Research and development (R&D) is actually a chimera – the so-called mythological creature with two characteristic heads on one body.
they have a solid academic background and usually publish papers, apply for patents, and work on ideas that are prone to come to fruition over the years. Research departments deliver long-term value by discovering the future, asking difficult questions and finding revolutionary answers.
are valued (and hired) for their practical skills and problem-solving skills. Development teams work in fast cycles, focused on achieving clear and measurable results. Although critics of development teams claim that they are simply packaging and repacking products, in reality it is the elements of the product that drive adoption.
If R&D were a basketball team, the players would come from the development department. The research team hung out asking whether or not they could change the rules of the game and whether basketball was even the best game for them.
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Changing AI barriers and value drivers
We see a shift in the AI space. Even though S&P or Fortune 500 corporations proceed to focus on hiring AI researchers, the rules of the game are changing.
As the rules change, the remainder of the game (including players and tactics) changes as well. Consider any large software company. Their core assets – people who they have spent hundreds of thousands of man-hours building and that are valued in the billions on their financial statements – are not houses, buildings, factories or supply chains. Rather, they are huge lumps of code that took a long time to duplicate. Never again. AI-powered automatic coding is the equivalent of robots that build latest homes in a matter of hours, for 1% of the typical cost of a home.
Suddenly we notice that the barriers to entry and value drivers have modified dramatically. This implies that the AI moat – the metaphorical barrier that protects a company from competition – has also shifted.
Today, a long-term and defensible business moat comes from the product, users, and opportunities surrounding it, not research breakthroughs. Perhaps the best sports teams in the world are people who have developed revolutionary strategies, but it is the community, brand and product offering that keep them at the top of the league.
Where will AI dollars bring good returns?
OpenAI, Google, Meta, Anthropic, Cohere, Mosaic Salesforce and at least a dozen other corporations have hired large research teams at great expense to create higher LLMs (large language models) – in other words, to develop latest rules for the game. These invested dollars are likely critical to society, but adding up patents and awards does not guarantee a large return on investment (ROI) for an AI startup.
What will make the difference today is the difference on the development side that transforms latest LLMs into products. Whether it’s a latest startup building something that was once unimaginable, or an incumbent company integrating this latest technology to supply something unique – latest AI capabilities in three key areas create long-term and lasting value:
- Infrastructure for artificial intelligence: As AI is implemented in organizations, corporations must adapt their infrastructure to adapt to changing computing requirements. This starts with chips (dedicated or otherwise) and continues through the data network layers that enable AI data to flow throughout the organization. Much like Snowflake tackled cloud computing, we imagine others are following a similar path in an organization’s AI stack.
- Utility: We are increasingly seeing a narrowing gap between learning through universities and acquiring talent from others. On the other hand, in large organizations, the challenge is not selecting best-in-class technology, but applying it to specific applications. Like Figma in front-end design, we imagine there is a place for corporations that allow many of the hundreds of thousands of developers who are not AI specialists to simply leverage the advantages of an LLM.
- LLM products with vertical orientation: Naturally, as the rules of the game change, latest products develop into possible. Just as Uber could only work when smartphones were prolific, we imagine that creative founders will enrich our world with latest products that were impossible before.
The most significant thing
The key to success in artificial intelligence is moving from groundbreaking research to creating practical applications. While research paves the way for future advancements, development translates these ideas into value.
The latest AI moat lies in unique AI products, not groundbreaking research. Companies that lead in creating user-friendly tools, infrastructure that permits seamless AI integration, and entirely latest LLM-based products shall be the future winners. As the focus shifts from defining the rules of the game to mastering them, the race is on to develop the simplest applications of artificial intelligence.
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