How Innovation works?
According to the OECD, a significant positive correlation exists between a country’s R&D expenditure and its economic growth rate. Economists have often argued that Innovation typically leads to the development of new technologies or processes that increase productivity. On a national scale, countries that support and invest in innovation tend to have more competitive industries. The World Intellectual Property Organization (WIPO) reports that economies with higher rates of patent filings (a proxy for innovation) like the U.S., China, and Germany, also show robust economic performance. Similarly, The Global innovation Index consistently finds a strong link between the adoption of new technologies and economic performance. Why do Economies that invest significantly in R&D tend to experience faster growth? and why does this investment fuels innovation, leading to new products and services?
Well, firstly Innovative businesses can adapt to changing market conditions and consumer preferences, often gaining a competitive edge both locally and globally. For instance, countries like South Korea and Israel, which spend over 4% of their GDP on R&D, have seen consistent economic growth well above the OECD average.
I recently came across a graph that confirms this idea:
Its not surprise then that Its interesting to note that Economies that quickly adopt new technologies often outperform their peers in GDP growth rates and Companies that are considered innovative (like those in the tech sector) often have higher market valuations and growth projections. For example, technology companies in the S&P 500 index show higher growth rates in revenue and market capitalization compared to more traditional industries.
Open Source:
Today, lot of world's infrastructure runs on software in one way or the other. In the early days of high-performance computing, the major tech companies of the day each invested heavily in developing their own closed source versions of Unix. Microsoft’s CEO, Bill Gates, famously wrote a letter to the computer hobbyist community in 1976, known as the “Open Letter to Hobbyists.” In this letter, Gates addressed the issue of software piracy, specifically within the context of Microsoft’s early software, Altair BASIC, which had been widely copied and distributed without Microsoft’s authorization. Gates argued that software development requires significant investment and that by not paying for software, hobbyists were undermining the industry’s ability to produce good software. He emphasized that developers should be compensated for their work, akin to any other profession providing a valuable product or service. Decades later, who would have predicted that Microsoft would become one of the top open source contributor.
In today's Open source scene, its still interesting how, good software is often developed by tight teams. Small teams usually have a clearer direction and can respond to change faster (startups are a good example). We do note that, Team size is just one (maybe the least important) of several factors that contribute to an Open-Source project's success. In “The Mythical Man-Month,” Fred Brooks introduces the concept of “the surgical team,” emphasizing the importance of having a unified, clear vision of the project’s vision and architecture. He argues that maintaining this clarity and coherence becomes increasingly challenging as more individuals join a project. With more contributors, there’s a proliferation of opinions and debates. While many contributors may have valuable ideas, not all of these ideas will necessarily align or be compatible with each other. This is why the history of open-source software often reveals that many successful projects start with a single visionary founder. Here are a few examples:
- Linux was created by Linus Torvalds.
- Nginx was created by Igor Sysoev.
- Memcached was created by Brad Fitzpatrick.
- Redis was created by Salvatore Sanfilippo.
- Python was created by Guido van Rossum.Open-source Linux eventually rose to prominence—initially because it offered developers the flexibility to alter its code freely and was more cost-effective. Even today, for many of the famous python projects the active core team size managing day-to-day development is relatively small.
Innovative Countries:
The 1960s was the decade of greatest innovation by the US. On the ground thousands of soldiers were dying in Vietnam. There were race riots in the South and campus riots against the war across the US. But the US was busy achieving some of the biggest successes in space travel, and some of the greatest innovations in computer science and medicine. Its a big accomplishment and US mindset of entrepreneurial risk-taking is worth admiring. There is a reason why silicon valley can't be easily replicated elsewhere. However, if we look at a list of contributions to innovations like pacemaker, ultrasound, wi-fi etc. then per capita Australia isn't doing bad either. As a disclaimer, we note that while Australia’s contributions to these technologies are notable, especially in the development of Wi-Fi, the origins and developments of these technologies involved international efforts. I was surprised to learn that Australia is doing fairly well on global innovation index and International Innovation index. There is interesting stuff happening with the space program and Adelaide has a whole bunch of space startups planning a moon mission.
Why should Companies invest in R&D? Cultivating a Mission Directed Approach to AI
Keeping an eye on the latest disruptive innovations in the AI space and adopting a long-term perspective on value creation enables organizations to thrive. Many promising technologies are still in development, discussed at research conferences, and have yet to be commoditized. These technologies hold immense potential for creating value in various organisations. An R&D mindset helps organizations stay informed about emerging trends influenced by algorithmic breakthroughs, hardware advancements, technological commoditization, and novel data availability.
For AI, we need a similar mindset, as companies often struggle to commit to long-term research investments as part of their core values. AI initiatives in organizations have become increasingly diverse, reflecting the broad nature of the field itself. Many industry professionals have acknowledged that relying solely on machine learning (ML) and statistics is insufficient for solving every data problem within the enterprise. By adopting a mission-directed approach, where AI development efforts are directed towards advancing the company's mission, organizations can better align their efforts and gain a competitive edge.
A mission-directed approach to technology development from an AI perspective involves developing a data strategy and exploring the key capabilities that current AI technologies offer. Developing a solid data strategy and building cross-functional teams with relevant expertise are crucial for success in AI and ML projects and can minimize the risk associated with failed initiatives. As a first step, companies can ideate by identifying and addressing key challenges that AI technologies can solve in the medium to long term. After identifying the opportunities, organizations can focus on building unique datasets and data infrastructure to address the identified challenges.
A second key aspect of these initiatives is developing a dedicated R&D team that can continually monitor the latest techniques and industry best practices, focusing on how best to solve data-related business problems within the organization. Over time, by building a team of in-house talent and capabilities, companies can position themselves to develop advanced capabilities beyond incorporating commodity technologies, thus maintaining a competitive advantage in the ever-evolving AI landscape. This approach encourages synergy between research and engineering activities, striking a balance between the two to foster innovation. Ideally, we need to blur the line between research and engineering activities and encourage teams and leadership to pursue the right balance of each.
In summary, AI and ML projects differ significantly from traditional software projects, with numerous unknowns such as the availability of the right datasets, model training to meet accuracy thresholds, and the fairness and robustness of models in production. Organizations can maintain a competitive edge and fully harness the power of AI by adopting a mission-directed approach to technology development, fostering an R&D mindset, and forming cross-functional teams. Embracing these strategies allows companies to stay at the forefront of innovation and maximize the potential of AI to solve complex data challenges in the enterprise.
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