# Matan Grinberg: Value accrual in tech is time-dependent, the US lacks frontier open models, and outsourcing AI development can enhance efficiency | 20VC > Source: > Published: 2026-06-13 18:44:27+00:00 # Matan Grinberg: Value accrual in tech is time-dependent, the US lacks frontier open models, and outsourcing AI development can enhance efficiency | 20VC AI's rapid evolution challenges businesses to balance cost-effective open-source models with expensive frontier solutions. ## Key takeaways - Value accrual in tech is influenced by time, affecting market trends and dynamics. - The lack of frontier open models in the US tech landscape is seen as a significant gap. - AI tools promise productivity gains, but businesses need time to adjust resources. - Focusing on core competencies is crucial for effective resource allocation. - Organizations often become inefficient by focusing on intermediate metrics. - Many firms should consider outsourcing AI development if it’s not a core competency. - Value capture in tech fluctuates, with different players gaining at different times. - The pace of AI model development is accelerating, especially in open-source. - Open-source models offer a cost-effective alternative to frontier models. - Enterprises might reduce the use of frontier AI models due to cost and ROI concerns. - Strategic resource allocation should prioritize business outcomes over headcount. - The rapid release of AI models presents both opportunities and challenges for businesses. - Outsourcing non-core AI development can enhance efficiency and focus. - Understanding value accrual dynamics is key to navigating the tech ecosystem. - Balancing open-source and frontier models is essential for enterprise optimization. ## Guest intro Matan Grinberg is the Founder and CEO of Factory, an AI research lab building autonomy for software engineering. He has raised more than $220 million for the company from investors including Sequoia, Khosla, NEA, Evantic, and 20VC, and previously studied physics before becoming a founder. ## The time-dependent nature of value accrual in tech - Value accrual is a time-dependent phenomenon so many of the tasks that we’re doing we don’t need the very frontier to do it — Matan Grinberg - Different players capture value at different times, influencing competitive dynamics. - Understanding these dynamics is crucial for navigating the tech ecosystem. - The reality is value accrual is a time-dependent phenomenon — Matan Grinberg - Value capture is not static; it shifts over time among different companies. - Companies need to adapt to these shifts to maintain competitive advantage. - It’s maybe for this next year this person is who has the pricing power — Matan Grinberg - Recognizing the fluctuating nature of value capture can guide strategic decisions. ## The US tech landscape and open models - It’s pretty embarrassing that we don’t have frontier open models in the United States — Matan Grinberg - The US lags in developing frontier open models, highlighting a significant gap. - Open models are crucial for driving innovation and maintaining competitiveness. - The lack of open models may hinder the US’s ability to lead in tech innovation. - Addressing this gap could enhance the US’s position in the global tech landscape. - We need to improve in open model development — Matan Grinberg - Developing open models can foster collaboration and accelerate tech advancements. - The current state of open models in the US reflects broader tech ecosystem challenges. ## AI tools and productivity gains - AI tools promise significant productivity gains for businesses. - We will see tremendous growth from these tools — Matan Grinberg - Businesses need time to adjust their resource allocation to leverage AI. - The integration of AI tools can transform business operations and efficiency. - A lot of businesses will have to ask do we want to solve more problems now — Matan Grinberg - Strategic resource allocation is essential to maximize AI’s potential. - The transition to AI-driven productivity requires careful planning and adaptation. - AI tools can enable businesses to solve problems more efficiently. ## Strategic resource allocation and core competencies - Focusing on core competencies is crucial for effective resource allocation. - What is the core competency for our business — Matan Grinberg - Aligning resources with core competencies enhances business outcomes. - Organizations should prioritize business outcomes over increasing headcount. - How do we allocate resources accordingly — Matan Grinberg - Strategic resource allocation can drive efficiency and competitiveness. - Companies need to shift focus from intermediate metrics to meaningful outcomes. - Organizations got bloated by focusing on intermediate metrics — Matan Grinberg ## Outsourcing AI development - Building AI technology is not a core competency for many firms. - Just because you can build a lot of these things does not mean you should — Matan Grinberg - Firms should consider outsourcing AI development if it’s not core to their business. - Outsourcing can enhance efficiency and allow firms to focus on core areas. - If it’s not relevant to your core business, outsource it — Matan Grinberg - Strategic outsourcing can optimize resource allocation and business focus. - Understanding the challenges of AI development is crucial for strategic decisions. - Outsourcing non-core AI development can drive business success. ## The pace of AI model development - The rate of model development is accelerating, especially in open-source. - Every few days yes especially when we look at Chinese open source — Matan Grinberg - Rapid AI model releases present both opportunities and challenges for businesses. - The competitive landscape in AI model development is intensifying. - It’s like three or four a week — Matan Grinberg - Businesses need to stay informed about the latest AI model developments. - The pace of development requires agile adaptation and strategic planning. - Understanding the implications of rapid model development is crucial for success. ## The role of open-source models in enterprise - Open-source models serve as a counterbalance to frontier models. - It’s a really important counterbalance — Matan Grinberg - Enterprises can optimize resource allocation by leveraging open-source models. - Open-source models offer cost-effective alternatives for many tasks. - We can do it much faster much cheaper with these open models — Matan Grinberg - The rise of open-source models is reshaping enterprise AI strategies. - Balancing open-source and frontier models is key to enterprise success. - Enterprises need to evaluate the trade-offs between different AI models. ## Short-term contraction in frontier AI model usage - Enterprises may experience a short-term contraction in frontier AI model usage. - We might see a short-term contraction of usage of the very frontier — Matan Grinberg - Cost concerns and unclear ROI are driving this contraction. - Evaluating the financial implications of AI models is crucial for enterprises. - The contraction reflects broader trends in enterprise AI adoption. - Strategic evaluation of AI model usage can enhance business outcomes. - Understanding the reasons for contraction can guide future AI investments. - Enterprises need to balance cost and innovation in their AI strategies. **Disclosure:** This article was edited by Editorial Team. 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