At Mining Disrupt Miami 2022, held July 26–28, 2022, with Bitcoin down more than half from its highs, Artem Bespaloff, Founder and CEO of ASIC Jungle, took a question every operator in the room was asking: how much should a mining machine cost? His ASIC pricing analysis skipped price predictions and offered a statistical approach to procurement. He argued that the chaos of the mining market is actually a structured distribution, and that understanding statistical anomalies is more valuable than trying to guess the next rally.
Bespaloff began with the basics. He noted that most of an operator's money goes into hardware. Therefore, buying efficiently is not just a nice-to-have; it is a survival mechanism. He presented thousands of data points from ASIC Jungle’s own sales and from public channels to create a clear picture of pricing trends. The goal was to provide a framework for determining whether a specific price point was a good deal, independent of the current price of Bitcoin.
The first key observation Bespaloff shared was the lack of synchrony between Bitcoin’s spot price and ASIC retail prices. The two track each other closely, at a correlation of about 0.85 in dollar terms, but they do not move in lockstep. ASIC prices lag the coin, and the lag is longest in downtrends: about two to three weeks. Operators and resellers often hesitate to lower prices immediately, hoping a sharp decline in Bitcoin is a temporary scare. This hesitation creates a window where hardware prices remain artificially high relative to the coin's new value.
there is a significant lag in the adjustment of ASIC prices in comparison to the bitcoin price
Artem Bespaloff · 6:00
His example came from a couple of months before the conference. With Bitcoin around $40,000, new models sold for $70 to $75 per terahash. When Bitcoin then fell by less than half, ASIC prices fell almost 70 percent, but only after a couple of weeks. Bespaloff argued that this delay is a critical variable for any procurement strategy. For the patient buyer, this lag represents an opportunity to wait for prices to normalize, rather than buying into the initial panic or the delayed premium.
Moving beyond the concept of lag, Bespaloff introduced the central metric of his analysis: the mean breakeven period. Using his dataset, he plotted the average ASIC price in terms of the time required to break even at a standard electricity cost of five cents per kilowatt-hour. The resulting scatter plot showed a high degree of variance, with prices scattering widely around a central tendency line. He identified this mean as the historical average price of ASICs over time. However, he cautioned that relying solely on this static historical artifact is insufficient for making educated purchasing decisions in a dynamic market.
The nuance lies in the context of the Bitcoin price. Bespaloff explained that while the mean was hovering around 15.5 months at the time, a 15.5-month breakeven is not inherently good or bad. Its value depends entirely on where Bitcoin is trading relative to its historical highs and lows. When Bitcoin is near all-time highs, paying a price that is further away from the mean can put an operation at risk. Conversely, when Bitcoin is trading at historical lows, operators might consider purchasing hardware even if the breakeven period is longer than the mean. The potential for a bull run reversal means that a slightly higher purchase price might still yield a favorable return as the coin's value appreciates.
To provide a more actionable tool, Bespaloff introduced the concept of standard deviation. He calculated the standard deviation for ASIC pricing to be 7.6 months. This figure allowed him to define a probability distribution for hardware costs, suggesting that 68 percent of all ASIC prices fall within one standard deviation of the mean. He compared it to the heights of people in the room: if the average is five foot ten with a standard deviation of three inches, someone who is six foot six is an extreme outlier. In statistics, this leads to the concept of regression to the mean, which posits that an extreme value is likely to be followed by a value closer to the average.
in um statistics called the regression to the mean and that refers to the fact that if you take a random variable in a sample and that variable is extreme
Artem Bespaloff · 8:46
Bespaloff applied this principle directly to ASIC purchasing. He argued that extreme prices are rarely sustainable. When Bitcoin is trading high and ASIC prices are selling at two to two and a half standard deviations above the mean, the statistical probability favors waiting. The likelihood that prices will correct downward in the coming months is high. Instead of trying to time the market, operators can use this statistical buffer to identify when a price is an outlier and when it is a fair value.
if you see asics especially when bitcoin is trading high selling at plus two or two and a half standard deviations away from the mean you might want to wait a little bit
Artem Bespaloff · 9:15
Buying within about one standard deviation of the mean, he said, is probably a good deal whatever the point in the market, and when Bitcoin is near all-time lows a buyer can go a little higher. This provides a clear rule of thumb for operators: if the price is within this range, it is a fair deal. If it is outside, the operator should exercise caution and wait for the market to correct.
Bespaloff also addressed the dynamics between different brands, a factor often overlooked in purely quantitative analyses. He observed a pricing gap between Bitmain and its competitors, Whatsminer and Avalon. In uptrends, the price difference between these brands is negligible; operators pay roughly the same dollar amount per terahash regardless of the manufacturer. However, in downtrends, a significant discount emerges for non-Bitmain hardware. Bitmain machines hold their value better and can often be resold at a small premium. Whatsminer and Avalon units do not carry the same resale premium.
when the market is trending down you have a significant discount on [Whatsminers] and avalons
Artem Bespaloff · 11:47
This phenomenon creates a strategic advantage for buyers in bearish conditions. When the market is trending down, the other brands offer a lower entry cost per terahash. The trade-off, Bespaloff said, shows up when you sell: buy Bitmain on an uptrend and you may be able to resell at a small premium, which you cannot do with Whatsminer.
One of the most persistent questions in the mining industry is how low ASIC prices can realistically fall. Bespaloff addressed this by looking at the floor of the market, specifically the scenario where Bitcoin’s price drops so low that mining becomes unprofitable at standard electricity rates. He pointed to October to December 2018, when Bitcoin’s decline made it unprofitable to mine at five or six cents per kilowatt-hour. Miners unplugged their machines and sold them off, and prices on the market were all over the place. Despite the widespread unprofitability, the market did not collapse into zero value. ASIC Jungle sold those S9s to operators in South America and other countries running on zero-cost electricity, pricing them at a six-to-ten-month breakeven on free power. Unprofitable at five or six cents, he said, does not mean unprofitable everywhere.
To conclude his analysis, Bespaloff offered a final piece of advice that cut against the grain of common trading instincts. He urged operators to avoid the temptation to time the market by averaging down or up on purchases. He argued that attempting to predict the exact bottom or top is a form of speculation that is rarely successful. Instead, he recommended developing a more robust strategy for acquiring ASICs, one that relies on the statistical principles he had outlined rather than on gut feelings about market direction. When this session was recorded, Bitcoin traded at $21,268; on October 11, 2026 it was $83,012.
don't try to time the market average down or average up your purchases
Artem Bespaloff · 13:03