The new frontier for evaluating AI products

The democratization of artificial intelligence is radically transforming the technology landscape and has significant implications for venture capital investment in AI startups. As barriers to entry for building AI products continue to disappear, a new wave of startups is emerging, and venture capitalists must adapt their evaluation and investment strategies to stay ahead in this rapidly evolving market.

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Being in the position of building new technology and working on the cutting edge is a wonderful place to be. Pushing the boundaries of what is possible is a truly exciting and rewarding experience. The same applies to the evaluation and funding of revolutionary technology and the discovery of innovation and new ideas.

In terms of AI technology, both aspects are going to be disrupted in a way that often seems to be marginal. It’s a change that will occur quietly. Like a tsunami wave that nears across the sea and then strikes with full force on the shore. It’s the democratization of Artificial Intelligence and the disappearing of barriers to entry for building AI-products.
Thanks to Open-Source data & AI communities, machine learning frameworks and no code tools, applied Artificial Intelligence is handed over to less-technical businesspeople. Marketing Manager, Sales Representatives and domain experts are now able to build and customize applications without the need for extensive coding knowledge.
This change happened quietly but didn’t come overnight at all. Commercial Machine Learning automation platforms and data science workflow tools have been around for more than a decade already. Those “drag and drop” tools did mark a first wave of AI democratization. 
The second wave reared up in form of a massive Open Source AI movement. Built-in algorithms embedded in free to use machine learning libraries made technology access even simpler. Community platforms were formed around these frameworks, which embraced collaboration, availability and transparency. 
And now a new wave, even more significant than the previous two, is approaching. This wave is characterized by Generative AI and Large Language Foundation Models such as GPT3 and GPT4. We’re talking about machines that can automatically generate and develop AI models based on natural language text prompts.
In other words: It has never been easier to put a working AI product together.
How AI Democratization reshapes the startup game
First of all, this development is great news for entrepreneurs and startups teams, which are now equipped with powerful tools and resources, that were once only available to larger companies. This becomes visible in a shorter time to market and lower development costs.
But what does this increased accessibility of AI technology mean for early-stage funding and VC investments?
What we will see in the near future is a larger pool of AI startups vying for funding caused by the easy access to technology and a decrease in the cost of developing ai products.
These startups will fundamentally differ in their technology stack and their approach in building AI solutions. We will see teams that will focus completely on the application layer by relying 100% on No Code and Gen AI. These companies will have an incredible speed in bringing AI products to market. Their weak point might be a high dependency from their third party tool stack.
We will see entrepreneurs who are deeply rooted in science and build the technology from ground up. These companies may take longer to bring their products to market and continue to follow iterative AI development cycles but make up for it with their flexibility and AI expertise.
And finally, we will see founders who combine the power of automation and Generative AI with their technology and development expertise.
As the market will become increasingly saturated with AI startups of different types, venture capitalists must be even more discerning in their evaluation process to identify high-potential AI companies with strong prospects for growth and profitability. A trade-off will be necessary between high short-term profit and potential long-term success.
How VCs score touchdowns on this new pitch
In an AI market that is picking up speed enormously, that is constantly producing new startups and that is open to entrepreneurs with non-technical backgrounds, the classic VC network is no longer sufficient as a competitive moat.
Deep tech understanding will become the new frontier of evaluating AI startups and the main competitive edge of Venture Capitalists and funds. Investors will have to look beyond shiny AI stickers on pitch deck slides or the mere presence of AI in a product and focus on the specific use case, the quality of the underlying technology and data, iterative development cycles and the potential for long-term scalability.
The underlying science and model layer of AI products will even more play a critical role in differentiation, making it more important than ever for VCs to bring in expertise in areas such as Natural Language processing, Computer Vision and Robotics.
VC Offensive strategies to dominate the AI game 
The way to get there is manifold but we’ve already seen some tentative approaches observed in recent years. 

More and more Venture Capitalists started to hire tech staff and built-up in-house Data Science and AI teams. This shift in strategy represents a departure from classic VC business practices, as VCs seek to enhance their understanding of emerging technologies.

Research partnerships with academic institutions can be a second way of bringing in deep AI tech knowledge. By partnering with universities and the academic world, VCs can gain insights into the latest developments in AI. 
By tapping into the intellectual capital of universities VCs can gain insights into the latest AI developments and leverage cutting-edge research to make better investment decisions. The gained knowledge can help VCs identify promising AI startups and technologies, as well as make more informed investment decisions.

A novel approach to traction is the development of an open source ecosystem that encourages collaboration between VCs. By actively participating in and contributing to open source projects, VCs can gain AI expertise, share knowledge, and foster a vibrant community of AI innovators. By engaging with other VCs, startups, researchers, and developers in the open source community, VCs can broaden their understanding of AI and its applications across various industries.
Moving forward
As the AI landscape evolves and expands, venture capitalists must adapt and innovate to stay competitive in this dynamic environment. The democratization of AI has given rise to a new generation of startups, making it even more crucial for VCs to have deep tech expertise to navigate the increasingly saturated market. By embracing strategies such as building in-house AI teams, partnering with academic institutions, and participating in open source ecosystems, VCs can ensure they have the knowledge and resources necessary to identify high-potential AI companies and make sound investment decisions.
 As we move forward, venture capitalists must remain vigilant in their quest for AI expertise and continue exploring new avenues to stay ahead of the curve. The future of AI investment will undoubtedly be shaped by those who are willing to push boundaries, embrace collaboration, and adapt to the ever-changing landscape of artificial intelligence. By doing so, venture capitalists can contribute to a brighter future for AI startups, drive technological advancements, and create a positive impact across industries.