👋 What’s up friends!
This is Prompt Punk — the best AI sales newsletter 😉
TL;DR - inside today’s newsletter
Playbook - Where to use AI in your sales funnel
AI Winners and Losers - AI support bot fail
Tools - Meeting to revenue intelligence, Sales agent
Punk POV - OpenAI asks for government backing
Tech and Deals - AWS, Apple, Synthesia
Let’s rip…
🤝 The Playbook: Where To Use AI In Your Sales Funnel (Right Now)
I often get asked by sales reps - “where can I use AI in my sales funnel”. So here it is…
1) Prospecting — feed the pipe without spamming
What to do
ICP + persona snapshots: Ask AI for firmographic look-alikes, org charts, and likely KPIs by role.
Personalized-at-scale outbound: Draft 1:1 intros that reference recent news, tech stack, or metrics.
List enrichment & routing: Add triggers (hiring, funding, tech changes), and auto-route to the right seller.
Proof it’s working
Measure KPIs: reply rate, booked meetings per 100 sends
Guardrails
Don’t auto-send everything the bot writes. Review, tighten, and keep it under 150 words.
2) Discovery — turn meetings into revenue intelligence
What to do
Live call coaching: Real-time prompts for deeper pain, budget, timeline, and next steps.
Auto-notes + field sync: Summaries, action items, competitor mentions → push straight into CRM fields you can forecast on.
Question libraries: Generate talk tracks per industry; keep 6 killer questions that reveal money, risk, and authority.
Proof it’s working
Measure KPIs: Stage conversion, next-step creation within 24 hours.
Guardrails
If recordings exist, listen to the hard two minutes (objections, pricing). Don’t outsource judgment.
3) Demo / Validation — show their product, not yours
What to do
Vibe-code the demo: Use AI to spin a light prototype, tailored dataset, or slide mock showing their workflow and KPIs.
Role-based decks: Create two versions, operator (how it works) and exec (so what, by when, how much).
Proof it’s working
KPIs: demo→pilot conversion, sales!
Guardrails
Cut feature talk. Discuss customer outcomes.
4) Closing — remove friction, not pressure
What to do
Redline assistant: Compare terms to your model contract; flag commercial landmines (auto-renewal, data locality).
Mutual close plan generator: Backward-plan from go-live; assign tasks to legal, security, finance.
Win/loss memos: Summarize buying rationale and blockers while it’s fresh; feed that back into discovery prompts.
Proof it’s working
KPIs: cycle time from verbal → signature, legal turns, discount given vs. plan.
Guardrails
AI helps you spot issues; your finance/legal team decides.
Cross-funnel boosts that always pay
Writing copilot: Cold emails, follow-ups, summaries, and “nudge” notes that sound like you (not a robot).
CRM hygiene: Auto-fill next steps, contacts, stage reasons; kill “notes in the head” risk.
Enablement at velocity: Turn one strong call into a mini-play: snippet, objection map, and email template.
Bottom line
AI belongs where it cuts time-to-proof-of-value.
Start with prospecting and discovery, the ROI lands in weeks. Use that credibility to upgrade demo quality and shave legal cycles. Sequence your wins, publish the lift, and earn the right to rewire the entire funnel later. No moonshots, just momentum that compounds.
🏆 Who’s winning (and losing) with AI?
🧯 AI Support: Genius in Demos, Panic in Production
Here’s what happened when beehiiv (newsletter platform with 50k+ active users) chased the “AI support agent” dream. They vetted a dozen vendors, picked four finalists, fine-tuned models on their internal docs and data, and were promised instant, accurate replies that would free humans to focus on gnarlier work. On paper, it sounded perfect.
Result
In practice, their CEO says systems missed easy tickets and collapsed on complex ones. During a server outage incident, with impact changing by the minute, the right answer demanded live engineering context: codebase details, dependencies, logs, runbooks, and Slack updates. The bots couldn’t see or reason about any of it.
Customers notice
Customers noticed confidently wrong replies during incidents. They noticed slower resolutions when a bot “tries first” before escalation. Most of all, they notice when support sounds sure but isn’t right.
Why it matters
Happy-path demos aren’t production. Great support is diagnosis, retrieval of live state, human coordination, and precise communication. Until agents can safely read vendors, code, logs, and runbooks, treat AI as assistive, not autonomous.
🛠️ AI Tools You Can Use
🔊 Kickscale — Turn Calls into Revenue Intel
What it does: AI revenue intelligence that auto-captures your sales meetings, understands what was said, syncs the right fields to your CRM, and surfaces deal risks, next steps, and market signals.
Example: After a discovery, Kickscale extracts pain points, competitor mentions, and commitments → updates the opp in your CRM → pings you if there’s churn risk or an expansion hook hiding in the transcript.
Why it’s valuable: Cleaner CRM, sharper coaching, faster forecasts. Their materials cite win-rate lifts of ~32% when teams adopt conversation analysis and coaching.
Website: kickscale.com
💨 HubSpot Breeze — AI Teammates for Your GTM
What it does: Breeze bundles HubSpot’s AI: Assistant/Copilot for on-the-spot help, Agents you can deploy from a marketplace and tune in Breeze Studio, plus embedded AI across the CRM for writing, summarizing, automating, and reporting.
Example: A Prospecting Agent enriches inbound leads, scores accounts, drafts first-touch emails, and opens the right deals/tasks, while Breeze shortens forms and auto-fills missing CRM data to keep conversion high and records complete.
Why it’s valuable: One place to create, automate, and ship, no tab-sprawl. Get started free with Assistant; advanced Agents land with premium tiers.
🧐 Prompt Punk Point of View
🛡️ OpenAI Asks U.S. Government to Vouch for GPUs
OpenAI’s CFO Sarah Friar just floated a big one: federal loan guarantees to underwrite the AI infrastructure buildout that could top $1T over time.
Not grants, but guarantees that cut borrowing costs and widen the lender pool if/when banks hit risk limits. The subtext: AI is now a national capability, not just clever software.
Why now? Because the buildout is insanely capital-hungry and weirdly perishable: There is the power-hungry data centers, GPUs that obsolete fast, cooling, grid upgrades, even upstream chip supply.
You’ve seen this movie: 2008 bank backstops, CHIPS Act, Department of Energy loans that helped Tesla scale.
OpenAI’s version: “Help us de-risk the capex that keeps America ahead.” Cynic’s version: “Socialize downside, privatize upside.” Both can be true.
What this really is
A cost of capital play dressed as industrial policy. Guarantees reduce loan costs.
A power + supply chain story, not just “more GPUs.”
A trial balloon to gauge Congress, agencies and public sentiment.
Why it matters:
This would make OpenAI virtually untouchable. A locked-in moat guaranteed by the U.S. government. If it wasn’t obvious, this is a “checkmate” move.
Bottom line: Whether you see AI as a space race or a subsidy spree, the center of gravity just moved from “cool models” to industrial finance.
If guarantees happen, the winners won’t just ship code, they’ll ship policy.
🤖 Fresh Tech, Hot Deals 🔥
🦾 AWS Bags OpenAI: $38B Cloud Coup
OpenAI signed a seven-year, $38B deal to run training and inference on AWS. The agreement includes immediate access to “hundreds of thousands” of NVIDIA GPUs with capacity targeted to be fully online by end-2026.
It’s the strongest signal yet that frontier labs will multi-cloud for compute. The restructuring of the OpenAI / Microsoft deal allows OpenAI to use any cloud for non-API products (ChatGPT, training new models) but Azure gets a guaranteed $250B in revenue.
Why it matters: OpenAI is showing confidence doing a deal like this. It shows the company is now a “Big Tech” player, and that they intend to keep their crown as the AI market leader. They are locking in their advantage. Who will challenge them? Chinese open source? Anthropic? Google or xAI? The field is narrowing.
🍎 Apple’s Siri Taps Google’s Big Brain
Reports say Apple is finalizing a deal to run a revamped Siri on a custom version of Google’s Gemini, paying roughly $1B a year while Apple’s own models catch up. The overhaul reportedly uses Gemini for planning and summarization, with some features still powered by Apple’s in-house models.
Why it matters
Apple has been the butt of too many AI jokes. If Siri gets reliably competent at multi-step tasks, the opportunity for commerce and workflows on iOS expands into bookings, billing, approvals, and more.
🇬🇧 Synthesia Levels Up: $200M, $4B Valuation
London’s AI-video workhorse reportedly raised $200M at a $4B valuation, led by Google Ventures. The story: enterprise communication is abandoning studio shoots in favour of templatized, on-brand AI videos for training, onboarding and product explainers. Recent coverage notes the round nearly doubles Synthesia’s January valuation and follows reported M&A interest from Adobe.
Why it matters
This is a budget line now, not an experiment. If you sell L&D or enablement you have AI videos. I also wonder if this is a strategy from Google to get more AI leaders to use their cloud.
📭 That’s a wrap
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— John
Prompt Punk