative of experimentation despite current limitations
• Final powerful statement on inevitability: “The question isn’t if agents will transform business operations, but when and at what price. The companies that solve this equation first will define the next decade of enterprise technology.”

SIDEBAR/CALLOUT BOX: AGENT PRICING MODELS TO WATCH

• Per conversation/task (Current Salesforce model)
• Subscription/fixed fee with usage caps
• Value-based (percentage of cost savings or revenue generated)
• Hybrid approaches with base subscription plus variable usage
• Future models: Self-optimizing pricing based on ROI calculations

ABOUT THE AUTHOR

• Brief bio emphasizing expertise in AI, enterprise technology, and market analysis
• Link to subscribe for future insights on emerging technology trends
• Call to action for feedback and discussion