Introduction
We are living in a co-working model with AI systems which the weight is increasing day by day for the advantage of AI. Meanwhile expectations from AI systems are heightened by the groundbreaking generative capabilities of GPT-like LLMs and the projected arrival of Artificial Superintelligence by 2027 (https://ai-2027.com/summary). Accompanied by all this, humans are still at the wheel in the age of AI.
Fully Autonomous, Unsupervised Driving – Regulatory Hurdles and the Need for Faster Hardware
Except for limited pilot programs, today’s autonomous driving systems are supervised. This kind of journey needs human-on-the-loop, requiring driver oversight which human monitors and be ready to intervene. Next-generation hardware is on the horizon which is expected to be ten times more powerful to arrive by the end of 2026 (https://x.com/elonmusk/status/1803856461333725615). With the arrival of next-generation hardware, robust unsupervised autonomy could bring better safety and security which could help for regulatory sign off.
Ending the Era of Spreadsheets – Advancements in Verifying Generalization and Tool-augmented Chain of Thought
I value spreadsheet software’s role in modern business more than any other tool. Ending it could serve as the canary in the coal mine for the decline of human-driven data analytics. If an advancement ends spreadsheet software usage by human operators, unfortunately this is going to be a time of “dark offices”. Such development affects many roles and accelerates the adoption of AI-driven analytics.
If someone claims that business grade spreadsheet software tasks can be fully automated, the workflow must demonstrate how errors are detected and corrected. To detect errors, you need to go beyond “guessing”. Therefore, in a business workflow, “guessing” is not enough but with cycles of formulate, try, check, and fix any error in formulas/macros/charts inside spreadsheet software are needed. This capability requires advancements in Verifying Generalization and Tool-augmented Chain of Thought. If an AI model faces an unseen task and writes a formula, the verifier can check if it produces the expected result by enforcing correctness checks. In addition, instead of thinking in the context only with complex reasoning, the Agentic workflow can leverage external tools in the intermediate reasoning steps to make the output accurate and trustworthy.
When AI Thinks Alike, Humans Stand Out – The Power of Human Creativity
Prompt engineering improves customization, yet all outputs still rely on LLMs trained on similar data sources, from web crawling, Wikipedia and various informative materials either printed 100 years ago and digitalized or natively in digital form. Human creativity is still at the top of the mountain and will continue to make us special at least to the time of Artificial Superintelligence arrival between 2027 – 2030 when I need to revisit my thoughts. Until then, we keep the wheel of creativity.
Conclusion
While
impressive technologically, AI and hardware developments are unsettling for
humans, as many jobs could vanish. Considering that, we are still steering the wheel,
at least for the next few years. Complex and fully autonomous, robust,
general-purpose self-verification systems are actively unfolding but not yet
complete. With respect to speed in advancements of AI systems and
next-generation hardware, fully autonomous systems are not far away. Artificial
Superintelligence, my expectation is between 2027 and 2030, may dramatically
change the human-AI dynamic. Roles are subject to change: as we expect to automate
everything with AI and make it our workforce, we may also become its workforce
by fueling the hunger of its vast energy demands and following its further
demands. But first, let’s wait for the end of spreadsheet software!
Founder, Vubion.ai