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Morning Coffee: The Davos Split-Screen: A $20B Boom vs. The ROI Bust
OpenAI CFO Sarah Friar dropped a bombshell on Sunday. The figures were staggering—but it was what they revealed about the rest of the enterprise market that sent ripples through Davos.
The numbers: Annualised revenue rocketed from $6 billion in 2024 to over $20 billion in 2025—a 233% increase. Computing capacity tripled to 1.9 gigawatts. Weekly and daily active users hit all-time highs. Revenue growth tracked almost perfectly with compute expansion.
Then came the pivot. "This is never-before-seen growth at such scale," Friar wrote. "We firmly believe that more compute in these periods would have led to faster customer adoption and monetisation."
Three signals emerged that enterprise leaders are now parsing:
The Infrastructure Arbitrage—OpenAI's revenue-to-compute correlation suggests the real constraint isn't technology, it's infrastructure access. Companies with preferential compute deals will pull ahead.
The Agent Pivot—Friar explicitly flagged that OpenAI's next phase will focus on "agents and workflow automation that run continuously, carry context over time, and take action across tools." The chatbot era is ending; the autonomous workflow era is beginning.
The Monetisation Gap—Here's where Davos got uncomfortable. PwC's 29th Global CEO Survey, released simultaneously, revealed that 56% of enterprise leaders have realised neither revenue nor cost benefits from their AI investments. Only 12% report meaningful returns on both metrics.
Mohamed Kande, PwC's global chairman, put it bluntly: "Everybody's going for it. Nobody is asking whether they should adopt AI anymore. But more than half are getting nothing out of it."
The market reaction was muted—investors are pricing in OpenAI's hypergrowth while remaining skeptical of enterprise follow-through. The real money is flowing to infrastructure providers and the handful of companies that have cracked the implementation code.
The question: If OpenAI can 10x revenue by simply scaling compute, why are 56% of enterprises—with billions in AI budgets—still stuck at zero ROI?
GROWTH HACK
The "Newsjacking" Engine
Turn viral moments into viral threads—automatically.
The Play: Orchestrate an n8n workflow that detects high-velocity tweets in your niche, processes them through Claude to generate structured contrarian threads, and posts to Typefully's API with the threadify parameter—automating the entire research-to-draft pipeline.
Why This Works:
Newsjacking works because attention compounds. A contrarian take on a trending topic gets algorithmic boost from engagement on the original, plus the novelty premium of offering a different perspective. The problem is speed—by the time you notice a trend, research your angle, and write the thread, the window has closed. Automation collapses that cycle from hours to minutes.
The Implementation Stack:
Automation: n8n (self-hosted free / cloud from $20/mo)
Scraping: Apify (pay-per-use, ~$0.25/1K results)
AI Processing: Claude 3.5 Sonnet API ($3/1M input tokens)
Publishing: Typefully API (Pro $12.50/mo)
Monitoring: Webhook.site (free for testing)
Step 1: Apify (The Radar)
Configure a Twitter scraper actor to monitor keywords in your niche. Set velocity threshold—posts gaining >100 engagements in <30 minutes.
{
"searchTerms": ["AI agents", "Claude", "GPT-5"],
"minRetweets": 50,
"minLikes": 100,
"maxAgeMins": 30,
"resultsLimit": 10
}
Step 2: n8n Trigger (The Orchestrator)
Create a webhook node that receives Apify's output. Filter for posts above your engagement threshold.
Step 3: Claude API (The Contrarian)
Pass the trending tweet to Claude with a structured prompt:
{
"system": "You are a growth marketing expert who writes viral Twitter threads. Generate a 5-tweet contrarian thread that offers a surprising counter-perspective to the trending take. Include specific data points and a memorable opening hook.",
"user": "Trending tweet: {{tweet_text}}\nEngagement: {{engagement_count}}\nGenerate a contrarian thread."
}
Step 4: Typefully API (The Publisher)
Execute HTTP POST with threadify enabled:
{
"content": "{{claude_output}}",
"threadify": true,
"schedule-date": "next-free-slot"
}
Step 5: Notification (The Review)
Send Slack/email alert with thread preview link. Open Typefully, polish for 2 minutes, publish.
The Result:
Time to first draft: 4 minutes (vs. 45+ manual)
Thread production: 3-5x increase
Engagement lift: 40%+ on newsjacked threads vs. evergreen
Manual effort: Review and polish only
From reactive to proactive—your content calendar now writes itself.
DAILY STAT
70% of Purchase Decisions Will Be Agent-Influenced by 2028
The scale: That's not 70% of research—it's 70% of actual transactions where an AI agent filters options, compares alternatives, and recommends (or executes) the purchase. The human approves; the agent decides.
The human comparison: Instead of browsing 15 tabs comparing features, you'll tell your agent "find me noise-canceling headphones under $300 that work well for podcasts" and it returns three options with a recommendation. One click. Done.
The Shift:
Welcome to Agentic Commerce—where outcomes define purchases, not impressions. When an AI agent shops on your behalf, it doesn't care about your brand's origin story, your influencer partnerships, or your beautifully shot lifestyle photography. It cares about specs, reviews sentiment, return policies, and price-to-value ratios. The agent optimizes for the outcome the human specified, not the emotional journey marketers designed.
The Economics:
Brand marketing has spent decades building awareness funnels: see the ad, remember the name, choose the familiar option at purchase. That model assumed humans made the final selection. When agents intermediate that decision, familiarity loses to function. The brands that win will be those whose products genuinely deliver on measurable outcomes—because agents will surface that signal from the noise.
What This Means for Builders:
Shift from brand awareness to outcome documentation—prove your product delivers
Publish structured, machine-readable product data (specs, benchmarks, verified reviews)
Build programmatic trust signals that agents can verify and weight
Optimize for recommendation engines, not just human discovery
Focus on post-purchase satisfaction metrics—agents learn from outcomes
The companies that treat AI agents as a primary customer segment—optimizing for their decision criteria rather than human browsing behavior—will capture disproportionate share as agentic commerce scales.
TOOL TIP
SurgeFlow — Your browser, but it actually does the work.
URL: surgeflow.ai
What it does: SurgeFlow is an AI-powered Chrome extension that automates multi-tab web workflows using natural language commands. Instead of clicking through 50 tabs, copying data, and switching contexts, you describe what you want done and SurgeFlow executes the entire workflow. It uses visual LLMs to understand UI elements rather than brittle code selectors, making it surprisingly robust on dynamic pages.
Pricing:
Free — Full features, unlimited use — Beta users / early adopters
Pro — TBA — TBA — Power users (pricing not yet announced)
Who it's for:
Researchers — Summarize findings from 20+ papers into Google Docs automatically
Marketers — Collect competitor pricing and features across 10 sites into Sheets
Job seekers — Auto-fill applications across multiple job boards simultaneously
Solopreneurs — Automate customer onboarding from dashboard to email in one command
What makes it different:
Planner-Executor-Evaluator system — See the plan before execution, stop or modify at any stage
Multi-tab orchestration — Works across all your open tabs simultaneously, not just single pages
Zero migration — Lives in your existing Chrome, keeps all your bookmarks and extensions
Visual understanding — Uses vision models instead of DOM selectors, handles dynamic UI changes
Core capabilities:
Natural language task description
Cross-tab data extraction and entry
Automated form filling and submission
Price comparison across multiple sites
Content aggregation to Sheets/Docs
Limitations:
Beta stability—edge cases with complex social media character limits
No detailed privacy documentation yet
Chrome-only (no Firefox/Safari support)
The bottom line: If your workday involves repetitive tab-hopping, data copying, and multi-site tasks, SurgeFlow is the "invisible employee" that actually delivers—and while it's free in beta, there's no excuse not to test it.
TICKER WATCH
BioCryst Pharmaceuticals (NASDAQ: BCRX) — $7.55
The Numbers That Matter:
Metric | Value |
|---|---|
Current Price | $7.55 |
52-Week Low | $6.00 |
52-Week High | $11.31 |
Market Cap | $1.6B |
Analyst Target | $9–$32 (avg $20) |
Q3 Revenue Growth | +37% YoY |
What They Do (Simple Version):
BioCryst makes a daily pill called ORLADEYO for people with hereditary angioedema (HAE)—a rare genetic condition that causes dangerous swelling episodes. They're the only company with an oral option. Everyone else requires injections. The pill is already generating $159M per quarter and growing 37% annually.
Why This Matters:
The stock is trading at $7.55 with a $20 average analyst target. That's a 165% gap between current price and Wall Street consensus.
Three things changed recently: (1) They just paid off all their debt, (2) the FDA approved their drug for kids as young as 2, and (3) they're buying Astria Therapeutics for $700M to own the entire HAE market—both daily pills AND long-acting injectables.
The Upside Case:
Conservative: Average analyst target of $20 = +165% from here
Bull case: H.C. Wainwright raised target to $32 in December = +324% from here
Profitability: Already operating profitable—Q3 operating profit up 285% YoY
Simple Math: $1,000 invested today could become $2,650 (conservative) or $4,240 (bull case) within 12–18 months.
The Math:
ORLADEYO generated $159M last quarter. At 37% growth, that's ~$600M+ annual revenue by end of 2025. The Astria acquisition adds navenibart—a long-acting injectable dosed every 3–6 months instead of daily. Phase 3 data comes in 2027. If it works, BioCryst owns the full HAE spectrum: daily oral for convenience seekers, quarterly injectable for those who hate pills. Either way, BioCryst collects.
Blackstone gave them a $550M credit facility. HSR antitrust review already cleared early. The deal closes Q1 2026.
The Risks (Be Honest):
Competition is real—Takeda's Takhzyro is the current injectable standard, Intellia has a gene editing therapy in trials
$550M in new debt to finance the Astria deal
Heavy concentration in one disease (HAE is 90%+ of revenue)
Astria deal dilutes shareholders by ~15%
Small-cap biotech = volatile on macro sentiment shifts
The Verdict:
This is a rare disease consolidation play with real revenue, real profits, and a transformative acquisition closing in weeks. The market is pricing it like a risky biotech, but the fundamentals say otherwise—$600M in revenue, operating profitability, debt-free before the deal, and now building a monopoly in HAE treatment.
If the Astria deal closes cleanly and navenibart Phase 3 delivers, there's 165–325% upside. If competition intensifies or integration stumbles, you could see a pullback to the $6 support level.
Position: Speculative. Small position only (2–3% max). The risk/reward at $7.55 vs. $20 target is compelling, but size for volatility.
Not financial advice. Do your own research.
WORKFLOW
The Autonomous SDR
Setup time: 25 minutes | Weekly value: 5-10 hours of lead qualification and email drafting automated
Turn raw email lists into qualified, personalized outreach—without touching a keyboard.
The Architecture:
Trigger: New row in Google Sheets
↓
Action 1: Apollo API — Enrich email with company/role data
↓
Action 2: GPT-4 — Calculate ICP Score (1-100)
↓
Action 3: Filter — If ICP Score > 70
↓
Action 4: Claude API — Generate personalized cold email
↓
Outcome: Draft appears in Gmail (ready for one-click send)
Step 1: Google Sheets (Trigger)
Configure n8n to watch for new rows in your prospect sheet. Minimum fields: email, source.
{
"spreadsheetId": "your-sheet-id",
"sheetName": "Prospects",
"triggerColumn": "A",
"pollInterval": 300
}
Signal: New lead entered = workflow begins.
Step 2: Apollo API (The Enricher)
Pull company data, title, employee count, funding stage, tech stack.
{
"email": "{{row.email}}",
"reveal_personal_emails": false,
"reveal_phone_number": false
}
Signal: Raw email becomes rich prospect profile.
Step 3: GPT-4 (The Qualifier)
Score against your ICP criteria:
{
"model": "gpt-4",
"messages": [{
"role": "system",
"content": "Score this prospect 1-100 based on: company size 50-500 employees (30pts), Series A-C (25pts), tech/SaaS industry (25pts), VP/Director title (20pts). Return only the number."
}, {
"role": "user",
"content": "{{apollo_enrichment_data}}"
}]
}
Step 4: Claude API (The Copywriter)
Generate personalized email for qualified leads:
{
"model": "claude-3-5-sonnet-20241022",
"messages": [{
"role": "user",
"content": "Write a 3-sentence cold email for {{name}} at {{company}}. They are a {{title}} at a {{employee_count}} person {{industry}} company. Reference their recent {{trigger_event}}. Pitch: [YOUR VALUE PROP]. Tone: conversational, no fluff, one clear CTA."
}]
}
Step 5: Gmail Draft (The Staging Area)
{
"to": "{{row.email}}",
"subject": "{{claude_subject_line}}",
"body": "{{claude_email_body}}",
"isDraft": true
}
Expansion Ideas:
Add Clearbit for company news/trigger events
Integrate with Calendly for automatic meeting link insertion
Layer in LinkedIn profile data for deeper personalization
Build A/B test variants with different Claude prompts
From manual prospecting to one-click sending—your pipeline fills itself.
THE BOTTOM LINE
The Davos split-screen isn't just about OpenAI's explosive growth versus enterprise stagnation—it's about which side of the implementation gap you're standing on.
The PwC survey's 56% dead zone reveals something crucial: the winners aren't those who adopted AI first. They're those who integrated AI into automated workflows that actually execute. OpenAI's revenue-to-compute correlation proves that capability isn't the bottleneck—deployment is. The companies pulling ahead are those building the plumbing: the n8n workflows, the API integrations, the autonomous SDRs that turn raw data into revenue without human bottlenecks.
The playbook: First, automate the research-to-action cycle. The newsjacking workflow isn't about content—it's about collapsing the time between signal and response. Second, prepare for agentic commerce. With 25% of search volume shifting to AI agents, your next customer might never see your website. Structured data and API accessibility become the new SEO. Third, look for the asymmetric bets. BCRX trading at $7.50 against a $20 analyst target, with profitable operations and a transformative acquisition closing—that's the kind of risk/reward that emerges when markets price uncertainty but not execution.
If you're building, ship the workflow before the strategy deck. If you're investing, find the companies where revenue already proves the model. If you're selling, optimize for the agent, not the human.
Ship daily.
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