Universe Today
In his 1964 paper, "Transmission of Information by Extraterrestrial Civilizations," famed astrophysicist and radio astronomer Nikolai Kardashev addressed the types of transmissions (and at what energies) astronomers should search for in their Search for Extraterrestrial Intelligence (SETI). As part of his analysis, Kardashev proposed a universal scale for classifying the technological advancement of civilizations based on their overall energy consumption. The resulting Kardashev Scale (as it came to be known) came down to the three categories.
- Type 1 (Planetary Civilizations): A civilization capable of harnessing all energy available on its home planet, (4 × 1019 erg/sec)
- Type 2 (Stellar Civilizations): A civilization capable of harnessing all the energy of its home star, likely involving a structure like a Dyson Sphere (4* ×* 1033 erg/sec).
- Type 3 (Galactic Civilizations): A civilization capable of harnessing the energy of an entire galaxy (4 × 1044 erg/sec).
Despite its influence, the Kardashev scale suffers from fundamental limitations in its modeling of the technological evolution of an advanced species. This has come to be known as Kardashev's Conundrum, which questions the viability of modeling development based solely on the exponential growth of energy consumption. In a recent study, Adjunct Researcher and Assistant Professor Sebastian Gurovich proposes a new variant of the Kardashev Scale to quantify a civilization's advancement.
Energy and Computing
At its core, the Kardashev Scale posits that development is characterized not only by energy throughput but also by the amount and complexity of information produced with that energy. As Gurovich notes, this concept dates back to Classical Antiquity, as demonstrated by the Antikythera mechanism (widely recognized as the first analog computer). This device converted mechanical energy into astronomical information, attempting to predict the positions of the Sun, Moon, and planets.
The same is true of Charles Babbage's Difference Engine and Turing Machines, as well as the Application-Specific Integrated Circuits (ASICs) that today secure the Bitcoin network. This belief was echoed by fellow SETI pioneers Joseph Shklovsky and Carl Sagan in their book, The Cosmic Connection: An Extraterrestrial Perspective, who proposed "information mastery" as an extension of energy consumption. However, this connection between energy consumption and computing power has not historically accounted for waste. As Gurovich writes:
A civilization that wastes energy inefficiently scores identically on the Kardashev scale to one that channels equivalent power into sophisticated computation. The Kardashev state variable — power in watts — is therefore dimensionally incomplete as a measure of civilizational advancement: it captures the quantity of energy consumed but not the quality of its use.
Another limitation that Gurovich highlights is Kardashev's projections about the growth rate of civilizations. Based on his estimate of a 1% annual growth rate, Kardashev calculated that humanity would reach a Type II level of development in approximately 3,200 years and become a Type III civilization in 5,800 years. However, this estimate falls short when compared to six decades of global energy production since the Kardashev Scale was proposed.
Updating the Scale
Since Kardashev first proposed his framework for measuring a civilization's development, many revisions have been proposed, while others have suggested recalculating it using other metrics. This includes the aforementioned "information mastery" proposed by Joseph Shklovsky and Carl Sagan, and "planetary mastery" suggested by Robert Zubrin in his book Entering Space: Creating a Spacefaring Civilization. Gurovich proposed framework begins with the Kardashev–Sagan–Nakamoto (KSN) model, which shifts the metric from raw energy consumption to the efficiency of converting energy into information.
According to official estimates, global energy production has increased by more than 3.5 times since 1964, whereas consumption has tripled, consistently growing by 1% to 2% annually. These numbers highlight the waste factor, while energy-to-information efficiency (within the KSN model) has improved by 14 orders of magnitude over the past fifteen years alone. Another consideration is the Landauer Limit, which sets a theoretical minimum energy required to erase a single bit of information, thus placing constraints on the efficiency of computation. As Gurovich indicated:
The KSN Type II threshold, defined as B approaching the Landauer limit, is therefore not merely an energy milestone but an information-thermodynamic one. Whether L is large or small may depend on whether civilizations reach this threshold before exhausting or destabilizing their energy resources — a question the KSN model frames quantitatively for the first time.
He then subjects this updated KSN model to analysis using the Application-Specific Integrated Circuits (ASICs) as a touchstone. These custom chips are optimized for specific tasks, delivering superior speed, power, and efficiency compared to traditional integrated circuits. They are widely used in data centers, smartphones, autonomous vehicles, healthcare, and also to secure the Bitcoin network. Gurovich selected the Bitcoin network because its annual average hashrate provides the only publicly available, auditable, and continuously updated global measure of proof-of-work computation.
The resulting model, says Gurovich, resolves the Kardashev Conundrum by quantifying humanity's historical development as an expression of energy cost per unit of computation from the Antikythera mechanism to the present. "The Kardashev–Sagan–Nakamoto model proposed here is the quantitative expression of this trajectory: it renormalizes the Kardashev state variable by the energy cost per unit of proof-of-work computation, tracing civilizational advancement from the Antikythera mechanism to the present day," he writes.
Finally, he subjected the data to Markov Chain Monte Carlo (MCMC) simulations and a linear Ordinary Least-Squares (OLS) statistical model to obtain an updated estimate of humanity's future trajectory.
Results
In the end, Gurovich's calculations showed that the Kardashev and Sagan exponential models, which yield timescales of thousands of years, do not fit real-world data. Meanwhile, the Linear OLS model produces an estimate of 1.6 × 1016 (1.6 quadrillion) years, far beyond the point at which our Sun will have exited its main sequence phase and become a red giant engulfing Earth.
Meanwhile, the KSN-ASIC model (accounting for the Landauer Limit) meets Sagan's requirement that the Kardashev Scale consider a civilization's information mastery by producing an equation that allows for more nuanced estimates. The timelines this produces are something Gurovich intends to explore in a later paper, but the results he obtained here provide quantitative language for discussing a civilization's technological development.
They also have implications for the Drake Equation and its most important parameter, which is the longevity of civilizations (L). It could also have implications for the Doomsday Clock, which is currently set at 85 seconds to midnight, the closest it has ever been to self-destruction.
Monte Carlo approaches to estimating the probability of causal contact between communicating civilizations provide a complementary framework in which the KSN variable may eventually constrain the longevity factor. This factor carries the largest relative uncertainty of any term in the Drake equation and is sensitive to whether civilizations tend to self-destruct or achieve long-term stability.
