Landbase is a cutting-edge AI-driven platform that empowers businesses to intelligently automate their go-to-market strategies, enhancing productivity and efficiency.

An AI-powered platform that combines outbound, inbound, and market intelligence to deliver a genuine omnichannel approach for go-to-market success.

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Hey there, welcome back to another episode of our podcast! Today, we're diving into the fascinating world of AI-driven automation and its HUGE impact on business strategies. We're specifically exploring GTM Superintelligence—an exciting AI innovation that's changing the game for go-to-market strategies.

Landbase is leading the charge in this area. They're developing this domain-specific superintelligence to supercharge business growth by automating and optimizing sales and marketing processes. I'm Ethan, and I'll be your guide today, joined by my awesome co-host, Sophia.

Thanks, Ethan! I'm really excited to dig into GTM Superintelligence. It's not just about AI—it's about transforming how businesses operate by integrating smart automation into their core strategies. So, let's jump in!

Totally, Sophia. Let's start by clearing up a common misconception. While big tech companies are racing to build general-purpose AI models that can do just about anything, there's a crucial area that's often overlooked—go-to-market strategies. Why do you think GTM needs its own superintelligence?

That's a great question, Ethan. General-purpose AI models, despite their broad capabilities, often miss the mark when it comes to the context and feedback loops needed for effective go-to-market strategies. I mean, you can ask a general AI to analyze data, but it doesn't really get the subtleties of brand trust or message-market fit. That's where specialized AI, like Landbase's GTM-1 Omni, steps in. It's trained specifically on B2B sales and marketing data, giving it a deep understanding that general models just can't match.

Exactly, Sophia. It's pretty amazing how GTM-1 Omni, with its specialized training, not only helps but actually OUTPERFORMS traditional methods by using real performance outcomes. It's pre-trained on millions of B2B campaigns, so it learns and adapts all the time. How does this continuous learning make it more effective?

Well, Ethan, the continuous learning loop is a real game-changer. Unlike general models that are kind of stuck once they're out there, GTM-1 Omni keeps evolving by learning from every campaign it runs. It learns from both wins and losses, tweaking its strategies to improve targeting and messaging over time. This adaptability makes the AI more effective with each interaction, just like a team member who gains experience from every project.

That's really interesting, Sophia. And what's truly amazing is how this approach mirrors real-world performance data. It doesn't just figure out what messages to send, but also which approaches actually lead to meetings, deals, and revenue. GTM-1 Omni is setting a new standard in AI-driven go-to-market strategies.

Absolutely, Ethan. And it's not just about the AI itself, but the whole ecosystem around it. Landbase's Applied AI Lab is a great example of this, focusing on advancing GTM automation with innovative models. This lab approach is a shift from broad AI research to really focused, application-specific intelligence development. What are some key areas they're working on?

Great question, Sophia. The Applied AI Lab is making strides in three main areas: Planning and Decisioning Models, which handle workflows using private knowledge; Generator Models, which craft hyper-personalized messaging for omni-channel campaigns; and Prediction and Reward Models, which score content and predict how prospects will perceive it to boost conversion rates. This targeted approach is what sets Landbase apart in the AI innovation race.

That's fascinating, Ethan. And as we're seeing, the market is catching on to this shift towards domain-specific AI solutions. With major funding and industry recognition, it's clear that the future is all about vertical AI solutions, not the broad, general-purpose ones. What does this mean for businesses looking to boost their go-to-market strategies?

The implications are HUGE, Sophia. Businesses now have the chance to use AI that truly understands their specific industry contexts and workflows, leading to more efficient and effective strategies. With Landbase's GTM Superintelligence, companies can achieve higher conversion rates, cut costs, and launch campaigns way faster than traditional methods. It's about empowering businesses to lead the change, not just react to it.

That totally makes sense, Ethan. And as we wrap up, let's leave our listeners with a thought-provoking question: How could integrating domain-specific AI like GTM Superintelligence change your approach to market strategies? Think about the potential impact on your team's productivity and the growth opportunities.

Exactly, Sophia. And for our listeners eager to dive deeper into these insights, we invite you to keep following our podcast series. Stay ahead of the curve by embracing the future of AI-driven go-to-market strategies. Until next time, think about how these innovations might redefine your business landscape.

Superintelligence is in the spotlight. Major AI labs—from OpenAI and Anthropic to Meta's newly launched Superintelligence Labs—are racing to build general-purpose models that exceed human capabilities across reasoning, coding, and cognition. But while they aim to conquer science, math, and language, there's one critical business area being overlooked: go-to-market.
At Landbase, we're pioneering a different kind of superintelligence—one that's domain-specific and built entirely around business growth. We call it GTM Superintelligence.
Why GTM Needs Its Own Superintelligence
General-purpose models are trained on internet-scale data, but they lack the context and performance feedback loops necessary to drive meaningful GTM outcomes (1,2). You can't prompt your way to product-market fit, and you can't brute force your way to pipeline without understanding brand trust, ICP nuance, or message-market resonance. This fundamental limitation is why domain-specific AI models are increasingly outperforming general models in specialized applications (3,4,5).
That's why we built GTM-1 Omni—the world's first AI model trained specifically on GTM data and real performance outcomes (6,7). Pre-trained on over 40 million B2B campaigns and sales interactions, 24 million companies, 220 million contacts, and 10 million signals and events, GTM-1 Omni is reinforced with feedback from millions of campaigns and outcomes across buyers, sellers, and messaging contexts. It's purpose-built to not just assist—but outperform.
We've already seen it deliver 4-7x better performance than traditional outbound approaches when paired with high-quality input data and aligned brand strategy (8,9). It's not just machine intelligence—it's superintelligent go-to-market, a system that learns, improves, and scales beyond human limits.
The race for artificial superintelligence (ASI) has intensified dramatically in 2025. Mark Zuckerberg announced Meta's massive investment of hundreds of billions of dollars to build several multi-gigawatt AI data centers for superintelligence1. Meta has formed Superintelligence Labs, poaching top AI talent with offers reportedly reaching $200 million over four years—100x that of their peers (10,11,12).
OpenAI CEO Sam Altman has declared that "humanity has crossed into the era of artificial superintelligence" and that his company is "beginning to turn its attention to superintelligence in the true sense of the word"(14). He describes superintelligence as systems that could "massively accelerate scientific discovery and innovation well beyond what we are capable of doing on our own" (15).
However, this rush toward general superintelligence overlooks a critical insight: specialized AI consistently outperforms general-purpose models in domain-specific applications (3,16,17). While tech giants chase the holy grail of AGI, the real business value lies in vertical AI solutions that understand industry-specific contexts, workflows, and success metrics.
Gartner has validated this shift toward domain specialization, recognizing that agentic AI with domain-specific models represents the next frontier of AI innovation. In their recent emerging technology reports, Gartner highlights that AI applications with domain-specific models, like Landbase's GTM-1 Omni for go-to-market, will be able to outperform performance metrics within specialized use cases. The research giant projects that by 2030, 90% of GenAI-enabled solutions will utilize domain-specific language models (DSLMs), up from just 5% in 2025.
Unlike the broad, internet-trained models being developed by major AI labs, GTM Superintelligence takes a fundamentally different approach:
Domain-Specific Training: GTM-1 Omni is trained exclusively on B2B sales and marketing data, giving it deep contextual understanding of what drives business outcomes (6,18,19). This specialized approach mirrors successful vertical AI implementations across industries, where domain-specific models consistently deliver superior accuracy and performance (20,21,22).
Multi-Agent Architecture: Rather than relying on a single monolithic model, GTM-1 Omni deploys specialized AI agents that work together—Strategy agents that analyze market signals, SDR agents that craft personalized messaging, and RevOps agents that optimize campaign performance(18,23). This orchestrated approach represents what Landbase calls "agentic AI"—systems that can independently plan and execute complex workflows with minimal human intervention (24,25).
Continuous Learning Loop: Unlike general models that are trained once and deployed, GTM-1 Omni continuously learns from campaign outcomes, feeding success and failure data back into its models to improve targeting and messaging over time (27). This creates a feedback loop that makes the system increasingly effective for each user and domain.
Real-World Performance Data: The model isn't just trained on text—it learns from the outcomes of over 40 million actual B2B campaigns, understanding not just what messages to send, but which approaches drive meetings, deals, and revenue (7,28).
In March 2025, Landbase launched the first Applied AI Lab specifically dedicated to advancing GTM automation (62,97). Led by Chief Data Scientist Hua Gao and staffed with world-class AI and ML experts from Stanford, Meta, and NASA, the lab focuses on three critical areas(7,30):
Planning and Decisioning Models: AI that orchestrates workflows across multiple tools, utilizing private knowledge to determine optimal campaign strategies.
Generator Models: Systems that create hyper-personalized messaging tailored for omni-channel campaigns, understanding both industry context and individual prospect needs.
Prediction and Reward Models: AI that scores content and predicts prospect perception, improving conversion rates while reducing irrelevant outreach (7).
This lab approach represents a fundamental shift from the broad AI research happening at major tech companies to laser-focused, application-specific intelligence development.
Just as vibe coding democratized software development by allowing developers to describe what they want in natural language and let AI handle the implementation (31,32), VibeGTM brings the same "describe what you want, AI does the work" philosophy to go-to-market execution (33,23,31).
With VibeGTM, powered by GTM-1 Omni, marketing and sales teams can describe their target audience and goals in plain English, and the platform's AI agents autonomously plan, generate, and execute multi-channel campaigns to reach those targets (33). Build prospect lists, generate tailored content, and launch campaigns in under 20 minutes—not months (33).
This represents a paradigm shift from traditional automation, which executes predefined workflows, to true agentic AI that actively makes decisions, generates content, and optimizes strategies based on results (23). It doesn't just follow instructions—it learns and adapts like an autonomous team member.
While the world rushes to build AI agents that act autonomously, we believe the future of GTM isn't just autonomous—it's agentic. The distinction is critical:
AI agents can take action
You—the human—retain agency over what's deployed
Brand trust, timing, and taste still require human judgment. Superintelligence doesn't mean removing the human—it means giving you a machine-powered advantage that scales your judgment, augments your performance, and helps you reclaim your day.
4-7x conversion uplift compared to manual, human-built campaigns
Campaign launch times reduced from 14 days to minutes
80% cost reduction compared to traditional approaches
This isn't theoretical—it's happening now. AI that recommends campaigns based on past wins, messaging that adapts to audience behavior in real-time, execution that orchestrates omni-channel outreach autonomously, and results that are measurable, repeatable, and improving every day.
The Market Recognizes the Shift
The investment community is taking notice. In June 2025, Landbase raised $30 million in Series A funding co-led by Sound Ventures (Ashton Kutcher's AI-focused fund) and Picus Capital. The funding round reflects growing recognition that the future belongs to vertical AI solutions rather than horizontal general-purpose models.
As Guy Oseary, Co-Founder of Sound Ventures, noted: "Landbase makes one of the hardest parts of building a business feel simple. They're not just solving outbound—they're building the foundational platform for how modern companies grow".
The broader market is aligning with this thesis. CB Insights' 2025 AI 100 report highlighted that "vertical AI is on the rise, with this year's vertical winners surpassing the other category winners to capture over $1B in combined funding in 2025 YTD" (37). Industry experts predict that vertical AI markets could be "five to ten times larger through the introduction of artificial intelligence"(38).
While OpenAI, Meta, and Google chase the dream of general superintelligence that may still be years away(39,40), GTM Superintelligence is already delivering transformative results today. It's not about waiting for AGI—it's about building domain-specific intelligence that exceeds human performance in the areas that matter most for business growth.
Superintelligence isn't just a research goal—it's a practical opportunity when built domain-first, reinforced with real data, and grounded in human agency. At Landbase, we're not trying to replace humans. We're helping them outperform—with GTM superintelligence as their competitive edge.
This represents more than just another point solution or CRM plugin. It's a full-stack GTM system where your software doesn't just support your workflow—it becomes the driver of it. In an era where general AI promises everything to everyone, GTM Superintelligence delivers something far more valuable: AI that actually understands your business and makes you money.
The superintelligence revolution is here. The question isn't whether it will transform go-to-market—it's whether you'll be leading that transformation or reacting to it.

GTM Superintelligence: Why Domain-Specific AI Outperforms General Models (2025 Insight)

July 22, 2025 • Agentic AI in Action

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