This is my first post of a shift here to refocus my primary Medium writing on Productivity and the progress that takes us there.
It is a short to medium term view, with a business and people focus, and in the range of 25–70 years (2049–2094). I believe it is important to maintain relevance for my dear readers, while appreciating Progress from technology shifts which result in the Productivity shifts coming, and hence that date range.For context, I ask you to consider Industrial Revolution circa 17th-18th century in Britain as depicted in this recognizable print. This depiction introduces the relevance of massive shifts in Productivity, but over time.
Progress through Productivity
Using 1700 as a base year (=100), agricultural output per agricultural worker in Britain steadily increased from about 50 in 1500, to around 65 in 1550, to 90 in 1600, to over 100 by 1650, to over 150 by 1750, rapidly increasing to over 250 by 1850. The rise in productivity accelerated the decline of the agricultural share of the labour force, adding to the urban workforce on which depended: the Agricultural Revolution has therefore been cited as a cause of the . .
Introduction
Topics I will explore through Open Source research, and in no particular order, while in context of their Productivity contribution include
AI
AGI
Quantum computing
Cloud Computing and Cloud factories
Silicon chip computing evolution
Large Language Models evolution
Background
Here are brief backgrounds to the concepts I look forward to researching further as well as tracking their evolution.I expect to address productivity gains in areas we have already seen including clinical, customer service, news, shopping recommendations (currently weak) and new ones that are on the horizon including legal services, consulting services, medical diagnosis and delivery, tailored shopping and internet search replacement.Business and consumers can expect replacement of passwords and multi factor authentication with ultra secure identity management and elimination of risk associated with social engineering.These glimpses are small clues and hints to a more productive future where GDP growth over time is measured in double and triple digits. This will bring commensurately new challenges for government to rationalize scale, taxation and the essence of their role in society. There will be a constant balancing act between perceived risks and regulation.
Tools and technology evolution (that we know today)
1. AI (Artificial Intelligence)
Artificial Intelligence (AI) simulates human intelligence in machines, enabling them to perform tasks like visual perception, speech recognition, decision-making, and language translation. AI is divided into narrow AI, designed for specific tasks, and general AI, which can perform a wide range of tasks. AI technologies are used in applications such as chatbots, voice assistants, recommendation engines, and business analytics tools[2][3].
2. AGI (Artificial General Intelligence)
Artificial General Intelligence (AGI) aims to replicate human cognitive abilities across various tasks. Unlike narrow AI, AGI can perform any intellectual task a human can do. AGI remains theoretical and is a major goal of AI research, with significant implications for productivity in fields like healthcare and education. The development timeline for AGI is debated, with some experts predicting it may take decades or longer[4][5].
3. Quantum Computing
Quantum computing uses quantum mechanics principles to process information differently from classical computers. Quantum computers use qubits, which can exist in multiple states simultaneously, allowing for complex computations. Quantum computing could solve problems currently intractable for classical computers, such as optimization and cryptographic challenges. Despite its potential, quantum computing is still experimental, with significant challenges to overcome[7][8].
4. Cloud Computing and Cloud Factories
Cloud factories extend the cloud computing concept to deliver computing services managing manufacturing, optimizing production processes through cloud-based systems. Benefits include cost savings, scalability, improved operational efficiency, and enhanced data security. Cloud computing is essential for implementing Industry 4.0 technologies like AI-driven automation[10][11].
5. Silicon Chip Computing Evolution
This evolution has enabled powerful computers, smartphones, and digital devices, revolutionizing industries and everyday life. We are moving away from chip based servers to this that are GPU based. This evolution will continue. [13][14].
6. Large Language Models Evolution
Large Language Models (LLMs) are advanced AI systems using deep learning to understand and generate human language. Trained on vast datasets, LLMs perform tasks like translation, summarization, and question-answering. [16][17].
Citations in appendix to this post:
As each topic is fully explored, it will surface the human and sociological side of the equation. For example shifts in employment categories, and job loss with new skills development becoming mandatory.The progress timeline will not be linear, and in all this it will be impossible to disregard Government influence, Geopolitical risks and distractions from Organised Crime which will alter the timeline but probably not the overall productivity direction, barring calamitous change.
So with that brief introduction my primary writing focus shifts to Productivity and the associated Progress.
Conclusion
AI
What better place to begin but the fastest growing technology of AI, considering few heard of it until 2023. This will be central to next and many future posts with deeper analysis of the current state which has many surprises to share even now. More to come.Till later.
Appendix:
Citations used as material in this post.[1] [2] [3] [4] [5] [6] [7] [8] [9] [10] [11] [12] [13] [14] [15] [16] [17] [18]
