When AI Becomes Table Stakes
In this piece, we explore venture capital in the age of ubiquitous AI
By Adegoke Olubusi, & Ajibola Okungbaye
In 1997 there were about 30 different search engines, and none of them were Google. Many people think that’s about where we are right now with artificial intelligence, AI. In other words, there’s a very good chance that the “Google of AI” doesn’t exist yet.
That’s where venture capital comes in. And every VC is looking for that “Google of AI.” But the explosion of accessible AI tech over the past 2 years means that almost every company is now an AI company in some form. Separating the signal from the noise is key, and it requires navigating challenges including potential market saturation, the need for specialized AI expertise in investment teams, and evolving regulatory considerations.
The fact is, AI is no longer a differentiator. It’s the foundation of the modern startup ecosystem. Investors need to adapt to the new ubiquity of AI, and evolve processes of evaluation. In the paragraphs below, we lay out some of what we’re seeing in terms of trends, new business categories, evolving metrics, risks, and our recommendations for forward-thinking venture capitalists. As impactful VCs, we not only help move technology forward; we also play a key role in shaping a responsible and impactful AI future.
Emerging Trends
Much has been made of the “AI plateau,” but investors shouldn’t take their eye off the ball. Even if there are claims of generative AI models like ChatGPT starting to hit a wall in terms of their pace of improvement, there is still much to explore about genAI.
An exciting development to watch is reasoning AI, systems capable of complex reasoning and decision-making that go beyond simple predictions. Data shows that the slower these reasoning AIs think, the more accurate their results are.
One hotly debated trend is open-source AI vs. closed models. The accessibility of powerful AI tools is a huge part of the democratization of AI. An example of open-source is our portfolio company Inference, a decentralized GPU cluster for LLM inference. Inference currently supports open-source models like Llama 3.1, making it accessible to a wider community of developers and researchers.
We’re also keen on agentic AI. These intelligent software programs are designed to perform tasks autonomously — and often rely on reasoning models. Think virtual assistants, customer service, and supply chain management. SEI (formerly Arda) is a good example: their AI agents seamlessly integrate with teams to amplify impact and help banks, enterprises, and broker-dealers navigate complex compliance landscapes.
Also essential are robust AI infrastructure and development tools. These are needed to support increasingly complex AI systems and include high-performance computing, storage solutions, and more. Applied AI is providing sector-specific solutions in industries like healthcare (diagnostic tools, drug discovery) and fintech (fraud detection and compliance.
New Business Categories, Evolving Metrics
With the democratization of these new technologies arise business categories that simply never existed before. Agentic AI opens up several possibilities, such as AI-powered personalized education, where AI tutors adapt to each student’s needs, requiring new metrics like engagement and retention. Or imagine conflict resolution, where AI agents are trained to negotiate complex agreements, resolve disputes, and facilitate compromise in challenging situations.
Considering the energy consumption of AI, sustainability will be a concern moving forward. But if AI is the problem, it can also be the solution. AI can be used to optimize energy efficiency, especially in data centers. And decentralized AI platforms, leveraging blockchain, promise secure data sharing and more transparency, though their viability for complex AI models remains uncertain.
As VCs, our due diligence process must evolve to better evaluate these new businesses. At MAGIC, we recommend a foundational focus on data strategy; it’s critical to assess the quality and relevance of data as well as how companies govern it and ensure privacy compliance. We need to evaluate AI models beyond basic performance metrics, considering resilience against adversarial attacks, transparency in decision-making, and scalability.
Considering the rapid pace of AI advancement, specialized AI expertise in investment teams is another priority. These specialists help evaluate deal flow and assess the competitive landscape, looking at market positioning, differentiation strategies, and go-to-market plans.
In today’s democratized AI ecosystem, it’s essential to move beyond traditional investment frameworks to holistically evaluate not just technology but the broader risks, ethical implications, and regulatory considerations. This comprehensive approach helps forward-thinking investors back AI companies with the potential for responsible growth and long-term success.
Navigating The Murky Waters
If real estate is all about location, when it comes to AI investment in a saturated market, it’s all about the moat.
Many AI startups rely on similar foundational models and APIs, so evaluating them requires identifying unique advantages like proprietary data or specialized algorithms. The challenge is to differentiate and establish a sustainable competitive advantage. Our strategy emphasizes identifying companies with protectable moats — something that can’t easily be replicated by competitors with more resources. It’s not enough to show a large customer base; the startup must offer something unique and protectable.
They also need to demonstrate scalability. This can be a real hurdle, as AI systems can be resource-intensive and costly. Startups must have a clear strategy to manage these expenses, with a focus on data quality and compliance, especially in regulated sectors. We’re also going to take a close look at the team’s expertise, given the technical and domain-specific knowledge required to build successful AI solutions.
Then there is the evolving regulatory landscape, with global AI laws still very much in flux. Unclear regulations can be a real problem, as 80% of European companies say they don’t completely understand the EU AI Act. For U.S. AI companies, some positive developments include the veto of California’s restrictive SB 1047 and potential tech-friendly policies under the incoming Trump administration. We believe that, overall, governments will support AI innovation — or at least get out of the way — and investors should place their bets optimistically.
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Forward-Thinking Venture Capital
Notwithstanding the risks and challenges we have laid out in this article, ubiquitous AI is a transformative moment. It is akin to the internet revolution, and it will similarly reshape venture capital. As we mentioned in the introduction, this is just the beginning. Forward-thinking VCs can take several steps to get ahead of the curve.
Here’s what we recommend at MAGIC Fund, based on how we’re adapting our strategies to align with a positive AI-driven future.
Embrace open-source AI: Open-source AI democratizes access, offering cost-effective alternatives to proprietary models. Our investment in Inference exemplifies our support for equitable and accessible AI ecosystems, as well as good due diligence.
Prioritize practical applications: We back startups solving real-world problems with unique advantages like proprietary data or niche expertise. Early investment in Jasper AI, now valued at $1.5B, reflects our focus on market-driven impact.
Shape a responsible AI future: We prioritize ethical AI by encouraging fairness, transparency, and sustainability, guiding companies toward responsible growth, and engaging policymakers to balance innovation with societal needs. Forward-thinking VCs must be committed to advancing AI as a force for good, driving progress, and delivering strong returns.
We believe that AI has the potential to revolutionize the world for the better. As a forward-thinking venture capital firm, we are committed to using our resources and expertise to invest in companies that are building a future where AI is a force for good available to all; where it drives progress, empowers humanity, and creates a more just and sustainable world.
Are you building something AI-enabled and amazing? We’d love to chat with you. Shoot us an email at hat@magic.fund.
