77% revenue jump via AI. Here’s how

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Hey, it's Clark.

In Moneyball, Billy Beane turned baseball upside down by valuing on-base percentage over star power. Now sales teams are doing something similar with AI. A new Gong study shows reps using AI are generating 77% more revenue per head. Not through charisma or brute force—but by knowing which calls to make, which leads to drop, and which buyers are likely to close. AI isn’t playing the game for them. It’s changing the rules of what counts as a good player.

The implications are real. Sales, historically fueled by instinct, is shifting toward systems thinking. When algorithms can decode buyer behavior faster than intuition, the edge moves from talent to tooling. It doesn’t replace human connection—it just makes the time between connections count for a lot more.

Let’s dive in…

IN TODAY’S EDITION:

Top Story: Microsoft’s GigaTIME model turns $10 tissue slides into immune system maps, reshaping cancer diagnostics and research economics

Industry Watch: Techman Robot’s AI inspection system slashes factory downtime by 40-50%

Notable Transactions: IBM bets $11B on Confluent to fuel AI-ready data streaming across enterprises

New Tools to Try: Skene speeds up post-meeting workflows by turning transcripts into clean documents

Power Prompt: Power Prompt decodes public companies like an institutional analyst—no MBA required.

THIS WEEK’S TOP STORY

BUSINESS IMPACT:

• Drug Development & Pharma: Population-scale tumor analysis that once required years and millions in lab costs now runs on archived tissue slides—compressing immunotherapy research timelines and lowering clinical trial screening expenses.

• Specialized Diagnostics Labs: Premium-priced tumor profiling services face margin pressure as AI replication turns thousand-dollar tests into software problems.

• Hospital Systems & Cancer Centers: Small and mid-size facilities gain research capabilities previously exclusive to elite institutions with specialized equipment—leveling competitive dynamics in precision oncology.

INDUSTRY WATCH

Manufacturing | Techman Robot's AI Inspection System Cuts Production Time by 40-50%

Techman Robot unveiled a High-Speed AI Flying Trigger Inspection system that performs real-time defect detection on moving workpieces, eliminating production pauses and reducing inspection time by 40-50%. Its Auto AI Training feature cuts AI setup time by 90%, enabling frontline operators to train models without specialized expertise. (Metrology News)

Real Estate | REA Group Debuts “realAssist” to Simplify Property Valuations for Millions of Homeowners

REA Group introduced realAssist, an OpenAI-powered conversational tool that helps homeowners interpret valuation data and receive personalized guidance. The feature builds on the company’s existing realEstimate product, already used by one-third of Australian homeowners to track property values. (Real Estate Business)

Agriculture | Agroz Group Expands AI-Driven Vertical Farming to Boost Local Food Production

Agroz Inc. launched Agroz Robotics with UBTECH Robotics, integrating the Walker S humanoid robot into its Agroz OS platform. The robots automate seeding, monitoring, harvesting, and optimization tasks in controlled-environment agriculture facilities. The company highlighted its role in strengthening food security through hyper-local cultivation. (iGrow News)

Tech  | Hyundai Unveils MobED, a Fully Autonomous Mobility Platform

Hyundai introduced MobED, an AI-powered, four-wheel autonomous platform built for logistics, research, and industrial applications. The system uses LiDAR-camera fusion and dynamic posture control to maintain stability on uneven terrain. (AI Business)

Healthcare | Medtronic’s Hugo Robot Secures FDA Clearance for Urologic Surgeries

Medtronic received U.S. FDA approval for its Hugo robotic-assisted surgery system in urologic procedures, unlocking access to a market of roughly 230,000 annual surgeries. The company plans further expansion into gynecologic and general surgery, potentially reshaping the soft-tissue surgical robotics market  (eWeek)

Banking & Finance | TD Bank's 75 AI Use Cases Generate $122 Million in Annual Value

TD Bank Group implemented 75 AI use cases in 2025, generating $122 million in value across loan underwriting, lead generation, and fraud prevention. The initiatives delivered a 26% year-over-year decline in fraud losses. (PYMNTS)

Retail | Albertsons Rolls Out Agentic AI Shopping Assistant to Transform In-Store and Online Experiences

Albertsons launched an agentic AI shopping assistant across Albertsons, Safeway, and Vons websites. The assistant generates meal plans, reorders purchases, finds recipes based on home inventory, and imports recipes from images. (Grocery Dive)

Media | Google Opens Ads Data Manager API Access to Agencies

Google developed APIs for its ads and demand-side platforms, granting agencies and advertisers access to its Data Manager for enhanced AI targeting control. The tools enable first-party data connections and sophisticated audience targeting, addressing marketer demands for transparency as AI handles more campaign decisions. (Ad Age)

NOTABLE TRANSACTIONS

IBM Acquires Confluent for $11B to Power AI Data Streams - Big Blue is paying $31 per share for the real-time data platform that feeds AI models, marking its largest deal in years as enterprise AI demand explodes. (TechCrunch)

Marvell Acquires Celestial AI for $3.25B - The chip maker is buying the optical interconnect startup to replace copper cables with light in next-gen data centers, slashing latency and power consumption for AI workloads. (All About AI)

Nvidia Invests $2B in Synopsys for Strategic Partnership - The GPU leader purchased shares at $414.79 each to embed its computing power into chip-design software, collapsing weeks-long simulations into hours and deepening control over the semiconductor stack. (TechCrunch)

Brevo Raises $583M, Becomes New Unicorn - The Paris-based CRM platform is targeting U.S. expansion to challenge Salesforce and HubSpot, aiming to transform 15% of current revenue into 50% as it chases America's outsized market share. (TechCrunch)

Unconventional AI Confirms $475M Seed Round at $4.5B Valuation - Former Databricks AI chief Naveen Rao raised a mega-round led by a16z and Lightspeed to build energy-efficient AI computers "as efficient as biology," with plans to raise up to $1B total. (TechCrunch)

NEW TOOLS TO TRY

POWER PROMPT

This Week: Investor Value Decoding System

Transform any ticker into institutional-grade analysis—understand how the business makes money, where its edge lies, what could break it, and whether the price makes sense today.

Analyze [Ticker] using the 13-point framework below.
- Use only verifiable, factual information (annual reports, investor presentations, filings, earnings transcripts, and reputable financial sources).
- Be concise, analytical, and concrete, with no filler or marketing language.


Output format (exact structure required):


Executive Summary (about 150–200 words)
Summarize in plain English how this company makes money, its economic quality, and where its edge and risks lie.
End with one sentence that describes the business to an investor in one line.


1. What They Sell and Who Buys
* Describe the main products or services.
* Define target customers by type, segment, and geography, and why they buy, including the main pain point or motivation.

2. How They Make Money
* Explain the revenue model and pricing logic.
* Clarify whether revenues are one-time, recurring, transaction-based, or hybrid.
* Include key revenue segments and their share if available.

3. Revenue Quality
* Assess how predictable and diversified revenues are.
* Break down recurring versus one-off components, customer or segment concentration, and exposure to economic cycles.

4. Cost Structure
* Outline major cost drivers, such as COGS, labor, logistics, marketing.
* Include gross and operating margins where possible.
* Comment on scalability, fixed versus variable costs, and how margins move with growth.

5. Capital Intensity
* Describe the assets needed to run and grow operations.
* Include capital expenditure levels, working capital needs, and cash conversion efficiency.

6. Growth Drivers
* Identify the main levers for revenue growth, such as volume, pricing, product mix, geographic expansion, or acquisitions.
* Clarify whether each driver is structural and long term or cyclical and short term.

7. Competitive Edge
* Explain what protects the company’s economics from competition, such as brand, cost advantage, switching costs, regulation, network effects, data, or intellectual property.
* Discuss how durable and testable this moat appears, using financial evidence such as margins, ROIC, or customer retention.

8. Industry Structure and Position
* Describe the industry value chain and where profit pools sit.
* Explain market structure, for example fragmented or consolidated, presence of pricing power, and key regulatory factors.
* Place the company within this context, including market share, relative scale, and whether it acts as a price setter, a price taker, a niche specialist, or a platform.

9. Unit Economics and Key KPIs
* Present unit economics at the relevant level, such as per customer, store, device, transaction, or cohort.
* Include metrics such as CAC, LTV, churn or retention, ARPU, utilization, occupancy, and payback periods where applicable.
* Comment on whether these unit economics and KPIs are improving, stable, or weakening over time.

10. Capital Allocation and Balance Sheet
* Summarize historical capital allocation across organic investment, acquisitions, buybacks, dividends, and debt reduction.
* Describe balance sheet strength, including leverage, debt maturity profile, liquidity, and any major off balance sheet obligations.
* Assess whether capital allocation has likely created or destroyed value.

11. Risks and Failure Modes
* Identify key risks, such as competitive, technological, regulatory, macroeconomic, customer concentration, or currency exposure.
* Describe in simple terms how the equity story could fail, and what would need to happen for this to occur.
* Highlight areas where uncertainty is especially high or information is limited.

12. Valuation and Expected Return Profile
* Compare current valuation with the company’s own history and with peers, using the metrics that best fit this business, such as P/E, EV/EBIT, EV/Sales, FCF yield.
* Provide a simple scenario framework with bear, base, and bull cases, including rough assumptions and implied upside or downside.
* State explicitly what must be true for the current price to be attractive, fair, or expensive.

13. Catalysts and Time Horizon
* List near and medium term catalysts, such as product launches, margin inflection, regulatory events, refinancing, or index changes.
* Note any slow building catalysts, such as mix shift or operating leverage that accumulates over time.
* Explain the expected time horizon for the thesis to play out and how the market is likely to recognize the value if the thesis is correct.


Tone: Analytical, neutral, precise.
Goal: Produce a concise yet rich narrative that lets an investor understand how this business works, how resilient and valuable its economics are, and whether the stock looks attractive at today’s price.

Prompt credit: BuccoCapital Bloke

THAT’S A WRAP

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Remember: The next decade will prove that the rarest skill isn't technical expertise—it's knowing which problems are worth the effort.

See you next week,
Clark Valberg

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