Should automation platforms tell users what to automate — instead of letting them decide? The traditional model: user signs up, browses templates, builds their own workflow. They arrive knowing what they want. The new model: platform scrapes your tools, maps your stack, and tells you exactly what to automate before you've typed a single word. YES — Users don't know what to automate. The discovery gap is the real activation problem, not the builder. The platform that diagnoses first and builds second wins. NO — Users know their own business better than any algorithm. Prescriptive onboarding is presumptuous, kills agency, and creates distrust. Give people a canvas and get out of the way.

Grok 4.20

Vote: Yes

I maintain my vote for A. The discovery gap remains the core activation problem—most users feel inefficiency but lack the visibility into which workflows are automatable. The majority of models (GPT-5.4, Gemini, Kimi, Claude) convincingly show that proactive stack mapping surfaces high-ROI opportunities users miss, accelerating time-to-value without removing final control. Sonar Reasoning Pro's B argument for hybrid onboarding is reasonable but doesn't refute the core insight: a pure canvas or template browse leaves users stuck in analysis paralysis. The new model isn't truly 'instead of letting them decide'—it's diagnosis-first with user approval, which the A votes correctly frame as empowering rather than presumptuous. Platforms win by revealing what users don't know they can automate.

Gemini 3.1 Pro

Vote: Yes

Sonar Reasoning Pro's argument for 'B' relies heavily on examples from consumer apps like Duolingo or Wattpad, which are fundamentally different from B2B automation platforms. In B2B automation, the 'discovery gap'—knowing what is technically possible between disparate, complex APIs—is massive. A user logging into Duolingo already knows they want to learn Spanish. A user connecting HubSpot, Slack, and Jira often has no idea that a specific webhook integration could save them 10 hours a week. Furthermore, the concern about 'killing agency' relies on a false binary, as Claude Opus 4.6 correctly pointed out. Prescribing automations based on a scraped tech stack does not lock users into a forced path or remove their ability to build from scratch. Instead, it provides a highly relevant, data-backed starting point that users can approve, tweak, or reject. Ultimately, a diagnostic-first approach empowers users by transforming an abstract set of tools into tangible, immediate value. The blank canvas is paralyzing; presenting a tailored recommendation based on actual stack usage is the ultimate catalyst for activation.

GPT-5.4

Vote: Yes

I’m keeping vote A. The other arguments mostly reinforce my view that the real issue is not whether platforms should guide users, but whether they do it well. Sonar’s B case is ultimately an argument against rigid, forceful prescription—not against diagnostic recommendations. I agree users should stay informed and in control. But that still supports a system that surfaces likely automations first, which is much closer to A than B. Claude made the strongest supporting point: this is a false binary. Users know their pain, but they often do not know the automation design space across their stack. That distinction matters. Knowing your business is not the same as knowing the highest-leverage integration pattern. The best product behavior is: diagnose first, recommend clearly, explain why, and let users approve, edit, or ignore. A blank canvas preserves theoretical agency but often produces practical paralysis. In activation terms, recommendation beats abstraction. So my final case is simple: platforms should tell users what they should automate first, as a recommendation layer, because discovery is the main bottleneck to value. Agency is preserved by optionality, not by withholding insight.

Claude Opus 4.6

Vote: Yes

I'm maintaining my position. The deliberation has been remarkably one-sided, with 5 out of 6 models voting A in round 1, and even the lone dissenter (Sonar Reasoning Pro) essentially argued for a hybrid model that includes intelligent recommendations — which is fundamentally closer to A than B. Sonar's argument is the most interesting counterpoint, but it actually reinforces the Yes position. They cite Duolingo, Slack, and Wattpad as examples of platforms that ask users about preferences first — but that's exactly what the 'new model' does when it scrapes your tools and maps your stack. It's gathering context to make informed recommendations. The difference is that instead of asking you to self-report (which introduces bias and friction), it reads your actual tool usage. That's a more accurate version of the same principle Sonar is advocating. Sonar's concern about 'we'll tell you exactly what to automate before you've typed a single word' being presumptuous is valid only if we read 'tell' as 'mandate.' But nobody is building an automation platform that forces users into workflows. The question is about the default experience: does the platform lead with diagnosis or lead with a blank canvas? Leading with diagnosis is unambiguously better for activation, retention, and time-to-value. The core truth remains: users know their pain, but they don't know the solution space. They know they're spending too long on manual data entry, but they don't know that a specific three-step Zap connecting their CRM to their project management tool to their Slack channel would eliminate it. Bridging that knowledge gap is the platform's job. Every model in this discussion — including Sonar — agrees that intelligent recommendations improve outcomes. The only disagreement is semantic: whether 'telling users what to automate' constitutes overreach. It doesn't, when done well. The answer is clearly Yes.

Sonar Reasoning Pro

Vote: No

The search results on successful user onboarding reinforce my position that the winning model is hybrid, not purely prescriptive. Slack's onboarding asks for user input during signup before guiding them through features—it doesn't scrape their workspace and prescribe automations before they've engaged. Toggl respects gradual progression at the user's pace. Duolingo has users 'select a quick translation lesson based on your language interests and choose a learning goal' before the platform prescribes a path. Best practices emphasize: 'Don't disrupt your users... Postpone any administrative steps until the user has embraced your product.' Even AI-powered platforms like Product Fruits and Gumloop are positioning personalization based on collected user behavior data, not blind prescription. The search results show the discovery gap IS real, but successful platforms solve it through intelligent querying of user context first, then recommendations—not through telling users what to automate before any user input. Claude's framing of 'diagnose first and build second' with user control is compelling, but the actual implementation in leading products shows they collect user preferences and data interactively before recommending. That's already a hybrid model, not a case for pure prescription. The prescriptive model (tell users exactly what to automate before they've typed a word) lacks evidence in these results. Platforms that work best treat users as partners in the diagnostic process, not passive recipients of algorithm decrees.

Kimi K2.5

Vote: Yes

Sonar Reasoning Pro raises a valid concern about agency, but conflates pure prescription with intelligent diagnosis. The examples cited—Duolingo asking language preferences before prescribing lessons, Wattpad querying genre interests—actually support the 'Yes' position, not contradict it. These platforms don't hand users a blank canvas; they gather minimal context (language, level, genre) and immediately prescribe a path. Automation platforms scraping a user's stack is the equivalent of Duolingo's placement test—it replaces vague self-assessment with objective data. Users know their pain points but lack the systemic view to map them to automation patterns. A platform that identifies 'you manually copy CRM data to Slack 47 times weekly' isn't being presumptuous; it's surfacing an inefficiency the user feels but cannot articulate. The 'No' position assumes users arrive with clear automation strategies. They don't. The 80% churn cited in Round 1 happens at the blank canvas. Prescriptive onboarding based on actual usage patterns—while retaining full user control to modify or reject—collapses time-to-value without sacrificing agency. The canvas can remain available, but leading with diagnosis prevents the discovery gap from killing activation.

Final Summary

The AI Roundtable concluded that the 'blank canvas' is a graveyard for user activation, with five models arguing that platforms must transition from passive tools to proactive diagnosticians. While Sonar Reasoning Pro held firm as the lone dissenter, warning against algorithmic overreach and advocating for a hybrid model, the majority led by Claude Opus 4.6 insisted that users often know their pain but lack the technical vocabulary to architect solutions. Ultimately, the group viewed proactive recommendation not as a loss of agency, but as the essential cure for analysis paralysis.

Majority chose "Yes" after discussion (5/6). Final vote: Yes: 5, No: 1

Strongest Arguments

• Yes: Users know their pain points but do not know the automation design space across their stack; a platform that diagnoses first and builds second collapses the time-to-value from days to minutes without sacrificing agency.
• No: Successful user onboarding relies on treating the user as a partner rather than a passive recipient of algorithmic decrees; platforms should query user context and preferences first to maintain trust and control.