Boosting R&D Productivity by 100x with AI Robot Scientists
NexaScience shared a vision for 'April Dream' to increase R&D productivity 100-fold using AI robot scientists, aiming to solve the structural inefficiency of Japan's research environments.
📋 Article Processing Timeline
- 📰 Published: April 2, 2026 at 08:59
- 🔍 Collected: April 2, 2026 at 09:03 (4 min after Published)
- 🤖 AI Analyzed: April 21, 2026 at 06:50 (453h 47m after Collected)
Our company endorses 'April Dream', an initiative to make April 1st a day to broadcast dreams. This press release is the dream of 'NexaScience Inc.'
## Why is Japan's R&D Failing to Produce Results Commensurate with Investment?
Japan's research and development spending exceeds 22 trillion yen annually, accounting for 3.7% of GDP—among the highest in the world. With approximately 900,000 researchers, it ranks third globally. Looking solely at the scale of investment, Japan is an undeniable 'science and technology powerhouse.'
However, that investment is not translating into results.
The number of highly anticipated papers ranking in the top 10% for citations remains at 13th in the world. Japan's global share of patent acquisitions, which once exceeded 30%, has shrunk to about 10%. Over 70% of corporate R&D focuses merely on improving existing products, leaving challenging research that pioneers new markets limited. The growth rate of Total Factor Productivity (TFP), an indicator of technological innovation, has remained at the lowest level among major countries since the late 1990s.
This 'efficiency gap' is the most significant structural problem plaguing Japan's R&D.
## Both at Universities and Corporations, Researchers 'Cannot Focus on Research'
At the root of this issue is the reality that researchers are unable to dedicate their time to fundamental research activities.
The proportion of time university faculty spend on research out of their total working hours dropped from 46.5% in FY2002 to 32.1% in FY2023—a decline of over 14 percentage points in 20 years. Overwhelmed by educational duties and administrative procedures, the number of people who can practically engage in research has actually decreased by nearly 20%, despite an increase in the total number of faculty. According to a survey by the Ministry of Education, Culture, Sports, Science and Technology, about 80% of university faculty respond that they 'do not have enough time for research.'
The situation is similar on the front lines of corporate research. The number of assistants and technicians supporting researchers continues to decline, forcing researchers themselves to handle experiment preparations and administrative tasks. Even more serious is the change in the 'content' of R&D. It has been pointed out that the R&D of Japanese companies heavily resembles what they were doing 10 years ago, showing little progress in tackling new technological domains. While U.S. companies with lower profits tend to seek renewal, Japanese companies show a tendency to remain in existing domains regardless of their profit levels.
Despite having 900,000 researchers, their potential is not being fully utilized. What Japan's R&D faces is not a funding issue, but a structural inefficiency in the research process itself.
## The Era Where AI Accelerates Research Has Already Begun
Meanwhile, globally, the automation and acceleration of research by AI are progressing rapidly.
AlphaFold, developed by Google DeepMind, achieved in minutes the 3D structure prediction of proteins that previously took months or years, leading to a Nobel Prize in Chemistry in 2024. Similarly, DeepMind's GNoME has discovered candidates for over 2.2 million new crystal structures in materials exploration, which is hundreds of times the amount discovered over the past decade.
In the field of drug discovery, there are cases where AI has shortened the period from target identification to clinical trial candidate specification from the traditional several years to about a year and a half. Research into 'autonomous laboratories' that autonomously plan and execute experiments anticipates a 10x to 100x research acceleration through the combination of robotics and AI.
Riding this wave, DeepMind CEO Demis Hassabis, also a Nobel laureate in Chemistry, envisions accelerating biological research by 100 times
## Why is Japan's R&D Failing to Produce Results Commensurate with Investment?
Japan's research and development spending exceeds 22 trillion yen annually, accounting for 3.7% of GDP—among the highest in the world. With approximately 900,000 researchers, it ranks third globally. Looking solely at the scale of investment, Japan is an undeniable 'science and technology powerhouse.'
However, that investment is not translating into results.
The number of highly anticipated papers ranking in the top 10% for citations remains at 13th in the world. Japan's global share of patent acquisitions, which once exceeded 30%, has shrunk to about 10%. Over 70% of corporate R&D focuses merely on improving existing products, leaving challenging research that pioneers new markets limited. The growth rate of Total Factor Productivity (TFP), an indicator of technological innovation, has remained at the lowest level among major countries since the late 1990s.
This 'efficiency gap' is the most significant structural problem plaguing Japan's R&D.
## Both at Universities and Corporations, Researchers 'Cannot Focus on Research'
At the root of this issue is the reality that researchers are unable to dedicate their time to fundamental research activities.
The proportion of time university faculty spend on research out of their total working hours dropped from 46.5% in FY2002 to 32.1% in FY2023—a decline of over 14 percentage points in 20 years. Overwhelmed by educational duties and administrative procedures, the number of people who can practically engage in research has actually decreased by nearly 20%, despite an increase in the total number of faculty. According to a survey by the Ministry of Education, Culture, Sports, Science and Technology, about 80% of university faculty respond that they 'do not have enough time for research.'
The situation is similar on the front lines of corporate research. The number of assistants and technicians supporting researchers continues to decline, forcing researchers themselves to handle experiment preparations and administrative tasks. Even more serious is the change in the 'content' of R&D. It has been pointed out that the R&D of Japanese companies heavily resembles what they were doing 10 years ago, showing little progress in tackling new technological domains. While U.S. companies with lower profits tend to seek renewal, Japanese companies show a tendency to remain in existing domains regardless of their profit levels.
Despite having 900,000 researchers, their potential is not being fully utilized. What Japan's R&D faces is not a funding issue, but a structural inefficiency in the research process itself.
## The Era Where AI Accelerates Research Has Already Begun
Meanwhile, globally, the automation and acceleration of research by AI are progressing rapidly.
AlphaFold, developed by Google DeepMind, achieved in minutes the 3D structure prediction of proteins that previously took months or years, leading to a Nobel Prize in Chemistry in 2024. Similarly, DeepMind's GNoME has discovered candidates for over 2.2 million new crystal structures in materials exploration, which is hundreds of times the amount discovered over the past decade.
In the field of drug discovery, there are cases where AI has shortened the period from target identification to clinical trial candidate specification from the traditional several years to about a year and a half. Research into 'autonomous laboratories' that autonomously plan and execute experiments anticipates a 10x to 100x research acceleration through the combination of robotics and AI.
Riding this wave, DeepMind CEO Demis Hassabis, also a Nobel laureate in Chemistry, envisions accelerating biological research by 100 times