A tank crew today fights with the same armor and the same engine it had a decade ago, but the software running its sensors, its displays and its threat-detection logic can be rewritten in a single update cycle. That gap, between a hardware lifespan measured in decades and a threat environment that shifts in months, […]
Bitcoin Magazine
Ocean Mining VP Jason Hughes: BIP-110 on Track to Fail as Miner Signaling Stays Below 1%
This is a guest post by Jason Hughes, VP of Development and Engineering at Ocean Mining. Opinions expressed are entirely his own and do not necessarily reflect those of BTC Inc. or Bitcoin Magazine. The article originally appeared on X.com and has been published with the permission of the author.
Let me start off by saying I’m not pro BIP110, and I’m not anti-BIP110. If it actually succeeds as something that gains true consensus within the network and ends up being enforced by a majority of the network… cool. If so, then we’ll go with it because the network has spoken and accepted it, and all nodes, including non-BIP110 nodes, will be pulled along for the ride. Unfortunately for proponents of the proposal, that simply isn’t currently the case by any measurable metric, nor does it appear to have a trajectory suggesting that will change, either.
There’s been a lot of misleading information about this whole thing, especially in the context of mining. A few quick key bullet points to briefly counter some hyperbole from proponents: BIP110 is NOT inevitable. It CAN fail. BIP110 can and will cause a chain split/fork in a minority hashrate situation. BIP110 is NOT without risk to miners choosing to adopt it. Miners not supporting BIP110 are not suddenly mining “invalid” blocks just because a proposal that isn’t yet adopted simply exists. You’re not a bad person or evil simply because you don’t like or support BIP110. (The fact that I feel the need to point out that last part is actually kind of sad…)
I was going to write a long post to help keep miners informed about things they need to remain aware of as this all plays out… before realizing I already did so months ago, as a document I authored that I had hoped could be put out as a miner education piece at OCEAN. Sadly, it never got published. So I went ahead and updated it, and well, here it is.
Again, keep in mind this was written months ago, intended to be as agnostic as possible in an effort to make it acceptable as a corporate post. That effort failed, so I’m posting it as a personal document today instead. As a miner making important decisions about your operations, you need to be aware of all of this without the sugarcoating and, frankly, outright misleading information coming from some of the BIP110 proponents. You must be vigilant and decide what’s right for you.
While there is certainly some misleading information from the opposition as well, nothing I’ve seen is nearly as egregious as the extremely premature claims of victory and accompanying hyperbole pushed by the BIP110 side. Summarizing my doc a bit, my personal suggestion to miners is this: Signal if you support BIP110. Do not signal if you don’t support BIP110 or don’t care. Either way, monitor the network on/around/before block 961632.
If you continue to see non-signaling blocks from major pools, you can be reasonably certain they’re not going to suddenly decide later to throw away millions of dollars’ worth of revenue to backtrack and signal for BIP110. If they do, by some chance, start to signal for BIP110, you should monitor that and consider switching as required to stay on the heaviest chain. The key point is that, realistically, only one side can win. It’s either BIP110 succeeds, and miners not on the BIP110 side fail, or BIP110 fails, and miners on the non-BIP110 side succeed.
Moving on, let’s dive into a small fraction of my rationale.
Depending on which centralized crawler you look at… no way to know for sure [how many BIP110 nodes are signaling support]. My personal private crawler puts this number much lower, but that’s a discussion for another day. Suffice it to say, I think it’s logical and correct to say that even 15% is not a majority.
Yep, because many miners, merchants, users, etc., all actually wanted Segwit. There was tremendous economic and community weight behind it. Without rehashing that whole thing, as plenty of resources on the topic from before BIP110 are worth a read, suffice it to say that BIP110 and Segwit activations are not quite comparable, as many have already pointed out. Segwit, for example, went into its UASF territory with around 1/3rd of the network’s hashrate already signaling support. With that kind of backing, the UASF to help push the MASF over the tipping point made a lot of sense. It doesn’t make sense here for BIP110.
[0.6% is a] pretty stark contrast to even Segwit’s low baseline support. Yes, I know it’s increased slightly in the past couple of weeks, but no new entrants. Just more clearly rented hashrate from one of the same small proponents.
Something to keep in mind is that mining BIP110 signaling blocks via DATUM on OCEAN carries virtually no risk to the miner up until the fork point at block 961632. The cost is negligible, as you’re effectively guaranteed to recoup rental costs, etc.
It’s awesome that the ability to do so exists, and I wouldn’t have it any other way… but just something to keep in mind when weighing signaling from such blocks in the grand scheme of things from a risk-reward, money-on-the-table perspective.
I also see no evidence to suggest that this could be the case. Subjectively, I disagree with the premise, as it’s not in a mining pool’s best interest to destabilize the network in such a way. Part of the reason for early signaling and lock-in periods is to help coordinate upgrades in a smooth fashion. Waiting until the last minute negates that benefit entirely. I see no compelling rationale or upside to doing so.
Continuing on this, as part of my personal node monitoring setup, I specifically monitor nodes known to belong to various entities, such as other mining pools, exchanges, large lightning nodes, merchants, etc. A supermajority of which are monitored with explicit permission and confirmation/coordination.
Expanding on that, most [mining pools] have updated their nodes since the proliferation of BIP110’s release, even since the release of Knots 29.3. Additionally, it is known that many mining pools run modified versions of their node software to facilitate various requirements of their specific infrastructure. Such changes would need to be ported to a BIP110-compatible client, tested, evaluated, and deployed ahead of time. I currently see no evidence that this is the case currently.
As far as I can tell, the pools are aware but ignoring.
This is one of the funniest and most ridiculous arguments I’ve heard from the pro-BIP110 crowd. Comparing a consensus change that can be unilaterally enforced upon the network by miners and accepted by 100% of existing nodes (a soft fork), with a hard fork which no existing node will accept… is disingenuous at best. T
ightening rules (like BIP110): Soft fork, can be enforced by miners if they choose to do so. Loosening rules (like canceling a halving): Hard fork, can not be enforced by miners without effectively 100% buy-in from the entire network… which isn’t likely to happen. Comparing the two is, bluntly, just stupid.
This would be true of a consensus change that has, well, consensus. While BIP110 has made a valiant effort to gain that consensus, it has yet to have any measurable majority at what is now arguably the 11th hour. Not in nodes, not in hashrate, not in the social layers (consensus.health has a cool visual there where you’ll find me in the middle).
If somehow BIP110 gains 51%+ of the network hashrate on/before block 961632… then, alright. It’s enforced, since as a soft fork a majority of miners can unilaterally enforce it in the absence of a fully adopted URSF (effectively a misnomer, as this would kind of be a hard fork).
Firstly… no I don’t, even though I have. Second, it’s a rushed proposal that never had the time to even try and gain real consensus. It’s been 7 months since the release of the first BIP110 client. There’s ~3 weeks to go before “mandatory” signaling starts as of now (less by the time you read this). 90% of the time available has passed with no change in overall sentiment from any relevant players. If it hasn’t gained sufficient adoption in the past 7 months, it’s not likely to do so in the next 3 weeks.
I’ll be the first to say, even I personally overstated the risk here early on when Core proposed its OP_RETURN change. I personally expected something particularly egregious to hit the chain almost immediately, and to the best of my knowledge, that’s not yet happened. Could it still happen? Yeah, I suppose.
But considering from a technical perspective, byte-for-byte the same contiguous arbitrary data can provably end up stored in the current chain or the BIP-110 chain without much issue… this particular argument for BIP-110 falls pretty flat to me at this point.
Do I want CSAM in the chain? Of course not. Am I a pedophile if I don’t support BIP110? Also not.
I could continue to go on and on and on, but I’ll stop here. I’ve wasted enough time on this. I’m sure I’ve done plenty to annoy both sides of the BIP110 debate at this point, as I don’t adopt either stance. I’m sure I’ll catch flak from all angles simply for daring to speak my mind on it.
Overall, I mostly think it was silly to approach addressing a real problem (the OP_RETURN default change in Bitcoin Core) with the maximum anti-spam manifesto based soft fork proposal… which provably cannot stop spam, arbitrary data, etc. 
(Yes, I know, proponents will claim it’s not about spam… and will also make semantic arguments that it does stop data as well… neither of which appears to be correct.)
I’ll close with the concession that I could be wrong. I’m not Nostradamus, and I can’t accurately predict the outcome with 100% certainty. I can only go by what the data tells me, and so I give BIP110’s success less than a 5% chance of actually succeeding… and I consider that generous. You can take my opinions on this however you wish, but I highly recommend you don’t discount the actual data points, remain vigilant, and do what’s best for you and your mining revenue. Don’t be gaslit by either side of the debate, and make your own decisions.
Here’s a link to the same document linked above for ease of access.
This post Ocean Mining VP Jason Hughes: BIP-110 on Track to Fail as Miner Signaling Stays Below 1% first appeared on Bitcoin Magazine and is written by Jason Hughes.
Every morning, airline dispatchers, grid operators, and farmers around the world make decisions based on the same thing: a weather forecast.
While these forecasts are something that most people glance at for two seconds, weather predictions influence major strategic decisions in many industries, with real money, livelihoods, and even actual lives at stake. Farmers use them to determine which crop variety to sow, when to fertilize, how much to invest in irrigation infrastructure, and how long livestock should graze. Utilities use them to decide where to build solar and wind farms, as well as how to price wholesale electricity. Predictions are used to warn people about extreme weather and to trigger emergency response measures. More recently, weather predictions have become relevant for an emerging industry: prediction markets, where people bet money on all kinds of real-world events, including the weather.
However, the temptation to manipulate weather data to get an edge in these markets, combined with a collective move toward data-driven AI weather forecasting, is starting to put the accuracy of weather predictions at risk. These risks are relatively manageable for now, but as experts in the field, we can foresee scenarios where they snowball into far bigger, more systemic problems.
To develop weather predictions, we need accurate observations of current conditions. These are collected from several sources, including weather stations at airports, utilities, or transport services. Traditional operational systems like the Weather Research and Forecasting model or the European Centre for Medium-Range Weather Forecast (ECMWF) Integrated Forecasting System combine these observations with numerical approximations in order to estimate future weather patterns.
Sometimes, weather stations have issues because of, for example, instrument failures or upgrades in equipment. These can be caught either in real time (through checking and correction) or retroactively. Traditional forecasting systems also have a built-in safeguard called data assimilation: Every incoming measurement is weighed against what the physical model says should be happening and against readings from nearby stations.
Together, these mechanisms help keep weather observations reliable and predictions robust. However, new threats are putting observational accuracy at risk. Earlier this year, news outlets reported that the weather station at Paris Charles de Gaulle Airport (CDG) had been manipulated to record suspicious temperature spikes on April 6 and April 15, 2026. Authorities speculate that a hand-held hairdryer or lighter might have come into play. Either way, it led to some big payouts for online prediction-market gamblers who had bet it would hit 22 °C (71.6 °F) on days when the actual average was around 18°C (64.4°F). One individual won $20,000.
Fortunately, tampering with a single station like this can usually be caught by human monitoring or current statistical methods. In this case, members of a French climate nonprofit association noticed the anomalies by chance and raised the alarm.
But what if there are no human monitoring systems in place? And what about other types of manipulation? What if, instead of tampering with one station, someone remotely nudged the readings at many stations at once—making each change small enough to look plausible on its own? Existing quality controls struggle to catch this kind of coordinated manipulation. And time works against us; careful checks of data and metadata take hours or days, but forecasts have to go out on schedule, whatever the weather is doing.
The shift toward artificial intelligence in weather prediction raises the stakes. These methods are even more dependent on accurate, reliable weather observations; in fact, they are known as “data-driven models.” For example, researchers at ECMWF are exploring whether high-quality weather forecasts can be produced directly from raw observations, skipping the assimilation step that currently acts as a quality filter. Other researchers are going one step further; combining geospatial data (including weather station data) with large language models and agentic AI to support real-time, autonomous decision-making during extreme events such as storms.
Possible benefits are improvements in accuracy, efficiency, and speed. But removing humans from the equation introduces a vast range of new risks.
At the low end of the risk scale, an individual speculator manipulates a weather station for personal gain—that is the CDG Airport case. One step up: A group of traders could coordinate to bias forecasts of renewable energy output, moving wholesale electricity prices and leaving whoever is on the other side of the trade holding the loss. And at the far end, a state actor or saboteur could manipulate one or many stations to set off an early warning system or even keep one silent when it should sound. Step by step, the risk grows, from fraud to compromised disaster preparedness to a matter of national security.
As long as there are financial (or other) incentives to manipulate observational data, adversaries will search for new opportunities, and it is our task to stay one step ahead. Here are three ways.
1. Watch the stations. Data quality controls should include station security, anomaly detection and correction, and human oversight. Weather stations should be monitored continuously to deter tampering. Data homogenization methods that clean up weather records also need to get faster, with the goal of catching problems in real time. This will become increasingly important as agentic AI systems use these data to deliver real-time decisions. Finally, human oversight is needed to flag questionable data and model outcomes. After all, it was humans who caught the CDG Airport manipulation.
2. Protect the data to safeguard the AI. Data defense mechanisms must be positioned throughout the AI pipeline. AI explainability and adversarial robustness tools can help us understand the underlying data and the AI model outputs, help us identify data- or model-related issues, and potentially make us more resilient to adversarial attacks.
3. Ensure continuous accountability along the chain. Observational data passes through many hands: the operators who run the stations, the national weather services that steward the records, and the forecasting centers that turn them into predictions. No single one of them can protect data integrity alone—each guards its own link, and any anomaly needs to be communicated along the whole chain, from station operators to the people acting on the forecast.
It is fortunate that the situation at CDG Airport was caught, but it should serve as a wake-up call. As the role of observational data grows in weather forecasting, we need to adapt to evolving threats. This means protecting our data and models by strengthening existing oversight and accountability structures, and improving coordination among key partners.
This op-ed was written by:
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