Daily GTM Feed

Make Friends With Your Market

useful booth manning advice

Meet Shapes, the app bringing humans and AI into the same group chats

this is just ...cool?

• Shapes is positioning as 'Discord with AI characters' - group chat with AI participants
• Represents emerging category of AI-as-peer vs AI-as-tool
• Insufficient detail to assess product-market fit or GTM relevance

Human-in-the-Loop vs. Human-on-the-Loop: When To Use Each System

• HITL (human-in-the-loop) requires human approval before AI executes critical actions - synchronous control pattern used for high-stakes decisions, compliance requirements, and low-confidence scenarios
• HOTL (human-on-the-loop) allows AI to execute autonomously while humans review results and adjust parameters - asynchronous pattern for scalable operations with exception-based oversight
• Framework applies across use cases: loan approvals, customer emails, social posts, fraud detection, and compliance workflows - choice depends on risk tolerance, regulatory requirements, and operational scale needs

I Need Agentic Email. Claude Said Try AgentMail For a New Project. So I Did. And Never Looked At Anything Else.

• AEO (AI Engine Optimization) is fundamentally different from SEO: LLMs return 1-3 recommendations vs Google's 10 blue links, creating winner-take-all dynamics where #1 position captures nearly all traffic
• AI search converts 5x higher than Google organic (14.2% vs 2.8%) because AI pre-filters and ranks - buyers arrive ready to purchase, not compare. Claude converts highest at 16.8% due to shortest recommendation lists
• Position bias in LLM recommendations is measurable and brutal - being the first recommendation matters exponentially more than in traditional search. 73% of B2B buyers now use AI in research, with 37.5% of ChatGPT usage being 'generative intent' (creating vendor comparisons, not searching)

Otter’s new feature lets users search across their enterprise tools

• Otter expanding from meeting transcription to cross-platform enterprise search
• Windows app enables passive meeting capture without bot joining - potential privacy/adoption shift
• Positioning suggests competition with knowledge management platforms, not just Gong/Chorus

Rebuilding the data stack for AI

• Enterprise AI adoption is bottlenecked by fragmented, ungoverned data infrastructure rather than AI model capabilities
• Competitive differentiation comes from proprietary data combined with third-party enrichment, not just AI tools
• Evolution from 'system of engagement' to 'system of action' represents shift toward autonomous AI agents managing workflows

Gartner: Explainable AI Will Drive LLM Observability Investments

• LLM observability adoption will jump from 15% to 50% of GenAI deployments by 2028, driven by explainability requirements for scaling beyond low-risk use cases
• Traditional IT observability (latency, cost) is insufficient - new metrics needed include hallucination detection, factual accuracy, logical correctness, and sycophancy measurement
• Gartner recommends XAI tracing for high-impact use cases, multidimensional observability platforms, and continuous evaluation frameworks with human-in-the-loop validation

From a $6.90 newsletter to $3M API: How a non-coder built Memelord | Jason Levin

• Non-technical founder scaled from $6.90/month newsletter to $100K ARR using Bubble (no-code), then raised $3M to build API-first product - validates no-code as legitimate path to venture scale
• Mandatory 'vibe-coding' rule for marketing team - employees must build their own AI tools/automations, representing shift from using AI to building with AI as core marketing skill
• Free AI tools as lead gen replacing traditional content - 'free tools are the new PDF downloads' generated hundreds of thousands of emails, signaling evolution in PLG motion

An Interview with Google Cloud CEO Thomas Kurian About the Agentic Moment

• Google Cloud emphasizing unified architecture with real production use cases, not just pilots
• Google allocating 50% of capex to Google Cloud, running same stack internally as external customers
• Shift from theoretical AI applications to agents running at scale, with security as key differentiator

Why RevOps needs to stop counting hours and start architecting outcomes

• AI investment scrutiny has shifted from 'what can it do' to 'prove the ROI' - CFOs now demand direct correlation between AI spend and measurable productivity/revenue impact
• The MIT '95% of AI pilots fail' statistic is driving C-suite skepticism and causing companies to question entire AI strategies, though the stat may be misleading
• Companies experienced rapid AI tool sprawl (2022-2026) - buying point solutions for every use case (call summaries, SDR avatars, email writing, enrichment) without strategic integration

54% of the Fastest Growing B2B SaaS Companies have a GTM Engineer

• GTM Engineer role has reached critical mass with 54% adoption among fastest-growing B2B SaaS companies
• Signal infrastructure and GTM engineering becoming competitive differentiator for high-growth companies
• Newsletter targets GTM founders/operators (7,783 readers), indicating audience interest in operational excellence

5 Interesting Learnings from ServiceNow at $14.7B in ARR: 22% Growth, Rule of 54, and the Paradox of Beat-and-Lose

• ServiceNow achieved Rule of 54 (22% growth + 32% margin) at $14.7B ARR—historically rare performance at this scale—yet stock dropped 13-15% after earnings, revealing disconnect between operational excellence and market sentiment
• Company accelerated growth from 20.5% to 22.5% and raised full year guidance by $205M, adding roughly $3B in net new ARR (equivalent to Datadog's entire ARR) in a single year
• The 'beat-and-lose' paradox represents emerging market narrative: even exceptional B2B SaaS performance at scale is being punished, suggesting fundamental shift in how public markets value enterprise software growth

How to show up in AI answers on LLMs

• GEO is fundamentally different from SEO: it's a citation game not a ranking game - AI models reference trusted sources rather than users clicking ranked results
• AI Overviews correlate with 34.5% CTR drop for #1 organic results based on 300k keyword analysis - the traditional traffic model is breaking
• Earned media in trusted third-party sources now outweighs owned content authority - biggest strategic shift from traditional SEO where own-site optimization could win

S2E2: "Context Is the Moat" | Zach Vidibor

• AI tools (Claude, GPT, Gemini) are now commoditized - competitive advantage comes from proprietary context layer (ICP definitions, value props, competitive positioning, institutional knowledge)
• Context engineering concept: treating GTM knowledge like code in a version-controlled repository that feeds all AI agents and human workflows consistently
• Strategy compression problem: institutional knowledge and nuanced positioning gets lost between leadership and frontline execution - structured context infrastructure solves this leakage

How GTM teams are using Claude Code (and Cowork)

• Survey of 200 GTM operators regarding Claude Code and Cowork usage exists
• Content appears to be from 'Hello Operator' newsletter/publication
• Article truncated - full analysis impossible without complete content

How Intercom 2x’d their engineering velocity in 9 months with Claude Code | Brian Scanlan

• Intercom doubled engineering throughput (merged PRs per R&D employee) in 9 months using Claude Code while maintaining code quality
• Built custom telemetry infrastructure to measure AI adoption and quality impact across hundreds of engineers, plus skills repository with automated enforcement hooks
• Achieved 100% adoption across engineering AND expanded to non-technical roles (designers, PMs, TPMs) shipping code—suggesting AI coding tools democratize development

I built a 3D brain that watches AI agents think in real-time (free & gives your agents memory, shared memory audit trail and decision analysis)

the repo for this is kinda cool - https://github.com/RyjoxTechnologies/Octopoda-OS

• Agent memory persistence is the #1 pain point (38%) for multi-agent systems, followed by debugging complexity (24%)
• Loop detection prevents runaway costs - one case saved $200 in a single afternoon from stuck GPT-4 calls
• Visual observability (3D graph showing agent activity, memory operations, and inter-agent communication) addresses debugging complexity that affects 24% of users

5 Interesting Learnings from Klaviyo at $1.2 Billion in ARR: 32% Growth, 110% NRR, and Somehow Only 4x Revenue

SaaS dead, dying or underpriced? Feels like a stock pickers market with attractive opportunities to me

• Klaviyo trading at 4-5x revenue despite 32% growth, 110% NRR, and profitability—potentially most mispriced public B2B company or signal of 'New Normal' for SaaS valuations
• NRR improved to 110% while scaling to $1.2B ARR by doubling $1M+ ARR customers and growing $50K+ customers 37% YoY—rare upmarket expansion success at scale
• International revenue grew 42% YoY and now represents 33%+ of business, breaking 'Shopify add-on' narrative with regional hubs in Dublin and Singapore

The slow decay of growth (and how to avoid it)

• Growth decay is a common pattern affecting successful PLG companies including Ramp, Notion, Airtable, Figma, Miro, and Canva
• There are documented examples of companies that successfully reversed growth deceleration
• Newsletter promises new data and real-world frameworks for addressing growth plateau

Mythos, Muse, and the Opportunity Cost of Compute

brilliant read

• Reasoning models (o1) fundamentally break Aggregation Theory by reintroducing marginal costs - compute scales with usage, unlike internet-era products
• Hyperscalers' business models were built on zero marginal cost assumption; AI inference costs challenge this foundation requiring new economic models
• The 2010s internet era may be viewed as anomalous 'naive time' - technology returning to capital-intensive, high-marginal-cost paradigm of pre-internet era

AI Weekly Issue #481: Musk wants Altman fired, Anthropic passes OpenAI, Meta goes closed

• Anthropic overtook OpenAI in revenue ($30B vs $24B run rate) driven by enterprise customers, doubling million-dollar accounts in under two months
• Meta abandoned open-source AI strategy with first proprietary model under Superintelligence Labs, reversing Llama approach
• AI legal/regulatory activity intensifying: Musk-Altman litigation escalating, Hollywood writers secured four-year AI protections

OpenAI Forecasts Advertising to Hit $102 billion by 2030

• OpenAI projects advertising revenue to reach $102B by 2030, becoming its largest revenue driver
• Near-term forecasts show aggressive growth: $2.4B in 2024 to $11B in 2025 (4x increase)
• Represents strategic shift from subscription-first model to ad-supported monetization for AI platforms

How Databricks Sells to Dozens of Industries Without Building a Single Vertical Product

• Databricks uses 'imperatives' framework instead of traditional personas/ICP to sell horizontal platform across dozens of verticals without building vertical-specific products
• Imperatives sit at intersection of three elements: customer priorities (OKRs/accountability), industry trends (market movements), and your product capabilities (differentiated value)
• Traditional personas focus on buyer characteristics and ICP focuses on account fit, but neither forces you to speak the customer's strategic language or connect to their CEO's priorities

I have been coding for 11 years and I caught myself completely unable to debug a problem without AI assistance last month. That scared me more than anything I have seen in this industry.

Problem solving etc degrading is an interesting phenomenon. See it in education too😃, how do you build the competency in first place. Wonder what need is to intentionally not use Ai for tasks periodically?

• Experienced engineer (11 years) discovered degraded debugging ability after relying on AI tools - took longer to solve problem manually than would have 3 years ago pre-AI
• The 'internal monologue' that generates hypotheses under uncertainty atrophies with AI dependency - not just knowledge loss but fundamental problem-solving skill degradation
• GPS analogy: Using AI tools doesn't just provide answers, it prevents the formation of mental models that enable independent problem-solving when tools aren't available

I gave Claude Code our entire codebase. Our customers noticed. | Al Chen (Galileo)

So risky, but the grounding and ROI makes sense

• Non-technical field engineer eliminated engineering support bottleneck by giving Claude Code access to entire 15-repo codebase, creating self-service technical answers
• Code as source of truth beats documentation - current codebase provides more accurate answers than static docs, especially for complex multi-repo architectures
• Customer quirks system creates hyper-personalization at scale - combining repo context with Confluence, Slack, and customer-specific deployment patterns turns single questions into reusable knowledge

I built an AI job search system with Claude Code that scored 740+ offers and landed me a job. Just open sourced it.

• Individual contributor built sophisticated AI job search system using Claude Code that evaluated 740+ listings and resulted in Head of Applied AI role - demonstrates practical AI coding tool capabilities beyond simple automation
• System emphasizes quality over quantity with 10-dimension fit scoring to prevent spray-and-pray applications - contrarian approach to typical job search automation that prioritizes volume
• Open-sourced complete system (MIT license) with 14 skill modes including resume tailoring, company scanning, interview prep, and ATS optimization - shows emerging pattern of professionals building and sharing custom AI workflow tools

People anxious about deviating from what AI tells them to do?

I mean, true or not, real or not this is an interesting topic (more so than the actual link)

• AI over-reliance creating anxiety about deviating from AI recommendations even when contradicted by authoritative sources (manufacturer instructions)
• Emerging pattern of AI-generated authority superseding domain expertise and primary documentation in user psychology
• Critical gap in AI literacy: users not evaluating AI outputs against context-specific authoritative sources or applying critical judgment

Anyone else feel like AI security is being figured out in production right now?

• AI security is being figured out in production with enterprises running 300+ unsanctioned AI apps and most lacking dedicated AI security teams
• Attack patterns mirror early-stage tech adoption: prompt injection, over-permissioned agents, and shadow IT rather than sophisticated exploits
• Traditional security knowledge transfers incompletely - prompt injection ≠ SQL injection, agent permissions ≠ API auth - creating expertise gap despite emerging frameworks (OWASP, MITRE ATLAS, NIST)

some revops teams have stopped doing revops

The SaaS is dead, the SaaS is going to rebound argument well encapsulated in a revops reddit thread

• RevOps teams are misallocating resources by building AI-powered replacements for cheap SaaS tools, ignoring total cost of ownership including AI API costs, engineering time, and quality degradation
• The 'uncapped Claude budget' approach represents a new form of technical debt where teams optimize for visible SaaS line items while creating hidden costs in maintenance, bugs, and user friction
• This represents a broader pattern of AI tool misuse: teams are treating coding assistants as cost-saving measures rather than productivity multipliers, leading to false economies that burn more value than they save

An AI state of the union: We’ve passed the inflection point, dark factories are coming, and automation timelines | Simon Willison

absolutely brilliant and insightful interview

• November 2025 marked inflection point where AI coding agents crossed from 'mostly works' to 'actually works' - specific timeline for capability shift
• Three agentic engineering patterns for daily use: red/green TDD, templates, and hoarding - actionable framework for practitioners
• Dark factory pattern emerging: AI does its own QA without human code review - next evolution beyond current copilot paradigm