The Fed trimmed rates to 3.75%-4%. Google Cloud launched another AI-focused startup academy. Nvidia's market cap keeps running. Those headlines tell you money is moving. They don't tell me whether a startup can handle the credits we control inside AWS Activate or Google for Startups.
My answer lives in three quieter signals, refined through accelerator rebuilds, venture platform reviews, and hundreds of fintech diligence calls. When I took over the Heritage Group Accelerator, applications jumped from 58 to 186 only after we rebuilt intake around measurable workflows. A 777-practice healthcare study only worked because we proved the data pipeline before we talked pricing. The common thread is a bias for disciplined systems. That's what I look for in every startup.
Workloads that stay upright. Cloud capacity is abundant, but resilience is rare. The Google Cloud Architecture Framework and AWS Well-Architected Framework are public, yet many decks still stop at "we run on GPUs." I need proof the team understands how their workload behaves when demand spikes, a region goes dark, or a compliance rule changes mid-quarter. We redraw the system from scratch in the meeting. I ask what happens if a cache fails, if a shard lags, if an external API throttles them. The founders should explain which components can break without killing the product and how quickly they can reroute traffic. Plain English, no jargon. I want to see cost-and-latency inflection points, runbook receipts with screenshots and audit logs from drills in the last 60 days, and evidence they already budget for compliance changes (HIPAA, EU AI Act, state privacy laws) without promising to "figure it out later." Teams that keep the whiteboard coherent get fast-tracked. Teams that can't don't touch the credit pool.
GTM discipline that survives scrutiny. VC platforms want founders to ship, not burn through credits without pipeline. I expect a 30-60-90 calendar showing weekly actions for the next quarter. During accelerator reviews I capped expert time at one hour (five companies, ten minutes each) and still hit 89% evaluator completion because everyone read the same board. Founders have to demonstrate that level of prep. I also want partner choreography: which investors are warming intros, which hyperscaler account managers are on deck, which credits or co-op funds are committed. And I need evidence of pull, not excitement. Two weeks of reality: product walkthrough clips, CRM exports, a short memo on why a prospect bought or walked. Named lighthouse customers with contact paths, not logos scraped from LinkedIn. Usage goals tied to credits ("process 40TB of telemetry in BigQuery by Dec 31") rather than "experiment with AI."
Trust rails you can audit. IBM pegs the average breach at $4.7M and 200+ days to contain. The NIST Cybersecurity Framework is table stakes. Yet I still see GenAI startups passing sensitive datasets through shared drives. One leak nukes the deal, the relationship, and the LP trust we owe our platforms. In the meeting, I ask them to pull up audit logs live. Who accessed what this week? Which permissions expired? If the answer is "we'll email it later," the meeting ends. I need to know who approves prompts that touch regulated data, how training sets are scrubbed, and who can halt a release. I ask for the most recent tabletop exercise. How long to detect the simulated breach? What changed afterward? If they've never rehearsed, I assume the first real incident will be chaos. Teams that clear this bar inherit the same intake templates, hardened file paths, and automation policies we refined over years of venture and accelerator work.
Three questions I'm still carrying: Which GenAI workloads deserve multi-region redundancy before $1M ARR? How many VC platform teams weigh security reviews as heavily as GTM scorecards? And where are hyperscalers seeing the steepest credit burn without retention?