11/5/25: The Decade of Agents: How AI, Automation, and Data Power Are Rewiring the Revenue Engine
Today my friends, SO many good things happening and excited to dig into the GTM AI Podcast sponsored by GTM AI Academy and AI Business Network.
This is free on here and on Substack, but the Substack has the full newsletter, goodies and others like a free book give away from me!
Here is the NotebookLM with resources, research, links and info you can chat with
First Goodie for Today, I am giving away Momentum.io NEW BOOK IGNITE!
IGNITE: The GTM Leader’s Playbook for Building Insurmountable Advantage with AI
IGNITE is not another book about AI tools. It’s a manual for GTM leaders ready to build intelligence as an organizational capability, not a feature. Written and assembled by the team behind Momentum.io and informed by practitioners from Winning by Design, Demandbase, Owner.com, Seismic, and Stage 2 Capital, the book redefines what it means to scale revenue in the AI era.
Contributors include some of the most respected operators in GTM and AI. Their shared perspective is simple: leadership in the AI economy depends on architecture, not adoption. Readers learn how to evaluate “build vs. buy” decisions through the lens of defensibility, measure enablement through predictive coaching systems, and design revenue organizations that think in intelligence layers rather than departmental silos.
We wrote this for the forward thinking innovative doer or those who get crap done.
Want a copy? Go to www.momentum.io/ignite and optin to get one for you!
Stop Watching Your Best Leads Disappear
Here’s what keeps most GTM leaders up at night: your website traffic is strong. Your campaigns are converting. But somewhere between that first click and the sales conversation, momentum dies.
A prospect lands on your site. They’re interested. And then... nothing. No form submission. No contact info. Just gone.
The typical fix? More tools. A form tool. A router. An enrichment platform. A scheduler. A follow-up automation layer. Five disconnected systems that somehow never quite talk to each other.
There’s a better way.
Apollo Inbound does something different. It identifies who’s visiting your site like even anonymous visitors and enriches their details automatically. Then it routes qualified leads to the right rep instantly or lets buyers book time directly. All in one place. No tool sprawl. No broken handoffs.
The result? Your website doesn’t just drive traffic. It drives pipeline.
For GTM teams drowning in tool complexity, this changes the game. You get the visitor intelligence you need, the lead scoring that actually works, and the speed to follow up that separates the best from the rest.
Ready to turn traffic into pipeline? Get the goods here
You can go to Youtube, Apple, Spotify as well as a whole other host of locations to hear the podcast or see the video interview.
GTM AI Podcast: AI in Sales: The Evergrowth Model for Maximizing Pipeline Efficiency
Honored to have my man JB Daguené the CEO and Founder of Evergrowth join me on the podcast this week!
JB Degune: From SaaS Enthusiast to AI Visionary
JB, or Jean Baptiste as his full name goes, has an intriguing history. His career path into the world of Software as a Service (SaaS) began with Trustpilot, a Danish company known for online reviews. As the fourth salesperson on their team, he helped take the company from 50 to 5,000 customers in just two years which shows a testament to his prowess and dedication. Such experiences not only honed his sales skills but also instilled a profound understanding of customer-centric methodologies.
The lessons JB gleaned at Trustpilot shaped the foundation of Evergrowth. Committed to aiding startups and established companies alike, Evergrowth’s mission was clear: bring a customer-centric sales playbook to life using the power of AI.
The Pivot: Introducing AI Agents at Evergrowth
The pivotal moment for Evergrowth came with the arrival of ChatGPT-3.5 in November 2023. This was not just an upgrade in technological capability but a fundamental shift for their business model. As JB described, their initial encounter with AI resulted in an “aha moment.” The AI could complete a week’s worth of research in just 20 minutes. Recognizing the disruption and opportunity this presented, JB and his team decided to lean into this technological advancement. They secured additional funding and began developing a platform that released its first full version in September 2024.
Reimagining Sales with AI Agents
Contrary to the prevailing notion of AI SDRs (Sales Development Representatives), Evergrowth positions itself on the opposite end of the spectrum. JB sees their platform as not one that simply automates existing processes, but one that redefines them. As JB explained, “Our goal really here is to not use AI to automate more of what we do. It’s quite the opposite.”
By utilizing AI agents, Evergrowth aids sales teams in targeting and qualifying potential clients with laser precision. According to JB, the essence of their work is “multi AI agents, workflows,” which are collaborative and designed to enhance productivity while maintaining a human touch in strategy.
Real-world Implementation: The Journey from Complexity to Clarity
In our conversation, JB recounted transformative outcomes facilitated by Evergrowth’s solutions. One extreme yet telling example included a client who replaced 25 research employees with Evergrowth’s AI agents. This move not only streamlined operations but drastically reduced costs, from 150 euros per research unit to an average of six euros.
Another remarkable case highlighted how the integration of AI agents led to a significant increase in output for an outbound team, achieving a threefold increase in output within just three months of implementation. This success underlines JB’s belief in AI’s capacity to bolster human capabilities while reducing overheads and increasing returns.
The Future of Buyer-AI Interactions
Perhaps the most exciting topic JB got into was the future of AI agents in the buying process itself. He proposed a future scenario where both sellers and buyers utilize AI agents, allowing for a radically enhanced procurement experience. JB envisions a world where buyer agents autonomously navigate the internet, attend meetings, and self-qualify opportunities, ultimately redefining how business transactions occur.
Conclusion: A Collaborative Future
The rise of AI agents stands to revolutionize how companies perceive and execute sales strategies. Evergrowth is at the forefront of this change, and JB’s insights provide a glimpse into a future where sales and marketing are no longer constrained by traditional tools and methods. As JB stated, “Imagine you had all the time in the world.” With AI agents, this imagined reality is becoming more attainable, bringing with it a paradigm shift in how businesses engage with their clients and streamline operations.
As we conclude, I am filled with anticipation for what lies ahead in the realm of AI-enhanced GTM strategies. JB’s work at Evergrowth exemplifies the integration of cutting-edge technology with human strategy, a perfect embodiment of innovation in action
The Decade of Agents: How AI, Automation, and Data Power Are Rewiring the Revenue Engine
Executive Overview
AI has entered its operational decade. What began as hype around agents and copilots is turning into a structural transformation of both digital infrastructure and economic behavior. The landscape is shifting from episodic AI adoption to persistent AI integration—where agents, data centers, and automation frameworks reshape how growth, productivity, and governance operate.
This report draws on the most significant developments across technical, economic, and organizational fronts—from Andrej Karpathy’s agent thesis and McKinsey’s AI-centric software model to the Wharton AI ROI survey and the Bank of America macroeconomic analysis.
Five structural shifts now define the next era for GTM and revenue leaders:
The long game of agents – automation will stretch over a decade, not a year.
AI as capital formation – capex, not headcount, drives productivity growth.
Distribution realignment – search becomes conversation, and traffic becomes trust.
From vibe coding to viable scaling – execution replaces experimentation.
Governance, regulation, and consolidation – trust becomes the new growth constraint.
1. The Long Game of Agents
When Karpathy describes this as the decade of agents, not the year, he is outlining a slow-motion industrial revolution. The idea is not that agents replace humans soon, but that they mature in layers: first as autocomplete assistants, then as semi-autonomous colleagues, and eventually as self-improving digital entities that perform specialized work.
The technical bottlenecks
Karpathy’s framing identifies what is still missing:
Memory and continuity: agents forget between sessions.
Context depth: they lack reasoning that spans long-horizon goals.
Evaluation: reinforcement learning is still crude—“sucking supervision through a straw”.
Tool use: agents cannot yet operate computers as humans do.
He estimates a decade to make these systems durable, scalable, and trustworthy. It is the same learning curve that took deep learning from lab demos to industrial-scale deployment between 2012 and 2022.
The practical application for GTM
Revenue teams should interpret this as a timeline for progressive automation, not disruption panic.
Short term (1–2 years): AI copilots and workflow triggers improve throughput but require human verification.
Medium term (3–5 years): bounded agents handle internal support, research, CRM hygiene, and documentation.
Long term (6–10 years): specialized “micro-firms” of agents perform measurable work under SLAs—content production, analytics, even sales follow-ups.
Coding as the proving ground
Coding is where autonomy first scales. It is structured, testable, and self-evaluating. Karpathy’s own NanoChat project demonstrates how LLMs assist but cannot yet substitute for architects. They speed boilerplate but fail when precision or style matters. This mirrors GTM automation today: it works for routine workflows, not strategic orchestration.
Key insight: the productivity curve of agents follows the same path as compilers and spreadsheets did—slow at first, then ubiquitous once infrastructure, trust, and economic incentives align.
2. AI as Capital Formation
AI investment has become macroeconomic infrastructure. The Bank of America Institute’s analysis shows that the AI economy is no longer a tech subsector—it is a new class of capital expenditure.
From opex to capex
In 2025, AI-related investment contributed up to 1.3 percentage points of quarterly U.S. GDP growth. That acceleration is driven by spending on software, computers, and data centers rather than labor expansion. The shift mirrors the industrial buildouts of the 1950s—factories then, data clusters now.
Data centers now consume energy on the scale of small cities. The Amazon–Anthropic complex in New Carlisle, Indiana, already draws over 500 megawatts, projected to exceed the power use of two Atlantas when complete.
SMB adoption is rising. Small business payments to tech services grew 6.9 percent YoY, led by manufacturing and construction firms that are now automating workflows previously untouched by software.
Software developers are projected to grow 17.9 percent through 2033, one of the few high-growth occupations as AI amplifies rather than replaces expertise.
Energy and bottlenecks
This buildout has a cost. Energy services inflation now outpaces headline CPI, and the infrastructure footprint—cooling, chips, and grid demand—risks becoming the next economic constraint. The U.S. is, as The Atlantic put it, “becoming an Nvidia-state.”
For GTM leaders
The implication is clear: AI is no longer a SaaS budget line—it is an asset class. CFOs now treat AI enablement like plant equipment, demanding measurable ROI and depreciation schedules. For vendors, this means moving beyond feature value to economic value. Your product must prove it saves capital or increases asset yield.
Key insight: productivity will decouple from employment growth; winners will be firms that treat AI as a capital multiplier, not a cost reducer.
3. Distribution Realignment: From Search to Systems of Record
Traffic is no longer a reliable growth metric. AI-driven discovery has rewired how customers find and trust information. Webflow’s recent data shows that 10 percent of all new signups now originate from AI referrals—four times higher year-over-year.
The new distribution hierarchy
ChatGPT dominates AI referral traffic with 91 percent share, converting at 24 percent, six times better than Google search.
Perplexity’s algorithm reinforces authority and recency. It uses a layered reranking system that filters out low-quality sources, boosts trusted domains like GitHub, Reddit, and LinkedIn, and decays visibility exponentially over time.
Reddit’s lawsuit against Perplexity underscores how valuable conversational data has become. Content once treated as community chatter is now high-grade training fuel.
AEO replaces SEO
The new competitive advantage is Answer Engine Optimization (AEO). Ranking depends not on backlinks but on clarity, factual density, and entity precision. The algorithm rewards content that can be directly cited or summarized by an LLM. Webflow’s own experiments show that FAQ structures, schema markup, and continual content refreshes are the difference between visibility and invisibility.
Platform realignment
OpenAI’s appointment of Fidji Simo (formerly of Meta) signals a deliberate pivot toward consumer platforms. Her mandate: build user growth, retention, and engagement systems—what some insiders call OpenAI’s “Facebook era.” The playbook includes network effects, developer ecosystems, and integrated products that make ChatGPT less a tool and more a daily environment.
Music rights and cultural normalization
Creative industries are following suit. ASCAP, BMI, and SOCAN now accept partially AI-generated works, legitimizing human–AI collaboration while rejecting fully synthetic compositions. Copyright frameworks are adapting to hybrid authorship—an early sign that AI participation is normalizing across regulated markets.
Key insight: distribution now favors accuracy, reputation, and recency. In AI discovery ecosystems, trust is the new traffic.
4. From Vibe Coding to Viable Scaling
The era of vibe coding—building prototypes with LLMs and assuming they scale—is ending. Founders are learning that AI-generated code carries hidden liabilities: technical debt, integration failure, and reliability gaps. The rebuild cost, according to Alex Turnbull, can reach $300,000 for early-stage startups that relied on “demo-quality” AI code.
The execution gap
This mirrors a larger maturity shift. McKinsey’s 2025 AI-centric software report argues that software firms must transition from “AI-enhanced” to AI-centric architectures. It identifies seven structural shifts: product reinvention, new business models, go-to-market redesign, full-lifecycle automation, AI-ready infrastructure, governance, and reskilled talent.
The financial impact is tangible:
AI-centric organizations report 20–40 percent lower operating costs and 12–14 percentage-point EBITDA improvement.
63 percent of software leaders expect AI to fundamentally change their business model within three to five years, primarily moving from per-seat to consumption-based pricing.
The operational reset
The move to service-as-software models—where vendors bundle platforms, agents, and domain expertise into outcome-based offerings—is redefining SaaS margins. Sales, marketing, and support functions are being restructured around forward-deployed engineers who work directly with customer data to drive adoption and usage.
Marketing automation in context
Google’s Pomelli AI tool marks the next leap. It extracts a brand’s tone, fonts, and imagery directly from its website to generate entire campaign packages automatically. The implication: marketers are no longer briefers but editors of AI output. Creativity shifts from blank-page invention to curation and refinement.
Key insight: AI maturity is operational, not ideological. The winners are not those who adopt AI fastest, but those who standardize, measure, and scale it without chaos.
5. Governance, Consolidation, and the Human Factor
AI’s next phase will be constrained not by compute but by trust—organizational, legal, and societal. Governance failures already dominate headlines, from data scraping lawsuits to internal board controversies.
The governance fault line
Deposition excerpts from Musk v. Altman show how fragile AI governance still is. Ilya Sutskever, OpenAI’s co-founder, testified that the 2023 board decision to remove Sam Altman was “rushed” and lacked due diligence. He confirmed that Anthropic proposed a merger where it would assume leadership—an offer he opposed. The testimony highlights how concentration of decision power, unclear fiduciary structures, and mixed incentives could destabilize critical AI institutions.
Legal acceleration
Reddit’s suit against Perplexity, ASCAP’s policy alignment, and the Copyright Office’s guidance all illustrate a coming wave of AI legal codification. What was once experimentation now requires compliance frameworks. Data provenance, human contribution, and fair use are moving from gray zones to contractual obligations.
Organizational adaptation
The Wharton Human–AI Research study provides a useful benchmark:
82 percent of enterprise leaders now use generative AI weekly, up from 37 percent in 2023.
72 percent track ROI metrics linked to profitability and throughput.
43 percent warn of “skill atrophy”—employees losing baseline capabilities as AI automates routine tasks.
88 percent expect to increase AI spending in the next year, even as they restructure teams.
The leadership implication is clear: success will depend less on tools and more on governance, training, and talent resilience. Companies that integrate ethics, evaluation, and continuous learning will build durable advantage.
Key insight: as AI moves from exploration to accountability, governance becomes strategy. Boards must treat AI oversight as a fiduciary function, not an innovation theater.
Conclusion: The Shape of the Decade Ahead
The next decade of AI will look less like a sprint and more like compounding interest. The infrastructure is being built, the economic returns are beginning to materialize, and the rules of distribution and authorship are being rewritten in real time.
For GTM and revenue leaders, five imperatives stand out:
Operationalize agents deliberately. Deploy where failure costs are low, learning value is high, and evaluation data accumulates quickly.
Treat AI spend as capital. Build ROI models, not vanity dashboards. Align AI with measurable throughput and margin outcomes.
Own your discoverability. Invest in AEO, structured content, and brand authority. Visibility in AI ecosystems compounds faster than clicks.
Refactor your organization around AI fluency. Every team member should understand prompts, evaluation, and risk. Training is now a growth lever.
Build governance as infrastructure. Establish data contracts, transparency layers, and human accountability before regulators force you to.
Automation will not erase the human factor, instead it will magnify its importance. The future will reward leaders who can bridge precision and judgment, scale and stewardship.
As Karpathy said, “We’re not building animals; we’re building ghosts.” Those who understand how to work with them and not fear them, will define the next industrial era.
Source Index
1. OpenAI’s “Facebook Era”
WebProNews – OpenAI Enters ‘Facebook Era’ with Meta Vet Fidji Simo at Helm
https://www.webpronews.com/openai-enters-facebook-era-with-meta-vet-fidji-simo-at-helm/
2. Reddit vs. Perplexity (Data Scraping Lawsuit)
Reuters – Reddit sues Perplexity for scraping data to train AI system
https://www.reuters.com/world/reddit-sues-perplexity-scraping-data-train-ai-system-2025-10-22/
3. Music Industry & AI Authorship
Music Business Worldwide – ASCAP, BMI and SOCAN will now accept registrations of partially AI-generated musical works
https://www.musicbusinessworldwide.com/ascap-bmi-and-socan-will-now-accept-registrations-of-partially-ai-generated-musical-works/
4. Grokipedia Launch
PBS NewsHour – Elon Musk launched Grokipedia. Here’s how it compares to Wikipedia
https://www.pbs.org/newshour/nation/elon-musk-launched-grokipedia-heres-how-it-compares-to-wikipedia
5. Google Pomelli Launch
Unite.AI – Google Debuts Pomelli AI Tool for Automated Campaign Creation
https://www.unite.ai/google-debuts-pomelli-ai-tool-for-automated-campaign-creation/
6. “Alpha Arena” Experiment
AI model trading benchmark summary (as outlined in user brief).
7. The “Vibe Coding” Reality Check
LinkedIn (Alex Turnbull) – Vibe coding is a joke. Lovable just cost another founder $300K in rebuild costs.
https://www.linkedin.com/posts/alex-turnbull-1ab9992_vibe-coding-is-a-joke-lovable-just-cost-activity-7388560098853548033-O3Sy/
8. Enduring Human Skills in the Age of AI
CNBC – Airbnb CEO Brian Chesky: The skills students should develop instead of worrying about AI
https://www.cnbc.com/2025/10/29/airbnb-ceo-brian-cheskys-advice-for-college-students-in-the-age-of-ai.html
9. The AI-Centric Software Imperative
McKinsey & Company – The AI-centric imperative: Navigating the next software frontier
https://www.mckinsey.com/industries/technology-media-and-telecommunications/our-insights/the-ai-centric-imperative-navigating-the-next-software-frontier
10. Enterprise Adoption & ROI Study
Wharton School / GBK Collective – 82% of Enterprise Leaders Now Use Generative AI Weekly, Multi-Year Study Finds
https://www.businesswire.com/news/home/20251028556241/en/82-of-Enterprise-Leaders-Now-Use-Generative-AI-Weekly-Multi-Year-Wharton-Study-Finds-as-Investment-and-ROI-Continue-to-Build
11. Perplexity Search Ranking Framework
Metehan.AI – Perplexity AI SEO: 59 Ranking Patterns and Entity Reranking Analysis
https://metehan.ai/blog/perplexity-ai-seo-59-ranking-patterns/
12. Answer Engine Optimization (AEO) in Practice
Growth Unhinged – Traffic Is No Longer a Reliable Growth Metric
13. AI and Economic Transformation
Bank of America Institute – Economic Shifts in the Age of AI (PDF analysis)
File: ai-impact-on-economy.pdf
14. Governance and Board Dynamics (Musk v. Altman)
United States District Court, Northern District of California – Sutskever Deposition Excerpts, Case No. 4:23-cv-06888
File: gov.uscourts.cand.433688.340.1.pdf