5/6/25: The Searchless Future: How Agents, Hallucinations, and AI Factories Are Reshaping GTM

Time for the next level.

May 06, 2025

This is sponsored by the AI Business Network and GTM AI Academy

For the full breakdown of all these articles, you can read for free at the www.gtmaipodcast.com GTM AI Podcast

Welcome my friends! I changed up how we are going to do the newsletter. In the past, I would go into a deep dive of the podcast and then feature 4-5 articles, research, news, and updates. The challenge is, it becomes very long and I want to help keep updated with AI without feeling overwhelmed.

In Depth Podcast Review of ValueOrbit | valueorbit.com CEO and Founder Sami Rejeb

GTM AI Tech of the Week: Abacus.AI

In the Free Newsletter I will cover trending articles and give my thoughts on overall trends as well as links to things you should know about.

This week we have:

Search Engine Land
Digiday & eMarketer Reports
Meta’s Llama 4 App Launch
NVIDIA CEO Jensen Huang at Hill & Valley Forum
Futurism on AI Hallucinations

And if you want to read the full newsletter where I go over more of the overall view this week and updates, you can go to the www.gtmaipodcast.com

Lets dig in to the podcast!

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.

Sami Rejeb is no stranger to transformative technology. With over 20 years of experience in revenue management, he's seen the sector evolve from manual operations to dynamic, AI-driven systems. His journey began as a CRM consultant at KPMG, followed by roles such as Customer Care Director for a mobile operator, Head of Value Selling for Oracle in the EMEA region, and managing RevOps for Salesforce in the Nordics. These experiences, filled with challenges and successes, motivated him to leverage AI to address the key issues revenue leaders face today.

From Global Corporations to Entrepreneurial Ventures

Sami’s global experience stretches across Oracle and Salesforce, and he has now taken a bold leap into entrepreneurship with ValueOrbit. This transition from large corporations to a startup naturally comes with its own set of differences. Sami recognizes that the agility of a startup offers unique advantages not typically found in larger, more established organizations.

While major corporations like Oracle and Salesforce are marked by a high level of sophistication in sales strategies, the startup ecosystem allows for more flexibility and fortunately quick adaptation to new opportunities. At ValueOrbit, Sami aims to harness this flexibility to answer crucial sales-related questions: What deal should I prioritize? Should this be in our forecast? What should my next step be? These questions were central during his tenure at Oracle and Salesforce, and remain so as he pioneers ValueOrbit.

The Birth of ValueOrbit

The inception of ValueOrbit stemmed from a personal mission: to maximize the potential of CRM systems in driving sales success. While working at major organizations, Sami built layers of methodologies atop existing CRM tools, but it wasn’t until the advent of AI that he truly saw the potential to transform these processes. The use of AI in sales—something that previously seemed like a distant dream—became a reality, offering unprecedented possibilities for process enhancement.

Sami’s approach with ValueOrbit focuses on revenue intelligence—spanning deal generation, closing, forecasting, and even conversational intelligence. Unlike traditional competitors, ValueOrbit doesn't simply aim to replicate existing solutions; it strives to redefine them by concentrating on process efficiency and automation.

The Future of CRM and AI Integration

Throughout our conversation, we explored the transformative potential of AI on CRM platforms. Sami believes that while CRM systems like Salesforce offer substantial value, they are ripe for evolution. He envisions a future where traditional CRM models, driven largely by manual input, are replaced or supplemented by automated systems that enhance user interactions.

In the modern sales ecosystem, the integration of AI is not just about speeding up existing processes. It offers a unique opportunity to rethink and redesign the entire sales methodology. The current tools provide enormous data capabilities, but aligning these with practical, day-to-day operations remains a challenge that Sami is eager to tackle.

Challenging the Status Quo: Innovation in Sales Strategy

One particularly enlightening aspect of our discussion was Sami’s commitment to challenging existing processes. Rather than offering a one-size-fits-all solution, ValueOrbit analyzes current methodologies, identifies deficiencies, and suggests improvements. This ensures that implementing new technologies actually addresses the pain points of sales teams, rather than simply automating outdated procedures.

AI’s potential to revolutionize sales goes beyond just data collection. It can predict precise actions that might help close a deal, from setting up meetings to drafting emails—actions that were traditionally based on instinct rather than data. The primary goal for any AI-driven sales tool should be to seamlessly integrate useful insights into the daily operations of sales teams without losing the invaluable human touch.

Navigating the Intersection of People, Process, and Technology

Sami’s vision for ValueOrbit is to sit at the intersection of technology, people, and process. This balanced approach ensures that sales processes are not only enhanced by automation but also retain their effectiveness through human engagement and expertise. He is focused on ensuring that each customer’s interaction with ValueOrbit is valuable and transformative, promising to deliver more than just a product, but a true partnership in success.

As I wrapped up our conversation, reflecting on Sami’s insights, it struck me how important it is to continuously explore new technologies. The evolution of AI presents both a challenge and an opportunity, prompting not just refinement in how we conduct business, but also inviting a complete rethinking of traditional sales processes.

Stay tuned for the next episode of the GTM AI Podcast, where we continue to explore the cutting-edge of technology and innovation in sales—and let’s keep learning and exploring the future just as Sami Rejeb is doing. If you’re as fascinated by these changes as I am, feel free to reach out directly or through the podcast channels. Here’s to a future where technology and human ingenuity go hand-in-hand to drive sales success.

Connect with Sami Rejeb

If you’re interested in learning more about ValueOrbit and the innovative work that Sami Rejeb is doing, I encourage you to connect with him directly. You can reach out via LinkedIn or email him at sami@valueorbit.com.

Thank you for joining me in this deep dive into the future of sales and AI with Sami Rejeb. Keep innovating and challenging the status quo—together, let’s build the future of sales.

The Searchless Future: How Agents, Hallucinations, and AI Factories Are Reshaping GTM

Source Summary:

Search Engine Land:
AI Overviews increase Google visits but reduce engagement
Users visit more, stay less. Queries remain short. A shift toward "resolve and leave" behavior.
Digiday & eMarketer Reports:
AI agents are reshaping search, shopping, and advertising
Advertising is losing ground during product discovery and consideration stages. Agents are now the consumer’s primary interface.
Meta’s Llama 4 App Launch:
Meta releases standalone AI app with deep personalization
Personalized results based on Instagram and Facebook history. Expanding LLM utility into user behavior prediction.
NVIDIA CEO Jensen Huang at Hill & Valley Forum:
AI is the new industrial revolution
Factories will produce intelligence (not goods), supported by “AI HR” departments. Trade jobs will rise to support AI factory infrastructure.
Futurism on AI Hallucinations:
Reasoning models hallucinate more, not less
OpenAI’s o4-mini hallucinates 48% of the time. Model scaling increases confusion. Trust in AI will be earned through constraints and validation.

Strategic Patterns and Implications for GTM Leaders

1. The Consumer Journey Has Collapsed into a Single Prompt

AI Overviews, Meta AI, and OpenAI's agents are compressing search, consideration, and sometimes even conversion into one query-response cycle. Pageviews are down. Query length is stable. Time-on-site is shrinking. The rise of “resolve and leave” behavior marks the end of the breadcrumb-based buyer’s journey. Buyers ask once, get an answer, and leave.

Implication for GTM:
Product awareness is now passive. Consideration is now invisible. Conversion is shifting to the agent layer. Traditional full-funnel tracking is eroding.
Action:
Rethink your buyer journey assumptions. Focus on enabling agent-to-agent product recommendations. Invest in AI-native product feeds and context-aware prompts that make your solution indexable and retrievable in LLMs.

2. AI Agents Have Replaced Search as the Interface to Product Discovery

From Google’s Mariner to OpenAI’s Operator to Meta’s Llama 4 app, AI agents are now the user’s entry point—not the website. They read documentation, interpret sentiment, compare features, and summarize reviews—often without ever sending traffic to you.

eMarketer projects ad visibility will drop 38% in discovery and 47% in consideration. Even if awareness remains, evaluation and differentiation will be handled by agents, not landing pages.

Implication for GTM:
Being “discovered” is no longer about keyword rankings or retargeting. It’s about what agents know and retrieve when asked. If your brand isn’t in their context window, it’s off the map.
Action:
Develop “agent indexing kits”: structured product data, pricing tiers, spec comparisons, and compatibility details formatted for ingestion by LLMs. Make it easy for agents to know you exist and can solve the query.

3. Personalization Is Now Native to AI Interfaces

Meta AI uses user history across Instagram and Facebook to tailor answers. This is not prompt-level personalization—it’s context-level. AI responses vary by who you are, what you’ve liked, and how you’ve behaved.

This expands the scope of segmentation far beyond what most GTM teams track today. It also means that identity-aware agents may show different product suggestions for the same query across users.

Implication for GTM:
Buyer segmentation is now continuous and behavioral. Static ICP definitions won’t hold. AI will personalize journeys based on live digital fingerprints.
Action:
Shift from persona-based to behavior-based messaging. Equip AI tools with dynamic content modules that adapt based on inferred user behavior and system context. Make your content agent-responsive.

4. Hallucinations Are Undermining Trust in Agent Responses

Despite the sophistication of OpenAI’s o3 and o4-mini models, hallucination rates are up. The smarter the model, the more it invents. OpenAI’s own benchmark showed a 48% hallucination rate for its latest reasoning model.

This creates a trust gap. If your product is surfaced by an AI that gets facts wrong, your credibility may erode without you knowing it.

Implication for GTM:
You can no longer rely on AI outputs to speak accurately about your brand. LLMs hallucinating pricing, features, or policies will create misinformation at scale.
Action:
Publish model-grounded data (FAQs, pricing, comparisons) and enable retrieval via RAG pipelines. Push verified product data to major LLMs and maintain changelogs for model trainers. Design your web content with structured facts in JSON-LD or schema markup to reduce misinterpretation.

5. AI Factories Will Transform the Nature of GTM Execution

Jensen Huang calls AI a new industrial revolution. Not metaphorically—literally. In this model, enterprises operate AI factories where electricity goes in and tokens come out. These factories will produce product documentation, sales enablement, onboarding guides, and customer support materials daily.

GTM teams won’t produce assets directly. They’ll direct production, measure outputs, and curate results. The shift is from authorship to orchestration.

Implication for GTM:
Enablement, content, and marketing operations will transition from content creation to process supervision. Your AI stack is your new GTM production line.
Action:
Set up prompt repositories, agent QA protocols, and versioning control. Create “AI output factories” that continuously generate and refresh content tied to business events (e.g., new feature launch → auto-update decks, FAQs, web content).

Closing Insight:

Search is not dying. It is disappearing into layers. From AI Overviews to agents embedded in Meta glasses, the interface to consumer intent is changing. What remains constant is the need for visibility, trust, and context. But how they’re earned is new.

Visibility now requires you to be readable by LLMs. Trust requires verified, traceable data. Context requires AI-compatible structures and flows.

GTM leaders must adapt or be abstracted away.

GTM AI Tech of the week: Abacus.AI

If you’re leading a GTM team in 2025, you’ve likely encountered a growing number of AI-enabled tools promising to accelerate application development, reduce developer dependency, and democratize innovation. Abacus.AI’s latest entrant, AppLLM, is one of the more notable players in this space—offering a low-code, AI-driven development environment that allows anyone to create web apps using natural language.

I’ve been testing AppLLM hands-on, and while it’s still maturing in a few key areas, the implications for product, marketing, sales enablement, and even customer experience teams are substantial.

What Is AppLLM?

At its core, AppLLM is a generative AI platform that allows users to describe an app in plain language, and the system builds it—end-to-end. From creating dashboards to basic JavaScript-based games, AppLLM converts prompts like “build a website that visualizes weekly sales by region” into a functional web app with hosting, editing, and deployment handled in-browser. The result? You have something testable and sharable in minutes, not weeks.

Abacus.AI is known for serious enterprise-grade AI infrastructure, and AppLLM is built atop that foundation. It plugs into their broader suite, including ChatLLM (conversational AI) and CodeLLM (code generation), creating an ecosystem where GTM teams can ideate, prototype, and iterate quickly with minimal reliance on engineering.

Why It Matters for GTM Teams

Here’s where things get interesting for go-to-market professionals:

1. Marketing & Demand Gen:

Want to launch a microsite for a campaign or interactive pricing calculator? AppLLM lets marketers spin up fully hosted assets without a dev sprint.
You can build lead-gen forms, dashboards, and even product demos dynamically—perfect for tailored ABM outreach.

2. Enablement & RevOps:

Imagine field teams requesting interactive playbooks or scenario-based training apps. These are now buildable in a few hours, not weeks.
RevOps teams can quickly mock internal tools for KPI tracking or quota visualization, removing delays from traditional BI or engineering workflows.

3. Customer Experience:

Teams can create self-serve apps for onboarding, support diagnostics, or interactive product walkthroughs using data visualization—without waiting for roadmap prioritization.

4. Product-Led GTM Teams:

AppLLM lets non-technical PMMs or solution engineers mock up product concepts or integrations for testing before formal build.

What Works Well

Speed: From prompt to hosted app in under 10 minutes.
Ease of Use: No code needed. The prompt-first experience is natural, and the IDE lets you tweak outputs.
Integration Potential: While still limited, Abacus.AI is clearly moving toward a suite play, allowing you to add chat agents or data sources as part of your stack.

Where It’s Still Early

Limited Extensibility: As of now, AppLLM doesn’t support databases, user authentication, or third-party API integration (though it’s on their roadmap).
Basic UI: Apps work, but don’t expect pixel-perfect design. It’s functional, not fancy—for now.
Lack of collaboration features: No team-based app management or version control yet, which matters for scale.

Pricing

$10/user/month gets you access, which is refreshingly affordable for GTM teams looking to test internal tools, customer apps, or content ideas without budget fights. The ROI here isn’t in replacing dev teams but in accelerating iteration cycles and reducing time-to-value.

The Bottom Line

AppLLM is not a Figma or a Bubble killer. It’s a creative, practical playground for GTM teams to prototype, test, and iterate without engineering friction. Think of it as a force multiplier for idea validation, stakeholder alignment, and rapid personalization in sales and marketing contexts.

In a world where velocity matters more than polish in early cycles, AppLLM may become a core part of how GTM teams co-create and experiment.

If you’re testing product narratives, designing new onboarding flows, or trying to arm field teams with custom tools, this is worth a hands-on trial. Just be ready for a bit of experimentation—it’s still early, but very promising.

Want a specific use case demoed? Let me know and I’ll test it inside AppLLM.

Abacus.AI's AppLLM

Overview: AppLLM is a low-code platform that enables users to create and deploy full-stack web applications using natural language prompts. It's designed for rapid prototyping and iteration, making it suitable for teams looking to quickly test ideas without deep coding expertise.

Key Features:

Prompt-Based Development: Users can describe the desired application, and AppLLM generates the corresponding code.
Browser-Based IDE: Offers an integrated development environment for editing and deploying applications directly from the browser.
Integration with Abacus.AI Suite: Seamlessly connects with other tools like ChatLLM and CodeLLM for enhanced functionality.

Considerations:

Early-Stage Features: Some functionalities, such as database integration and user authentication, are still in development.
Customization Limitations: While suitable for prototyping, it may not yet support complex, production-level applications.

Lovable.dev

Overview: Lovable.dev positions itself as a full-stack AI engineer, aiming to simplify the app development process for both technical and non-technical users. It emphasizes a balance between AI assistance and user control, providing tools for customization and collaboration.

Key Features:

Multi-Modal Input: Supports prompts, templates, and visual inputs like Figma designs or screenshots to generate applications.
Integrated Backend Services: Offers native integrations with services like Supabase for database management and authentication.
Visual Editor: Provides a Figma-like interface for real-time UI adjustments.
Collaboration Tools: Features like workspaces and chat modes facilitate team collaboration and project planning.UI Bakery+2Lovable+2bolt.new +2 Lovable+4No Code MBA+4Lovable Documentation+4 Trickle AI+1Lovable+1

Considerations:

Learning Curve: While user-friendly, maximizing Lovable.dev's capabilities may require some familiarity with its features and integrations.
Performance: Depending on the complexity of the project, performance may vary, especially for more intricate applications.

Bolt.new

Overview: Bolt.new is an AI-powered app builder that focuses on speed and simplicity, allowing users to go from idea to working code directly in the browser. It's particularly appealing to developers looking for quick prototyping without the overhead of setting up a development environment.BuildCamp

Key Features:

Instant Code Generation: Users can input natural language prompts to generate full-stack applications, including frontend, backend, and database schemas.
Browser-Based IDE: Built on StackBlitz's WebContainers, it offers a seamless development experience without installations.
Framework Support: Supports various frameworks like React, Next.js, and Svelte, providing flexibility in development choices.
Deployment Options: Integrates with platforms like Netlify for easy deployment.UI Bakery Cursor 101+2Banani+2Bolt+2 No Code MBA+4Refine+4Lovable+4

Considerations:

Token-Based Usage: The free plan has daily token limits, which may restrict extensive usage without upgrading.
Customization: While great for rapid development, complex customizations might require additional manual coding.

Conclusion:

AppLLM is best suited for teams seeking a quick and straightforward way to prototype applications without delving deep into code.
Lovable.dev offers a more comprehensive solution for full-stack development, balancing AI assistance with customization and collaboration tools.
Bolt.new excels in rapid development scenarios, allowing developers to quickly bring ideas to life with minimal setup.

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