AI has reached escape velocity. The essential ingredients—data, algorithms, compute, talent, capital, and market demand—are all present. Generative AI is now used by 65% of companies, a dramatic increase from just 33% in 2023. AI is projected to add nearly $20 trillion annually to the global economy by 2030.
These advances present an unprecedented opportunity for founders. GC has been at the forefront of this change since 2017, investing billions to back what we see as the most promising AI technologies and talent. We have seen the impact of AI extend beyond individual tools, and we envision a near future where applied AI will fundamentally reshape how companies operate. Applied AI will touch every department—from legal and accounting to customer service, sales, engineering, and beyond—changing the very nature of work and enabling broader industry transformation.
Three key pillars are driving this transformation: intelligence, infrastructure, and workforce enablement. Intelligence pushes the frontiers of model capabilities, infrastructure builds the platforms and systems powered by that intelligence, and workforce enablement reimagines how work is done. At GC, we’re partnering with founders who leverage these pillars to unlock new value and rethink how their industries operate. Throughout this article, we’ll share examples of companies—in our portfolio and beyond—that are building in these areas and fueling transformation.
Intelligence Capabilities: Advancing the Power of AI Models
Business transformation begins with intelligence—the AI models that enable smarter decisions and fuel enterprise growth. We’re seeing a new generation of AI models emerge that are not only powerful but also human-centered, industry-specific, and focused on transparency. These principles are important for building AI solutions that solve pressing business challenges while fostering trust, mitigating risks, and empowering humans to do their most impactful work.
Designing Human-Centered AI
Mistral AI and Anthropic are two leaders in this space, building AI systems that unlock human capabilities and reflect human values. Mistral AI does this by making advanced AI models more accessible and customizable. Its platform allows developers to fine-tune models for specific tasks, like generating different creative text formats or translating languages. By opening up access to powerful models and developer tools, Mistral puts the power of AI into more hands.
Anthropic distinguishes itself by building trust through safety and interpretability. Its Constitutional AI framework, in which models are trained to stay within ethical guidelines and safety boundaries, promotes more predictable behavior. Anthropic is also invested in making its models easier to interpret so users can trace the circuits and algorithms behind a model’s decisions. These principles help to demystify AI and ensure that AI systems are aligned with human values.
Specializing to Solve Industry Challenges
AI systems are also tackling domain-specific challenges. PhysicsX and Manas AI are great examples of companies building specialized AI models that are transforming complex processes like manufacturing simulation and drug discovery respectively.
PhysicsX is using deep learning models to accelerate physics and chemistry simulations, which have been a major bottleneck in industrial manufacturing. Its aerospace model enables end-to-end generative design, allowing engineers to translate performance requirements into aircraft shapes in minutes—a process that would otherwise take days to months.
Manas AI shows how highly specialized AI models can reduce the time and cost of bringing life-saving treatments to patients. Its platform models highly complex relationships between molecular structures and their therapeutic effects, speeding up the drug discovery process for certain cancers and autoimmune conditions.
Hyper-specialization can create real business advantages, but the rapid advancement of AI means those advantages can be fleeting. Companies that rely on a single specialized AI solution risk falling behind as their competitors innovate. Founders need to stay agile, refining their models and looking into new applications of their technology. A hybrid approach—combining specialized and general-purpose models—can also allow companies to create AI systems that are both highly effective and adaptable to a wide range of challenges.
Prioritizing Trustworthy Data Practices
It’s critical for AI models to be built on trustworthy data and respect privacy. Moonvalley is taking this approach in the creative industry with a team assembled from DeepMind, Meta, and Microsoft. Moonvalley’s generative videography platform is built exclusively with licensed data and respects IP rights. This approach benefits the entire industry, building trust with artists while reducing production costs and lowering barriers of entry for filmmakers worldwide. By prioritizing ethical data practices and mitigating legal risks, Moonvalley is setting a new standard for responsible AI.
Infrastructure Development: Building for Enterprise Scale
The democratization of AI is underway. Innovations in model architecture and training techniques are creating more cost-efficient models, as evidenced by the work of OpenAI and DeepSeek. This trend is opening the door for wider AI adoption, particularly in fields like healthcare and biotech, where companies like Manas AI are leveraging cost efficiencies to develop complex AI models for drug discovery.
Powerful and cost-effective models are essential, but they’re just the beginning. Successful enterprise solutions must tackle the complexities of deployment and integration, overcome data challenges, and ensure robust security and compliance. Strategic partnerships can help to overcome these hurdles. We’re seeing great results from collaborations that bridge the agility of innovators with the resources and expertise of established companies.
Operationalizing AI
Innovative companies are building the infrastructure needed to integrate AI into complex business environments. Many current AI models can be unpredictable, sometimes producing varying outputs for the same input. This makes deployment more challenging. Infrastructure needs to be designed so AI can perform reliably and consistently.
Beyond ensuring reliability, successful integration also hinges on weaving AI into existing workflows. This means developing tools that make AI a practical and nearly invisible part of everyday tasks. Codeium is a great example in the world of software development. Its enterprise-focused AI coding assistant and the Windsurf Editor, an agentic IDE, provide highly relevant suggestions and generate code snippets that accelerate development; they also help developers understand complex codebases and enforce coding standards.
To achieve this level of integration, AI solutions need to be secure and compliant. Codeium gets this right with its rigorous approach to data encryption, access controls, and compliance certifications like SOC 2. Its popularity with over 1,000 enterprise customers and one million developers shows the strong demand for AI solutions that boost efficiency without compromising security and governance.
Unifying Data to Drive Insights
Data unification is another piece of the infrastructure puzzle. To effectively integrate AI into their operations, companies first need to break down data silos. Companies like Rox (sales intelligence) and Glean (AI workplace assistant) are using AI to help businesses unlock the full potential of their data for insights and automation.
Rox consolidates revenue-related data from various sources—CRMs, ERPs, ticketing systems, and data warehouses—into a single operating system. Its AI agents automatically research and monitor account activity, helping sales teams orchestrate relationships with their most valuable customers and saving more than eight hours per week per employee.
Glean’s platform taps into data from across an organization’s entire digital workspace so that employees can easily find information, create content, and automate tasks. Most companies have valuable information spread across systems like Jira, Salesforce, Slack, and Teams. By connecting all of this data, Glean becomes an AI-powered work assistant that saves up to 110 hours per user annually.
Scaling through Collaboration
Deploying AI often involves integrating new technologies into existing organizational structures. This process can be complex, but strategic partnerships between innovators and established companies can help to build more effective solutions and navigate the potential challenges of implementation.
The Commure x HCA partnership—which we believe to be the largest integration of AI in healthcare to date—shows how collaboration can drive enterprise-scale implementation. Commure and HCA co-developed an AI platform designed to support existing workflows for providers and healthcare administrators. By working closely with end-users, they ensured effective adoption and are now scaling the technology across 188 hospitals and 2,400 care sites. Its success highlights the importance of designing infrastructure in partnership with those who will ultimately use it.
Strategic partnerships can also accelerate AI solutions for the most critical and complex industries. Take Helsing and Mistral AI, two of our portfolio companies partnering to bolster Europe’s defense capabilities. This partnership combines Helsing’s defense technology expertise with Mistral’s generative AI models, charting the course for a new generation of defense solutions with human-AI collaboration on the battlefield.
While individual partnerships drive innovation, widespread transformation requires mobilizing entire ecosystems—and countries. The EU AI Champions Initiative, which GC is leading with over 70 European companies and in partnership with governments, brings together technology providers and established enterprises to accelerate AI adoption across the region. The initiative shows how success at scale comes not from working in isolation but from partnering to modernize entire sectors.
Workforce Enablement: Transforming How Industries Operate
AI is not just helping people work more efficiently; it's changing the nature of work itself. This shift unlocks unprecedented opportunities to boost productivity, enhance job satisfaction, and deliver better outcomes for everyone.
Revolutionizing How Work Gets Done
AI is transforming the way we work, going beyond simple automation to address labor shortages and redefine employee roles. AI systems are already making a difference in healthcare, increasing the supply and scalability of providers while reducing the unit cost of care. Take Hippocratic AI, which is pioneering a new approach to healthcare automation. Hippocratic has developed AI agents that can handle low-risk, non-diagnostic patient services, creating capacity for medical professionals to focus on the most critical parts of patient care. Hippocratic illustrates how AI can be integrated into healthcare in ways that complement rather than replace human medical expertise.
Intelligent AI agents free up humans to focus on work that requires creativity, emotional intelligence, and complex decision-making. As this trend continues, managing AI agents will likely become a new function or part of an expanded role. Imagine manufacturing engineers collaborating with AI systems to optimize production lines in real-time or energy traders using AI to predict market fluctuations and make more informed investment decisions.
This shift towards an AI enabled workforce will re-onshore productivity and reduce the reliance on offshore labor. We expect new platforms will support this evolution by facilitating collaboration between human and AI workers, training and upskilling workers, and helping companies adapt to the evolving demands of the workplace.
A New Operating Model
AI-native companies will follow a radically different growth model than what we've seen from traditional SaaS companies—one that doesn't rely on headcount and ad spend to achieve scale. Instead, we see an opportunity for companies to reach multi-billion-dollar valuations with lean teams supplemented by an agentic workforce. AI agents are already handling tasks like lead qualification, customer onboarding, basic code generation, and much more. Founders must adjust their growth and hiring strategies accordingly, embracing a more autonomous operating model.
Reimagining Service Delivery
As autonomous operating models take hold, we’re seeing a parallel revolution in service delivery. While previous Silicon Valley innovations focused on digital intermediation, the next wave of AI-driven innovation is transforming industries like accounting, legal, and marketing.
Kick is a leader of this shift with agents that automate multi-stage, complex workflows and fundamentally change how businesses operate their accounting function. With fewer accountants entering the profession, these accounting firms are as much as 10x more efficient, enabling them to serve more clients, increase profitability, and focus on high-value advisory work instead of manual tasks.
The shift from software platforms to AI-powered service providers is already transforming how value is created. Consider Crescendo, which is reshaping the call center industry using an AI-enabled roll-up model. Instead of simply selling software to call centers, Crescendo acquired existing ones like Partner Hero and integrated its AI technology to improve its operations. Crescendo now directly provides an AI-powered service that handles routine customer inquiries and streamlines administrative tasks. By automating both frontline customer support and back-office functions, Crescendo creates margin expansion and infuses technology into the business. This combination of AI and M&A exemplifies GC’s focus on investing in companies that are reimagining legacy business through applied AI.
Creating an Applied AI Ecosystem
The full potential of applied AI can only be realized through a thriving ecosystem. This is why GC is committed to fostering collaboration across industries, startups, established enterprises, and the public sector. We believe that by breaking down traditional boundaries, we can accelerate the development and adoption of transformative AI solutions.
One example of this collaborative approach is the Health Assurance Transformation Company (HATCo), an open platform where providers, technology innovators, and industry experts can converge to share solutions and best practices. This initiative exemplifies our commitment to building ecosystems that benefit both the healthcare industry and the patients it serves.
Building a robust AI ecosystem also requires strong collaboration between the private and public sectors. The General Catalyst Institute is our initiative to drive constructive policy discussions and development in the US, EU, and India. By working closely with governments, we aim to create regulatory frameworks that encourage responsible AI development and pave the way for a more resilient and prosperous future.
Success in applied AI requires more than capital. That’s why we support founders far beyond our individual investments. We provide access to a vast network of potential partners and customers, deep expertise in navigating complex regulatory landscapes, and strategic guidance to help founders refine their business models and go-to-market strategies. We are actively seeking partnerships with founders who are applying AI to modernize core enterprise workflows, build the infrastructure to scale these advancements, and create new possibilities for business and industry transformation.
