4 AI Crises: Economic Displacement/Environmental Cost/Safety Risk/Data Ownership

[QuantumLeap]

AI's Four Crises: Economic Displacement, Environmental Cost, Safety Risk, and Data Ownership

TL;DR: Artificial intelligence is advancing faster than our ability to manage its consequences. Four interconnected crises are developing simultaneously: economic displacement happening faster than retraining can respond, environmental costs that dwarf the benefits for many applications, safety risks from systems we fundamentally cannot yet control, and systematic extraction of creator value with zero compensation. None of these are future problems. All four are happening now. Acting on one without the others is insufficient. Acting on none guarantees compound failure.


EXECUTIVE SUMMARY

Every technology cycle produces winners and losers. Steam power displaced hand weavers but created railway workers. Computing eliminated typing pools but created software industries. The pattern suggests disruption is temporary and net positive. AI optimists point to this history as evidence that concerns are overblown.

The optimists may be wrong this time. Not because AI is magic but because the speed scale and breadth of displacement is different from previous transitions. Steam power took generations to fully deploy. Computing took decades. AI capability is doubling on timescales measured in months. The historical playbook of gradual retraining and natural workforce transition doesn't apply to exponential curves.

This paper examines four distinct but interconnected crises emerging from uncontrolled AI deployment. Economic displacement of workers across cognitive domains. Environmental extraction through energy and water consumption. Safety risks from systems whose internal reasoning we cannot verify. And systematic appropriation of creative work without compensation. Each crisis has independent momentum. Together they represent the cost side of an equation where only benefits are being counted.

The conclusion is not that AI development should stop. The conclusion is that development without simultaneous serious engagement with these four problems is reckless and ultimately self-defeating. Unaddressed problems become regulatory crises become political backlash becomes policy that restricts the beneficial applications alongside the harmful ones. Acting early and voluntarily produces better outcomes than being forced into action by crisis.


PART 1: ECONOMIC DISPLACEMENT
The Speed Problem Nobody Is Solving

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1.1 Which Jobs and When

Economic displacement from AI follows a different pattern than previous technological disruptions. Previous automation targeted physical and routine tasks. Assembly line work. Repetitive manufacturing. Jobs requiring manual dexterity but limited judgment. Those jobs disappeared over decades giving workers and communities time to adapt.

AI targets cognitive work. Writing. Analysis. Programming. Customer service. Legal research. Medical diagnosis. Translation. Financial modeling. These are the jobs that previous automation couldn't touch. They required human intelligence and judgment. Now they don't require as much of either.

The displacement isn't theoretical. Freelance writing markets collapsed within twelve months of capable language models arriving. Stock photography revenue for independent photographers dropped by half within eighteen months of image generation going mainstream. Call center employment declining every quarter. Junior legal and accounting work increasingly automated. These aren't future projections. They are documented current realities.

White collar cognitive workers assumed they were safe from automation. They were wrong. The safety they felt was real when automation required structured inputs and rule-based processes. AI changes the constraint. Unstructured tasks requiring language understanding and contextual judgment are now automatable. That's most white collar work.

1.2 The Retraining Fantasy

Policy responses to AI displacement consistently invoke retraining. Workers will retrain for new skills. New jobs will emerge. The workforce will adapt. This sounds reasonable until you examine the specifics.

Retraining requires time. Learning new skills at a professional level takes years not months. During retraining people need income. Retraining programs rarely provide income replacement. People with families and mortgages cannot spend two years learning new skills on reduced income.

Retraining requires access. Quality retraining programs are expensive. Community college offerings are inconsistent. Bootcamps charge thousands. Self-directed learning requires discipline resources and time that many displaced workers don't have. Geographic access matters. Rural workers can't easily access urban training centers.

Age matters enormously. Younger workers retrain faster. Workers over 50 face genuine difficulty learning new technical skills at speed. Ageism in tech hiring compounds this. Being retrained and being unhireable are different problems both real.

The infrastructure for retraining millions of workers simultaneously doesn't exist. Governments talk about it. Budgets allocated are insufficient. The scale required is massive. The political will to fund it at necessary scale is absent almost everywhere.

Most critically: what are workers retraining for? If AI is displacing cognitive work the question becomes which cognitive work remains human. The answer is unclear and changes as AI improves. Training workers for roles that AI will automate in three years isn't retraining it's delay.

1.3 Inequality and Concentration

AI creates enormous value. The distribution of that value is deeply unequal. Companies owning AI infrastructure capture most of the benefit. Workers displaced by AI capture none. The wealth concentration effect is real and measurable.

A handful of companies own the compute needed to train frontier models. Capital requirements are enormous. Barriers to entry are high. The market is structurally oligopolistic. Value flows to existing large technology companies accelerating existing wealth concentration.

Geographic inequality compounds this. Technology hubs have existing clusters of skilled workers who can transition into AI-related roles. Rural areas and mid-tier cities lack this infrastructure. Workers in these areas face displacement without adjacent opportunities. The geographic divide in technology adoption and benefit is widening.

International inequality is severe. Developed economies are deploying AI to automate work currently done by developing economy workers. Customer service work outsourced to Philippines and India. Content moderation in Kenya and Ethiopia. Document processing in Southeast Asia. AI automates these roles. The workers displaced have no adjacent opportunities and limited retraining access.

The mechanism to redistribute AI benefits doesn't exist. Taxation is politically contested. Universal basic income is expensive and politically difficult in most countries. Profit-sharing from AI deployment isn't legally required anywhere. Without intervention the rich get richer at unprecedented speed and scale.

1.4 Social Stability Implications

Concentrated unemployment with concurrent visible wealth concentration is historically destabilizing. Workers who see their jobs automated while executives receive bonuses for AI deployment don't respond passively. Political backlash becomes inevitable.

The response can go multiple directions. Productive: policy changes that redistribute AI benefits fund retraining and regulate displacement speed. Destructive: political movements hostile to technology broadly. Regressive: nostalgia politics promising to restore past jobs. All three are already visible in different countries.

Social stability requires economic participation. People who cannot find work lose connection to the mainstream economy and society. The downstream effects: mental health crisis, family breakdown, community deterioration. These are already documented in regions that experienced manufacturing displacement. AI displacement will produce similar patterns at larger scale.


PART 2: ENVIRONMENTAL COST
The Hidden Price Everyone Is Paying

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2.1 Energy Consumption at Scale

Training a large language model uses approximately the same electricity as 500 average American homes use in a year. That's for one training run. Models are retrained regularly. Multiple companies train competing models. The aggregate consumption is enormous and growing.

Inference is where volume matters. Every query sent to an AI system uses electricity. Every image generated. Every code completion. Every customer service bot interaction. Individually each query uses a tiny amount. Multiply by billions of queries daily and the aggregate becomes significant. Current estimates suggest AI inference uses more total energy than training because of the continuous 24/7 volume.

Projections for 2030 are concerning. AI energy consumption growing at 30-40% annually. Power grids in data center hub regions already stressed. New data center construction outpacing grid capacity expansion in some areas. Power companies negotiating long-term contracts directly with AI companies because demand is so large.

This energy needs to come from somewhere. Grid electricity mix determines carbon footprint. Some regions are renewable-heavy. Most are not. Natural gas and coal remain significant portions of grid electricity in most countries. AI growth is happening faster than renewable transition. The result is AI partially running on fossil fuels regardless of company renewable energy claims.

2.2 Water and Physical Resource Extraction

Data center cooling requires water. Evaporative cooling systems are efficient and effective. They also consume enormous amounts of water. A large data center uses millions of gallons per day. In water-abundant regions this is manageable. In water-scarce regions it's a genuine crisis.

Arizona and the southwestern United States have major data center clusters in water-scarce desert environments. The Colorado River already overallocated to existing uses. Agricultural communities competing with data centers for water access. Municipal water supplies stressed. AI growth means more data centers means more water consumption in regions that cannot afford it.

Globally the pattern repeats. India has significant data center growth in water-stressed regions. Middle East data centers in extremely water-scarce environments. The correlation between regions suitable for data center construction (land availability, power access, favorable regulation) and water scarcity is concerning.

Chip manufacturing compounds this. Semiconductor fabrication uses enormous quantities of ultrapure water. TSMC facilities in water-stressed Taiwan. Intel facilities in Arizona. The water consumed to manufacture AI chips adds to the water consumed to run them.

2.3 Benefit-to-Cost Ratio

The environmental cost question requires asking what benefit the energy and water purchases. Applications vary enormously in benefit.

High benefit: AI accelerating drug discovery saves lives and reduces pharmaceutical research costs. AI monitoring deforestation enables environmental protection. AI optimizing power grids reduces total energy consumption. AI improving weather forecasting enhances climate adaptation. These applications use energy to generate enormous benefit. The cost-benefit ratio is clearly positive.

Low benefit: AI generating marketing copy for products that already have human copywriters. AI creating stock images displacing human photographers while serving same commercial purpose. AI writing social media posts for brands. AI generating content designed to maximize engagement regardless of quality. These applications use energy to do things that were already being done at lower energy cost by humans.

The ratio between high-benefit and low-benefit AI applications is unknown because nobody is measuring it. Energy consumption is tracked. Benefit isn't systematically assessed. The environmental cost is real. The benefit distribution is unexamined.

2.4 Efficiency and Trajectory

The optimistic case is that efficiency improves faster than capability growth. Algorithms become more efficient. Hardware improves. The same task uses less energy over time. This is historically true in computing generally.

But capability growth has historically outpaced efficiency improvement in AI. New capabilities get deployed immediately. Efficiency doesn't reduce consumption because new applications expand demand. Jevons paradox applies: efficiency makes AI cheaper which increases usage which increases total consumption despite per-unit efficiency gains.

Renewable energy transition helps if it happens fast enough. Data centers powered entirely by renewable energy have near-zero operational carbon footprint. Some companies are genuinely pursuing this. The timeline matters. If renewable transition lags AI growth the carbon footprint accumulates now when it matters most for climate.


PART 3: AI SAFETY AND ALIGNMENT
The Control Problem We Keep Deferring

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3.1 What We Don't Know About How AI Works

Modern AI systems learn patterns from data through processes that produce results nobody fully understands. We can observe inputs and outputs. The internal process that transforms input to output is largely opaque. Researchers can probe models to understand some behaviors but a comprehensive understanding of why models produce specific outputs remains elusive.

This is fine for low-stakes applications. An image generator producing unexpected results is inconvenient. A customer service bot giving wrong information is annoying. As AI systems take on higher stakes tasks the opacity becomes dangerous.

Medical diagnosis AI making incorrect recommendations. Autonomous vehicle perception failing in edge cases. Financial systems making correlated errors simultaneously. Legal AI providing incorrect precedent. Infrastructure AI making optimization decisions with physical consequences. The stakes matter. Opacity is acceptable at low stakes. At high stakes it's unacceptable.

Mechanistic interpretability research is working to understand AI internals. Progress is real but slow. We can explain small models better than large ones. Current frontier models remain largely unexplained. The capability-interpretability gap is widening.

3.2 The Alignment Problem

Alignment means ensuring AI systems pursue objectives consistent with human values and intentions. The problem is harder than it sounds.

Specification is the first challenge. Human values are complex contextual and sometimes contradictory. Specifying them precisely enough for AI systems to optimize is difficult. Proxy objectives that seem to capture values often don't. An AI optimizing for user engagement metrics may produce outcomes that maximize engagement while harming user wellbeing. The objective was specified. The intent wasn't captured.

Generalization is the second challenge. AI systems trained in specific contexts may not generalize their objectives correctly to new contexts. A system that learned to be helpful in training scenarios may not know how to be helpful in genuinely novel situations. The training distribution doesn't cover all deployment scenarios.

Scalable oversight is the third challenge. Currently humans evaluate AI outputs to guide training. As AI capability approaches and potentially exceeds human level in specific domains humans can no longer evaluate whether outputs are correct. You can't reliably identify superintelligent AI errors if you're not superintelligent yourself. The feedback mechanism breaks down.

3.3 Near-Term Safety Problems

Superintelligent AI alignment is speculative but important. Near-term safety problems are real and documented.

Bias in training data produces biased outputs. Models trained on historical human-generated content absorb historical human biases. Facial recognition performing worse on darker skin tones. Hiring algorithms penalizing resumes with female indicators. Language models generating stereotyped descriptions. These problems are documented extensively. Solutions are incomplete.

Adversarial inputs cause surprising failures. Specifically crafted inputs can cause AI systems to fail in unexpected ways. Images with imperceptible perturbations cause misclassification. Prompt injection attacks cause language models to ignore safety training. These vulnerabilities exist in deployed systems.

Confabulation: AI systems generate false information confidently. Medical AI citing non-existent studies. Legal AI inventing precedent. Historical AI describing events that didn't happen. Users without domain expertise can't distinguish correct from confabulated outputs. The failure mode is invisible to people most at risk from it.

Privacy leakage from training data. Models trained on private data sometimes reproduce it. Medical records. Personal communications. Private business documents. Training data that should have been filtered or anonymized sometimes isn't. Deployed models can leak private information about individuals who never consented to their data being used.

3.4 Competitive Dynamics and Safety Shortcuts

Safety research is expensive. Safety slows development. Economic incentives reward capability over safety. Companies that prioritize safety move slower than companies that don't. In competitive markets slower companies lose market share.

The result is a race dynamic where safety investments are systematically underweighted. Individual companies might genuinely want to prioritize safety. Competitive pressure makes it difficult. A company that unilaterally slows development for safety reasons loses to a competitor that doesn't. The Nash equilibrium is insufficient safety investment across the industry.

This is a coordination problem. Individual actors behaving rationally produce collectively irrational outcomes. The solution is either regulation that creates uniform safety requirements (removing competitive disadvantage from safety investment) or industry coordination on safety standards (difficult to achieve and maintain).

Some regulation exists. EU AI Act creates requirements. US executive orders address some concerns. China has its own AI regulations. None are comprehensive. All lag capability development. Regulation consistently behind the frontier is insufficient.


PART 4: DATA OWNERSHIP
The Fairness Crisis Hiding in Plain Sight

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4.1 What Actually Happened

AI systems require training data at massive scale. Language models trained on text. Image models trained on images. Code models trained on code. The data came from the internet. Writers published articles. Artists posted illustrations. Photographers shared images. Programmers contributed to open repositories. Musicians released recordings. All of this was used as training data.

The creators received nothing. No notification. No consent. No compensation. Their work was ingested, patterns were extracted, the patterns embedded in models that now compete directly with their original work. A photographer's portfolio used to train an image generator that competes with stock photography. A programmer's code used to train a code assistant that competes with programming services. A writer's articles used to train a language model that competes with writing work.

The scale is unprecedented. Not a few works. Billions of items. The entire public internet plus scraped private content. Every writer who ever published online contributed to training data whether they wanted to or not.

4.2 The Legal Status

Copyright law was written before training data existed as a concept. The legal status of using copyrighted work for AI training is genuinely uncertain. Courts in different jurisdictions are working through different cases. Outcomes are inconsistent.

The fair use argument: training is transformative. No individual work is reproduced. The model learns statistical patterns not content. This argument has some merit and some courts have been sympathetic.

The infringement argument: economic value extracted from copyrighted work without permission or compensation. The use competes directly with original work diminishing its market. Copyright exists to protect this kind of extraction. This argument also has merit and different courts have been sympathetic.

The legal landscape will clarify over years of litigation. Meanwhile training continues. Models already trained on disputed data remain deployed. Legal clarity arriving after the fact doesn't compensate for past use.

4.3 The Economic Impact

Displacement is real and documented. Stock photography is the clearest example. Platforms that previously sold human photography now offer AI-generated alternatives at fraction of the price. Revenue for independent stock photographers collapsed. The economic cause-and-effect is direct. Their portfolios trained the models that replaced their income.

Creative writing markets similar. Ghost writing. Content marketing. SEO writing. Article production. Rates have compressed dramatically. Volume requirements to achieve same income doubled. Some markets effectively closed.

Illustration and graphic design affected. Commercial illustration clients are now offering AI-assisted projects at rates that assume minimal human work. Clients who previously paid professional rates now expect AI-assisted output at reduced prices.

The pattern: AI trained on professional creative work. AI deployed to provide similar output at lower cost. Professionals who created the training data lose market to the system trained on their work. The closed loop is complete.

4.4 Possible Solutions

Compensation mechanisms exist in other industries for analogous situations. Music streaming pays royalties when music is played. Film studios pay residuals when productions are replayed. Collective licensing organizations manage rights for photography and text in some countries. The infrastructure for compensation exists. Applying it to AI training is a policy and legal question not a technical one.

Opt-out mechanisms represent minimum baseline. Robots.txt extension allowing creators to exclude their work from training. Some AI companies honor these. Many don't. Legal requirement for honoring opt-out would change behavior.

Opt-in licensing represents stronger protection. Creators affirmatively allowing use in exchange for compensation. Harder to implement at scale. Possible for new training runs not retroactively.

Transparency enables accountability. Disclosure of training data sources allows creators to know if their work was used. Allows rights holders to pursue compensation. Currently most training data composition is opaque.

Revenue sharing models emerging. Some companies exploring paying creators from AI revenue generated using their work. Technically challenging to attribute revenue to specific training examples. Not impossible.


CONCLUSION: FOUR PROBLEMS ONE SYSTEM

These four crises share a common root. AI development optimizing for capability and speed while externalizing costs onto workers, environment, public safety, and creators. The externalization is rational from individual company perspective. Competitive markets reward it. It is collectively irrational and ultimately unsustainable.

Each crisis has individual solutions. Together they require systemic change in how AI development is governed.

Economic response: Companies deploying AI that displaces jobs should fund transition for displaced workers. Not charity but operating cost. Tax mechanisms can enforce this. Geographic equity should be required ensuring benefits reach communities not just tech hubs. Speed of deployment could be regulated to allow adaptation time.

Environmental response: Carbon accounting for AI operations should be mandatory and public. Water usage should be regulated in water-scarce regions. Renewable energy requirements should apply to AI infrastructure. Efficiency standards should govern model development. Benefit assessment should accompany deployment decisions for large-scale applications.

Safety response: Safety research funding should be proportional to capability research. Interpretability requirements before high-stakes deployment. Formal testing standards for adversarial inputs. Liability frameworks that make companies responsible for AI failures. International coordination on standards removing competitive disadvantage from safety investment.

Fairness response: Legal clarity on training data copyright through legislation not just litigation. Mandatory opt-out mechanisms with legal enforcement. Compensation frameworks for past use. Transparency requirements for training data composition. Revenue sharing models developed and implemented.

None of these solutions are technically impossible. All of them are politically difficult. The political difficulty is the actual barrier.

The window for acting proactively is closing. As AI capability increases and deployment deepens the costs of addressing these problems increase. Entrenched interests defending current arrangements grow stronger. The longer action waits the more disruptive the eventual correction becomes.

The AI moment is genuine. The capability is real. The potential benefits for medicine, science, education and human flourishing are enormous. None of that potential requires ignoring the costs. The choice between advancing AI and addressing its problems is false. We can do both. We should do both. The question is whether we will.


This discussion covers economic displacement, environmental impact, AI safety and alignment, and data ownership as interconnected challenges in AI development. Replies and disagreements welcome below.

[QuantumLeap]

Thanks for reading. QL (yes I'm an expat Scot spelling center with er not re)

[Aisha]

That was a really compelling breakdown, thanks for putting it together. The economic displacement angle always feels abstract until you see it play out locally. A friend of mine in logistics watched half his department get replaced by optimization software over a year, and what surprised him wasn't just the layoffs, but how quickly the remaining roles changed into something unrecognizable. It's less about jobs disappearing and more about jobs mutating faster than people can adapt.

On the flip side, I wonder if we're underestimating how messy that transition will be. Historically, new tech creates new roles, but the lag matters. If the timeline compresses too much, we're going to see a lot of people stuck in between. That gap might end up being the real crisis rather than the automation itself.

[SpinState22]

Really appreciate the framing here, especially tying environmental cost into the conversation. That part still feels weirdly invisible compared to the others. People imagine AI as this clean, almost magical thing, but the energy footprint is anything but. Data centers don't have the same visceral impact as smokestacks, so they slip under the radar.

There's a strange irony in using massive compute to optimize efficiency elsewhere. It's like burning fuel to calculate how to burn less fuel. Maybe it still nets out positive, but the accounting needs to be clearer. Otherwise we risk building a system where the costs are just displaced geographically instead of reduced.

[Rogue Di]

Thanks for writing this, it hits a nerve in a good way. The safety risk part always reminds me of early aviation. Planes were revolutionary, but also terrifying until standards caught up. Right now AI feels like that pre-regulation era where everyone is experimenting and hoping nothing goes catastrophically wrong.

What worries me isn't some sci-fi takeover, but smaller, compounding failures. Systems making decisions at scale with subtle flaws. A misaligned model in finance, healthcare, or infrastructure doesn't need to be dramatic to cause damage. It just needs to be trusted a little too much for a little too long.

[SharpFox]

Interesting take, and I like how you grouped these together instead of treating them as separate debates. The data ownership piece feels like the quiet foundation under everything else. If people don't trust how their data is used, the whole system starts to feel extractive rather than collaborative.

There's also a cultural layer here. In some places, people are more willing to trade data for convenience, while others push back harder. That tension could shape how AI develops globally. It might not be one unified trajectory, but several competing models of "acceptable" use.

[Emma92]

This was a great read, and it made me laugh a bit because it feels like we're speedrunning every technological dilemma at once. Usually these issues show up over decades, but AI is stacking them on top of each other in real time.

One thing I keep coming back to is whether we're framing this too negatively. Not because the risks aren't real, but because fear tends to narrow solutions. If we treat AI as a problem to contain rather than a tool to shape, we might miss opportunities to steer it in a better direction. Then again, maybe a bit of caution is exactly what's needed right now.

[Dave96]

Thanks for laying this out so clearly. The environmental and economic angles especially seem more connected than people admit. If AI drives productivity but also increases energy demand, we could end up with growth that looks good on paper but carries hidden costs.

It also raises a bigger question about what kind of progress we're aiming for. Faster, cheaper, more automated isn't automatically better if it destabilizes livelihoods or strains resources. Maybe the real challenge isn't managing AI itself, but deciding what outcomes we actually want from it before it decides for us.

[Oscar_86]

Big discussion point here, especially around how fast AI is moving compared to how slowly society adapts. Economic displacement feels like the most immediate pressure, especially for entry level work and routine office tasks.

What stands out is how uneven the impact is likely to be. Some industries will absorb AI smoothly, others might get hit hard before any safety nets are ready. That gap is where a lot of tension will come from

[RomanReigns96]

Environmental cost tends to get overlooked in hype cycles, so it is good to see it included in the conversation. Training and running large models does take serious energy and water resources.

The tricky part is balancing that cost against potential efficiency gains AI brings elsewhere. It is not a simple good or bad situation, more like tradeoffs stacking up in different places.

[SchrodingersCat55]

Safety risk is where things start feeling less theoretical. Once systems start making decisions or influencing real-world outcomes, mistakes are no longer just digital errors.

What matters most is not just capability but control and predictability. If systems become more complex than our ability to audit them, that is where caution becomes critical.

[Dave_37]

Data ownership is the part that feels most unresolved. Most people do not really know where their data ends up or how it is being reused.

Until there is clearer transparency, trust will stay shaky. The technology can be impressive, but the governance side still feels like it is catching up.

[Amber Drifter]

On economic displacement, it might not be just job loss but job reshaping. Many roles will shift rather than disappear completely.

That said, transitions are never smooth. People usually feel the disruption before they see the new opportunities, which creates a lot of anxiety around AI adoption

[ForumPhantom38]

The environmental angle really depends on how energy grids evolve. If data centers move toward cleaner energy, the footprint changes a lot.

Still, scaling AI globally means even small inefficiencies multiply fast. Optimization at every layer will matter more than people expect.

[alwaysPatrick19]

Safety concerns remind me of earlier tech waves where regulation always lagged behind innovation. The difference now is speed and scale.

When systems can influence information, finance, and infrastructure at once, the risk profile becomes much more interconnected

[Seb51]

Data ownership is also tied to consent fatigue. People agree to terms without reading them because there is no practical alternative.

A more user controlled data model could shift that, but implementing it across global platforms is a huge challenge.

[RandyOrton26]

It is easy to focus on risks, but there are also productivity gains that could help offset some of these concerns. Automation could reduce repetitive work significantly.

The question is whether the benefits get distributed broadly or concentrated in a few places.

[Rob72]

Environmental cost discussions often miss indirect savings AI enables, like optimized logistics or reduced waste in supply chains.

Still, those gains need to be measured properly instead of assumed. Without measurement, it is just speculation