🇯🇴 AI, The Economy & The Slop Problem In A Warming World - Jordan

Green Technology•١٠ كانون الأول ٢٠٢٥•By 3L3C

AI is flooding the economy with capital, content, and energy demand. Here’s how to invest in AI responsibly, avoid “slop,” and align it with green growth.

AI and the economygreen technologysustainable AIAI strategyclimate techdigital transformation

Most companies get AI spending wrong long before they write the first line of code. They chase headlines, not impact. That’s how you end up with billion‑dollar data centers, no clear ROI, and a creeping suspicion that the “AI transformation” is mostly slideware.

Here’s the thing about AI and the economy in late 2025: the money is real, the hype is louder, and the environmental bill is arriving faster than most boards expected. If you care about sustainable growth, green technology, and actually getting value from AI instead of pouring cash into servers and “slop” content, you can’t just copy what the biggest players are doing.

This matters because AI is now shaping three things at once:

Where capital flows (infrastructure, chips, cloud, automation)
How work is organized (jobs displaced, jobs created, skills re‑weighted)
How much energy and material we burn to keep the whole thing running

The reality? It’s simpler than you think: AI only creates durable economic value when it solves real problems more efficiently than the status quo and when the environmental cost per unit of value keeps going down. Everything else is noise.

In this post, I’ll walk through how AI is actually reshaping the economy, why we’re drowning in AI “slop,” and how green‑tech leaders and sustainability‑minded businesses can invest in AI without falling into the hype trap.

How AI Is Really Changing The Economy

AI is changing the economy by reallocating capital, reshaping labor, and hard‑wiring new dependencies on data and energy infrastructure.

1. Capital is flooding into AI infrastructure

Over the last two years, tech giants have committed hundreds of billions of dollars to AI data centers, custom chips, and networking gear. Some analysts project global AI‑related infrastructure spending could exceed $1 trillion by 2030.

That scale of spending has three big economic effects:

Concentration of power: A handful of hyperscalers own the models, the hardware, and the training data. Most other companies rent access.
Upstream booms: Chipmakers, power utilities, grid operators, and specialist materials suppliers are experiencing AI‑driven demand spikes.
Downstream pressure: Everyone else is pushed to “keep up” with AI—whether or not there’s a clear business case.

Green‑tech angle: those data centers are hungry. A single large model training run can consume as much electricity as thousands of homes use in a year. If that load isn’t matched with clean power, AI growth works directly against climate commitments.

2. Labor is being reorganized, not just replaced

Most public debate still gets stuck on “Will AI take all our jobs?” We’ve been here before with automation scares. Historically, technology replaces specific tasks and roles, but net employment can grow when new industries and services emerge.

What’s different with AI in 2025 is the speed and scope:

Routine cognitive work—drafting, summarizing, templated analysis—is now cheap.
High‑value work is shifting toward problem framing, oversight, judgment, and integration.
Entire functions (like customer support or basic ad production) can be partially or heavily automated.

For sustainability and green‑tech companies, this reorganization can be an advantage:

Data‑heavy tasks (sensor data cleaning, emissions reporting, compliance docs) can be handled by AI.
Human experts can focus on system design, stakeholder engagement, and innovation, not paperwork.

But the risk is very real: if AI productivity gains are captured only by capital owners, inequality widens, and resistance to climate and tech transitions hardens.

3. AI is turning data and energy into core economic inputs

With traditional software, compute was a cost. With large‑scale AI, compute and electricity are the product. You’re buying predictions and generative output that exist because you’re burning energy through models.

That means two things:

Energy efficiency is now a competitive advantage. Firms that can train and deploy models with fewer FLOPs per outcome will win.
Clean energy is a strategic asset. Access to cheap, low‑carbon power directly shapes where AI infrastructure gets built.

If you’re in green technology, this is your opening. AI needs cleaner grids, better cooling systems, smarter demand management, and high‑efficiency hardware. Those who can deliver these will sit in the value chain, not just on the sidelines.

The AI Hype Index And Why We’re Drowning In Slop

The AI “slop” problem is simple: when it becomes cheap to generate content, the internet fills up with cheap content. Most of it’s forgettable; some of it’s actively harmful.

“Slop” shows up as:

Auto‑generated articles that repeat the same points with no insight
SEO spam pages that exist purely to capture ad or affiliate revenue
Low‑effort videos, product copy, and images churned out at industrial scale

From an economic perspective, AI slop is negative‑sum:

It overloads users, making it harder to find accurate information
It pollutes training data for the next generation of models
It drives a race to the bottom in content quality and trust

For brands, especially those working in green technology where trust and scientific credibility matter, this is deadly.

Why people keep producing AI slop anyway

Despite all the downsides, AI slop keeps growing for three reasons:

It’s cheap. Once your workflows are set up, marginal cost per article or image is close to zero.
Short‑term metrics love it. More pages, more impressions, more “content shipped.”
Hype pressure. Boards and investors want to “see AI,” and spinning up content farms is visible and easy.

I’ve found that teams slip into slop not because they’re lazy, but because they’re measuring the wrong thing. They track volume instead of outcomes. “We published 200 AI blog posts this quarter” sounds impressive until you look at:

Genuine leads created
Time on page and return visitors
Stakeholder trust and reputation metrics

Most companies discover—too late—that their AI content strategy has flooded their own channels with noise.

The slop‑resistant approach

There’s a better way to approach AI in content and communication, especially for climate and sustainability topics:

Use AI as a research and drafting assistant, not an autopilot
Keep experts in the loop to inject real data, field experience, and clear positions
Publish fewer, better pieces that answer specific, high‑value questions
Audit your outputs: if a piece adds nothing unique, don’t ship it

This isn’t just an ethical stance. It’s an economic one. High‑trust, high‑signal content drives better leads, attracts serious partners, and makes your brand a reliable node in an increasingly messy information ecosystem.

How Much AI Investment Is Too Much? The Sensible Spending Threshold

There is a limit to sensible AI spending, and companies are starting to crash into it.

The pattern goes like this:

Leadership sees AI margins and valuations rising elsewhere.
They green‑light massive AI budgets without a portfolio of grounded use cases.
Two to three years later, they’re stuck with sunk costs, rising cloud bills, and underused models.

Learning from early missteps, a smarter AI investment strategy now follows three rules: ROI clarity, staged experimentation, and resource realism.

1. ROI clarity: tie AI directly to business and climate goals

Before building anything serious, answer two hard questions:

What metric moves if this works? (Revenue, cost per unit, emissions per unit, defect rate, project cycle time, etc.)
How big is that movement, realistically? (10%? 30%? Not just “better.”)

For green‑tech and sustainability teams, strong AI projects usually sit in areas like:

Predictive maintenance for turbines, solar farms, and industrial equipment
Grid balancing, demand forecasting, and energy storage optimization
Automated carbon accounting and supply‑chain traceability
Smarter logistics and routing that cut fuel and emissions

If you can’t map an AI project directly to a business KPI and a sustainability KPI, it’s probably not investment‑grade yet.

2. Staged experimentation: start small, scale what works

Sensible AI spending treats projects like a venture portfolio:

Stage 1: Cheap experiments (weeks, not months). Use off‑the‑shelf models and existing data to test whether there’s signal.
Stage 2: Pilot in production with a narrow scope and clear exit criteria.
Stage 3: Scale and integrate only if the pilot proves its worth.

Each stage has a kill switch: if it doesn’t hit pre‑agreed metrics, you stop.

This approach keeps you out of the “infinite AI R&D” trap—where teams burn budget exploring impressive demos that never ship.

3. Resource realism: power, people, and partners

The hidden constraint on AI projects in 2025 isn’t just money; it’s:

Power availability: Can your sites handle the extra load? Can you offset it with renewables?
Talent: Do you have people who understand both AI and your domain (energy, climate, infrastructure), not just one or the other?
Vendors and partners: Are you locked into one provider, or can you swap components as the market shifts?

Over‑investing in AI without planning for energy and talent leads straight to stranded assets—hardware and models that are technically impressive but strategically useless.

AI, Inequality, And The Climate: Who Wins And Who Loses?

AI can either accelerate a fair, green transition—or make inequality and climate risk worse. The direction depends on who controls the tools and who benefits from the gains.

Where AI can widen inequality

AI increases inequality when:

Productivity gains accrue mostly to asset owners and a small pool of high‑skill workers
Low‑income communities face more automated surveillance and fewer real services
Countries without capital or infrastructure are stuck buying AI from those that do

You already see hints of this: powerful AI backed by huge compute budgets in a few countries, while others struggle with basic grid reliability. The same pattern shows up inside countries too—AI deployed to optimize advertising yields for tech giants, but not to improve public services.

Where AI can support a just, green transition

If used intentionally, AI can:

Lower the cost of clean energy by improving forecasting, dispatch, and maintenance
Upgrade public services through better planning, routing, and targeting of interventions
Empower communities with tools that translate complex climate and regulatory data into understandable options

A practical rule I use: if an AI project doesn’t visibly improve outcomes for workers, customers, or communities—and only shows up in a shareholder deck—it’s probably reinforcing inequality.

For organizations in green technology, this is a chance to lead by example:

Include distributional impacts in your AI project design: who benefits, who bears the risk?
Offer training pathways for your existing workforce to move into higher‑value roles around AI.
Partner with cities, utilities, and NGOs to deploy AI where it helps the most vulnerable adapt to climate stress.

A Practical AI Playbook For Green‑Tech Leaders

To pull this together, here’s a concise playbook for using AI in a way that makes economic and environmental sense.

1. Start with material problems, not features

Don’t ask “What can we use AI for?” Ask:

Where are we wasting the most energy or materials?
Where do we have recurring delays, failures, or bottlenecks?
Where are compliance, reporting, or stakeholder communication burdens heaviest?

If a problem isn’t material to your P&L or your emissions, it’s not your first AI project.

2. Choose “green‑ROI” projects first

Prioritize AI use cases where financial ROI and climate ROI move together, such as:

Reducing unplanned downtime in clean‑energy assets
Optimizing process parameters to cut fuel and waste
Automating accurate, auditable emissions tracking for customers

These are the projects that not only pay for themselves but also make your climate story stronger.

3. Design for human oversight and accountability

Ryan Bearden’s line captures it well:

“AI is a very powerful tool—it’s a hammer and that doesn’t mean everything is a nail.”

Treat AI as a tool that:

Proposes options, doesn’t mandate them
Surfaces anomalies, doesn’t silently “fix” them
Assists experts, doesn’t try to replace all of them

Codify this with clear AI use policies, review checkpoints, and domain experts who own the final decisions.

4. Track three dashboards, not one

If you’re serious about using AI responsibly in a green‑tech context, measure:

Business impact: revenue, cost, margins
Climate impact: emissions avoided, energy saved, resource efficiency
Trust and safety: errors caught, user complaints, regulatory issues

You want upward trends on (1) and (2) without degrading (3).

AI, The Economy, And Your Next Move

AI spending will keep rising. Slop content will keep clogging feeds. Some firms will overshoot and spend their way into a mess. Others will quietly use AI to run cleaner operations, smarter grids, and more resilient supply chains.

The difference won’t be who has access to the biggest model. It’ll be who picks precise problems, respects constraints, and insists on real outcomes—financial, social, and environmental.

If you’re building or investing in green technology, now’s the moment to decide which camp you want to be in. Treat AI as another noisy trend, or treat it as infrastructure you design thoughtfully—aligned with the economy you actually want to live in.

The next wave of leaders will be the ones who can look at an AI proposal and ask, with a straight face: Does this create durable value without burning the planet or our credibility? And if the answer is no, they’ll walk away.