Cold LinkedIn DMs to CTOs and heads of engineering are almost universally ignored, especially when targeting competitor users, and 20-minute discovery calls feel like too big an ask with unclear value.
Founders doing customer discovery for technical AI/infra products struggle to get targeted CTOs and heads of engineering to respond to cold LinkedIn DMs or engage in calls.
WHY is it so hard to get people to talk about their problems?
WHY is it so hard to get people to talk about their problems?
competitor users may be the coldest group.
commentFor this audience, I would stop asking for a general 20 minute discovery call and ask for a very specific teardown. Something like: "I am mapping where agent failures first show up in prod. Could I send you a 5 minute Loom with 3 failure modes and you tell me which one is closest to your world?" That is a much smaller yes than a call with a stranger. Also, competitor users may be the coldest group. They already solved enough of the pain to move on. The warmer group might be teams posting incidents, eval complaints, flaky workflow stories, or "we tried agents and backed off" comments. Those people still have an open wound.
Solo-to-small-team technical founders conducting customer discovery with CTOs and heads of engineering on agent reliability and observability pain points.
Multiple complaints about ignored cold DMs, low willingness for calls, and targeting competitor users being ineffective, confirmed across OP and commenters.
Hyper-focused on technical AI/infra personas with async-first format and mutual value exchange, unlike generic LinkedIn or broad survey tools.
A niche platform matching AI infra founders with pre-vetted technical users willing to do short async feedback or 15-minute calls in exchange for relevant insights or small incentives.
Founders already invest time and money in conferences and high-volume cold outreach with poor ROI; signals show strong frustration with ignored messages and desire for better access to validate problems.
“Get 5 qualified discovery responses from CTOs this week.”
A niche platform matching AI infra founders with pre-vetted technical users willing to do short async feedback or 15-minute calls in exchange for relevant insights or small incentives.
Launch in AI founder communities on X, Reddit r/MachineLearning and r/SaaS, and targeted LinkedIn groups for technical founders.
Hard to attract enough CTOs and heads of engineering willing to participate without a strong initial network.
Technical insights may require live conversation depth that async methods struggle to deliver.
Founders may try once and churn if early matches don't yield strong validation.
Run an Investment Memo to get a structured Go / No-Go verdict, competitor landscape, unit economics, and a 90-day validation roadmap for this opportunity.
Generate an investment memoThis idea scores in the upper-middle range of opportunities surfaced by MonetScope, with a validation sub-score of 8/10 against 3 independently sourced evidence signals. A "promising" rating usually indicates a real pain has been detected and discussed in the open, but the pipeline did not find enough signal to flag it as urgent or high-frequency. These opportunities can still produce excellent businesses — they often correspond to "boring" problems that established players have ignored — but the founder should expect a longer customer-development cycle to confirm willingness to pay.
It sits at the intersection of "ai-powered", "analytics", "automation", which makes it relevant to a specific subset of founders rather than a generic horizontal opportunity. SaaS opportunities at this stage tend to win on the strength of their initial wedge — a single workflow that the target user runs every week, where the existing solution is either spreadsheets, a clunky incumbent feature, or a manual process they hate. The build cost is moderate; the distribution cost is everything. The MonetScope pipeline surfaces this category alongside other saas signals, which is why it appears here rather than in a generic "trending ideas" feed.
Scores are derived from real forum discussions across Reddit, Hacker News and X, weighted by evidence volume and signal quality. How scoring works
MonetScope does not generate ideas from a language model's imagination. Every opportunity on this site is anchored to specific source posts and comments from real public discussions — typically on Reddit, Hacker News, or X — where actual users describe the pain in their own words. The AI's role is structuring, scoring, and grouping those signals into a navigable opportunity, not inventing the problem.
MonetScope's spider pipeline runs continuously and surfaces opportunities as new evidence accumulates. The "Updated" date in the header reflects the most recent re-scoring of this specific opportunity. Most saas opportunities visible in the public catalog draw from discussions in the last 30-60 days; older signals are de-prioritized because user pain shifts faster than most founders assume.
Overall score is a composite across six dimensions — pain, urgency, willingness to pay, market size, defensibility, and execution ease — designed to give a single number for triage. Validation score is narrower: it asks "how cleanly does the same signal repeat across independent sources?" An opportunity can score high on overall but lower on validation when one or two large discussions dominate the evidence; conversely, validation can be high on a smaller-overall idea where the signal is consistent but the addressable market is modest.
