The Botpower Equation
Shifting from the Age of Horsepower to Manpower to Botpower
In a prior blog post, I wrote about our concept of botpower. Read all about it here and think about it like this: throughout history, people have calibrated value in terms of units of work performed. In the industrial age, “horsepower” represented the might of machines that replaced manual labor. The subsequent shift was towards “manpower” or “workforce,” measured the capability of human workers. I believe we are now in the era of "botpower," pointing to the strength of machine learning and artificial intelligence. This shift makes sense since it's typically the top units of measurement that drive economic and technological progress.
Laying the Foundations of a Botpower Equation
GIven a general grasp of botpower, how can we more precisely measure AI's impact at work? It will involve calculating and assessing AI's importance and influence on various tasks, merging technology with a lot of assumptions. While any approach will be subjective, including ours, it's crucial to begin somewhere as an actual equation's benefits can be highly impactful.
There are reasons for this. Grace Hopper's quote in the beginning of this blog stresses the importance of one precise measurement over many expert opinions. Building business models and financial forecasts are foundational to growing any company – and they require concrete inputs. In our first Botpower post, we mentioned that for numerous companies, the ongoing "build versus buy" debate will fade away. The predominant option will be "build," due to reduced costs and improved integration efficiencies. What should that dollar amount be?
Botpower Equation: Deciphering the AI Effort
In coming up with an equation, perhaps we should start with a basic scenario. Let’s consider a situation where an AI system translates volumes of text. The equation could contain several variables:
AI Efficiency (E): How quickly and accurately the AI performs the translation task.
Task Complexity (C): The intricacy of the content being translated.
Scale (S): The number of documents the AI translates.
Operational Overhead (O): The cost of maintaining and running the AI system.
Developmental Cost (D): The resources spent in building and training the AI.
A straightforward formulation could then be: Botpower = E * (C * S) / (O + D)
Easy peasy! My work here is done!
Okay, maybe not, but it’s a start. We next have questions to explore around standardization, valuation, and the costs of bot-human trade-off, in order to enhance any possible equation.
Can We Standardize Botpower? The Feasibility Debate
The concept of botpower standardization raises some interesting questions. Since AI systems are highly specialized and diverse, creating a universal metric that can capture their overall capabilities is not straightforward. Factors like application purpose, training data quality, and integration challenges could introduce significant measurement disparities. However, considering the rapid progress made in AI development and implementation, standardization is crucial to ensure responsible use of these systems.
To address this issue, various organizations have taken on initiatives to establish guidelines and metrics for measuring AI performance. For example, the IEEE Global Initiative on Ethics of Autonomous and Intelligent Systems has developed a series of ethical principles that aim to guide the design, deployment.
Establishing a common framework could help in comparing the performance of AI systems across different applications, facilitating better decision-making for deployment. Though achieving this standardization is no easy feat, the benefits in terms of scalability, interoperability, and innovation could be substantial, driving forward the advancement of AI in many sectors.
2. Can We Price Botpower? Valuation in the AI Age
As we explore the practicality of a botpower equation, here’s another question: can AI's worth be accurately valued? This conundrum boils down to the classic build-versus-buy decision, which ironically is morphing because of AI itself. In the past, organizations would have to weigh the costs of developing AI capabilities in-house against the convenience and possibly lower costs of purchasing or licensing them from the market. However, today open computing is changing all of that.
Open computing is the development and sharing of technologies among multiple parties or networks. Through this shared tech, we can alleviate the burden of customized, centralized R&D enhance interoperability across enterprises and tech stacks, and enable easy access, verification, development, and security across multiple participants.
So bringing back our simple equation: Botpower = AI Efficiency * (Task Complexity * Project Scale) / (Operational Overhead Of Maintaining AI + Development Costs in Building and Training AI)
We see that open computing will drive down the denominator significantly as operational overhead and development costs collapse. However, we are still in the early days of open computing architectures being widely used by enterprises.
Furthermore, as the routine operational, construction, and training tasks may diminish due to AI, there arises a need to consider the necessary oversight, analysis, and internal political management based on its outcomes. It is imperative to evaluate the human costs within a botpower equation, leading to point 3.
3. Can We Manage Botpower? A Test for Corporate Leadership
Botpower will force us to confront the meaning of work, and the inevitable existential questions in an era where machines take on roles that were once the domain of human prowess and intellect. How do business management and HR balance the increased efficiency and potential economic growth against the displacement of workers?
Some will argue this is why it's crucial to have proactive workforce development strategies and re-skill employees. This way, when some jobs get automated, employees can undertake new challenges, potentially in more creative, strategic, or complex problem-solving capacities that machines can't easily do.
But here are some sober facts:
According to a survey conducted by the Digital Data Design Institute at Harvard’s Digital Reskilling Lab and the BCG Henderson Institute, the average half-life of skills is now less than five years, and in some tech fields it’s as low as two and a half years. For millions of workers, upskilling alone won’t be enough.
The World Economic Forum estimates that upskilling the 1.37 million workers in the US at risk against AI, will require a total investment of US$34 billion. This translates to approximately US$24,800 per individual. Expanding this effort globally by a factor of 100 reveals the truly monumental scale of resources needed.
And obviously the costs vary across industries and roles, leading to a lack of uniformly applicable management practices across enterprises. Given these time and expense pressures, corporate leaders need to build their strategies now to manage through this evolution.
Final Thoughts: Embracing Botpower Quantification
Botpower represents a technological leap that should be welcomed and grasped within the wider scope of human experience. There’s no doubt about the challenges however.
Quantifying Botpower and creating a universal metric would be a way to help enterprises manage, forecast, and lead through this change. Like measuring horsepower and manpower in the past, these abstract measurements eventually found concrete applications and contributed to building the world we know today.
With our quest to measure botpower, we're at a pivotal point of assessing tech innovation, economic growth, and human capital management in one unit. While we haven’t (and may never) land on a perfect equation, there is no denying that business leaders need solid ground in this age of AI. Rather than having opinions about what AI means for a company, we need to have the facts. Attempting to find that right quantification is a way to achieve that.