As the EU invests in AI gigafactories, France is confronting the land, water and energy behind digital growth. The challenge is to use AI where it creates genuine value without treating natural resources as unlimited.

On 10 July 2026, the Grenoble Administrative Court suspended the building permit for an artificial intelligence computer centre near Valence.

The project would require more than 60 MW of electrical power at full capacity. The court found that an environmental impact assessment should have been carried out and identified serious doubts about whether the installation complied with local planning rules. The decision is temporary, pending a final ruling. [1]

Twelve days later, ADEME published a position paper on the environmental effects of generative and agentic AI. It highlighted rising demand for data centres, energy, hardware, land and water, alongside the difficulty of measuring these impacts, given that leading providers disclose so little about their models and infrastructure. [2]

Then, on 30 July, the European Union launched a call for up to seven AI gigafactories. The programme could receive up to €10 billion in European and national public funding and is expected to mobilise at least €20 billion in private investment. Its purpose is to strengthen Europe’s computing capacity and technological sovereignty. [3]

Together, these decisions bring a less visible part of AI growth into focus. Data centres are built in real territories, where land, electricity and water are shared with communities and ecosystems.

Europe has legitimate reasons to seek technological sovereignty, but forecasts of future demand should not be enough on their own to justify additional infrastructure. They should open a wider discussion about which uses create genuine value, where new capacity can be built responsibly and what environmental limits should apply.

Europe’s AI ambition is becoming physical

Technological sovereignty is usually discussed in terms of model ownership, data residency, security, and regulation.

• Can European organisations rely on foreign providers for critical infrastructure?
• Where is their data processed?
• Which jurisdiction controls the service?
• Can European startups compete without depending entirely on American hyperscalers?

These concerns are legitimate, but sovereignty also has a physical layer that receives far less attention.

Advanced AI requires computing capacity. That capacity depends on processors, servers, electricity, cooling systems, suitable land and network connections.

The EU’s gigafactory programme is designed to expand this foundation. The selected sites will combine advanced processors, cloud technologies, high-speed connectivity and energy-efficient data centres. They will support the training, fine-tuning and inference of advanced models for startups, SMEs, researchers, industry and public authorities. [3]

Companies cannot build competitive AI products without access to infrastructure, but bringing more computing capacity into Europe also brings its constraints closer to European territories.

Why France can still be a strategic location

There is also a legitimate environmental argument for locating some capacity in France. RTE notes that French electricity production is more than 95% decarbonised, meaning that workloads hosted in France may have a lower operational carbon footprint than equivalent activity powered by more carbon-intensive grids. The distinction, however, is whether new French capacity replaces activity elsewhere or simply adds to total global demand. [16]

The sovereignty question therefore becomes more demanding: Can Europe expand AI capacity without treating energy, water and land as invisible inputs?

The answer will shape more than environmental policy. It will influence where infrastructure can be built, how quickly capacity becomes available, what providers charge and which companies can afford to compete.

The territorial cost of computing capacity

Land cannot be treated as a neutral surface available for digital expansion. The Office français de la biodiversité notes that soils host close to 60% of terrestrial biodiversity and that artificialisation damages their biological, hydrological and climatic functions, while destroying and fragmenting habitats.

New infrastructure should therefore prioritise existing industrial and already artificialised sites before consuming natural, agricultural or forest land. [9]

Water reveals the measurement gap

AI’s water footprint has become one of the most visible parts of the infrastructure debate.

Data centres may use water directly for cooling. Water is also involved indirectly through electricity generation and the manufacture of processors, servers and other equipment.

But the numbers are easy to misuse because they often measure different things. These categories are not interchangeable:

• Water withdrawn from a local supply
• Water consumed and not returned to the same system
• Potable water used for cooling
• Indirect water associated with electricity production
• Training or inference
• Operational use or the full lifecycle

ARCEP reported that the French data centres covered by its environmental survey withdrew 681,000 cubic metres of water in 2023, primarily drinking water. That represented a 19% annual increase, although ARCEP notes that the total remains modest compared with withdrawals for other activities. [4]

The French competition authority estimates that the electricity consumed by data centres is associated with more than 5.2 million cubic metres of indirect water withdrawals each year. [5]

National averages can hide local pressure

National totals can appear modest while local pressure remains significant. Water availability varies by season and river basin, and withdrawals during dry periods occur when rivers, wetlands, soils and vegetation may already be under stress. Water scarcity affected 28% of EU territory and 32% of its population during at least one season in 2023. [10]

Data centres are not currently France’s largest water or electricity users. ARCEP describes their direct water withdrawals as modest compared with other activities, while RTE estimates that data centres account for around 2% of French electricity consumption today. The concern is therefore not that they represent the country’s dominant environmental pressure, but that their resource demand is rising quickly, new facilities are becoming considerably larger and local impacts can be concentrated in particular territories and river basins. [4][16]

Why AI-specific figures remain unavailable

The two figures describe different parts of the system. More importantly, neither represents AI alone.

Data centres also support cloud services, websites, financial systems, streaming, storage and conventional enterprise software. Public facility-level reporting cannot currently isolate the water used by one model, product or marketing workflow.

This is why simple statements such as “AI consumed this much water in France” are unreliable.

Why litres per prompt can mislead

Turning an AI interaction into a bottle or glass of water makes an invisible system easier to imagine.

It can also create false precision.

The footprint of a response depends on factors including:

• The model and its size
• Length and complexity of the task
• Number of model calls
• Hardware
• Data-centre location
• Cooling system
• Electricity mix
• Boundaries of the assessment

Mistral offers one of the few public, model-specific examples.

Its lifecycle assessment estimates that a 400-token response from Le Chat, excluding the user’s device, is associated with 1.14 grams of CO₂ equivalent and 45 millilitres of water consumption. [6]

The figure is meaningful within the specific model, infrastructure and methodology assessed by Mistral, but it cannot be generalised to ChatGPT, Claude, Gemini or every Le Chat interaction.

Mistral also acknowledges that environmental accounting for large language models remains incomplete. Hardware data is limited, methodologies are not yet fully standardised, and providers do not all measure the same boundaries.

Without context, environmental efficiency risks becoming another vague product badge.

Growth teams influence which demand gets scaled

Growth teams may not control data-centre cooling or processor manufacturing, but their decisions influence the demand placed on that infrastructure.

They help decide:

• Which tools enter the stack
• Which processes are automated
• How many model calls a workflow makes
• Whether every visitor receives live personalisation
• How much content is generated
• How widely automated outreach is deployed
• Whether complex models are used for simple tasks
• Whether an underperforming experiment continues to run

These choices affect both commercial efficiency and infrastructure demand.

The French competition authority estimates that electricity represents approximately 30% to 50% of a data centre’s operating costs. It also argues that AI frugality could become a competitive factor by enabling providers, including smaller companies, to offer efficient services at lower cost. [5]

AI changes the economics of software usage

Once built, a conventional software feature may cost very little each time it is used. An AI feature creates variable costs whenever a model is called, particularly when it uses a large model, generates rich media or chains several steps together.

Growth teams already decide which activities receive budget, automation and scale. That same discipline can reduce unnecessary ecological demand by limiting duplicated outputs, excessive model calls, real-time personalisation with no proven benefit and large models used for simple tasks.

Commercial efficiency still matters, but it should support a wider objective: creating useful outcomes without treating infrastructure as limitless.

When efficiency increases consumption

Generative AI has lowered the time and monetary cost of producing many marketing outputs.

A team can create more campaign concepts, email variations, advertisements and landing-page drafts than it could produce manually.

This can improve experimentation and execution, but it can also encourage teams to scale output before proving its value.

ADEME specifically warns that AI assessments must account for indirect effects, including rebound effects. When a process becomes more efficient and accessible, total usage may rise enough to offset part of the original saving. [2]

Growth has seen this pattern before: more advertising inventory did not guarantee better acquisition, automation did not necessarily improve nurturing, and a larger volume of content did not automatically create authority or organic demand. AI can amplify the same mistakes at much greater speed.

Content

Producing 100 articles instead of ten is not a growth result.

If the additional content repeats existing ideas, competes for the same search intent or attracts irrelevant traffic, production has scaled while demand has not.

Outbound

AI can research accounts and generate personalised messages at considerable volume.

If targeting is poor, it simply makes irrelevant outreach look more polished. The business creates more activity, model usage and reputational risk without producing more qualified conversations.

Agents

One customer request may trigger planning, retrieval, reasoning, verification and generation across several model calls.

What matters is not the number of actions performed, but the proportion of tasks completed correctly and with proportionate resources.

When production becomes cheaper, the risk is not simply that companies create more. It is that they create more without becoming more useful.

Efficiency needs an ecological boundary

Improving energy efficiency per request will not be enough if the total number of requests, features and facilities continues to rise. A more efficient system can still increase overall pressure when it is deployed everywhere, including where it adds little value.

Growth teams therefore need to assess necessity before optimisation. Before asking how cheaply or efficiently a workflow can run, they should ask whether it needs AI, whether a simpler system can perform the task, and whether the outcome justifies the additional demand.

From cost per token to cost per useful outcome

Tokens are valuable for technical monitoring and billing.

A cheaper model call is not automatically a better decision if the result requires extensive correction, creates a poor customer experience or fails to influence revenue.

Commercial cost per useful outcome

Cost per Useful Outcome offers a more useful starting point by connecting the full operational cost of a workflow to a verified result. The principle is not new. It applies familiar unit-economics thinking to AI workflows, while tracking resource intensity alongside financial and operational costs.

It can improve commercial discipline, but it should sit within clear ecological limits. A workflow may perform well financially and still be unacceptable if it depends on avoidable infrastructure, unnecessary land artificialisation or additional withdrawals in a water-stressed territory.

The calculation should include: Platform and API costs + implementation + human review + correction and failure costs

divided by: Verified successful outcomes

The successful outcome depends on the use case. It could be:

• Qualified meeting
• Incremental conversion
• Activated user
• Retained customer
• Accurately resolved request
• Completed internal task
• Creative asset that improved campaign performance

The outcome must be defined before the workflow is expanded.

Resource intensity per useful outcome

Environmental and technical indicators should be tracked alongside the commercial calculation, not mixed into the same unit.

Depending on the data available, these could include:

• Model calls per successful outcome
• Tokens or compute per outcome
• Energy per outcome
• Water estimates supplied by the provider
• Percentage of work routed through smaller models
• Human corrections per outcome
• Quality of the vendor’s environmental disclosure

The objective is to make a better commercial decision about whether each use case merits continued investment.

What resource-aware growth looks like

Resource-aware growth requires the same discipline already expected in acquisition, experimentation and revenue operations.

Begin with a defined problem

“We need an AI agent” is not a business case.

“We need to reduce the time Sales spends researching accounts without lowering research quality” is one.

A clear problem makes it possible to compare the AI-supported workflow with the existing process.

Establish a baseline

Before implementation, measure:

• Time required
• Current cost
• Output quality
• Error rate
• Conversion or completion rate

Without a baseline, adoption is easily confused with improvement.

Prove incrementality

Use control groups, phased deployment or before-and-after analysis to determine whether AI changed the outcome.

A generated landing-page variation that performs no better than the original has not created incremental growth.

Match the model to the task

Not every activity requires the largest available model.

Classification, extraction, routing and structured rewriting may be handled effectively by smaller or specialised systems.

A more resource-intensive model is justified when it materially improves accuracy, commercial performance or risk management.

Build stopping rules

AI workflows can expand automatically, which makes operational limits essential.

These may include:

• Usage budgets
• Maximum calls per task
• Model-routing rules
• Confidence thresholds
• Human review requirements
• Content-volume limits
• Performance thresholds below which an experiment stops

Ask vendors better questions

Buyers may not receive complete environmental data today, but they can still ask:

• Where are workloads processed?
• Can the model or processing region be selected?
• Are smaller models available?
• Does the system use model routing?
• Is usage visible by workflow?
• Has the provider published lifecycle or inference data?
• What does its methodology include?
• Which impacts remain estimated?

The ability to answer clearly can become part of vendor trust.

What should Europe require before scaling further?

The EU already requires data centres with installed IT power demand of at least 500 kW to report energy and water indicators to a European database.

The framework covers total energy and water use, Power Usage Effectiveness, Water Usage Effectiveness and waste-heat reuse. Public information is currently published at aggregated Member State and EU levels. [7][8]

The Commission is also preparing a wider Data Centre Energy Efficiency Package, including a common European rating scheme and future minimum performance standards. [7]

This does not yet provide workload-level transparency. A buyer cannot use the database to identify the water footprint of its chatbot or campaign.

Energy and water efficiency are moving from an internal infrastructure concern towards reporting, procurement and competition.

From reporting to responsible infrastructure decisions

Europe now has an opportunity to define the conditions under which additional capacity is acceptable. Rather than treating every forecast of AI demand as a reason to build, policymakers, providers and buyers should ask:

• Can an existing industrial or artificialised site be reused?
• Is the proposed location already exposed to water stress?
• Has a complete environmental assessment been carried out?
• Would the facility replace existing foreign capacity or simply add to total demand?
• How will habitats, soils and ecological corridors be protected?
• Could smaller models or fewer calls deliver the same outcome?
• Can users disable generative features they do not need?
• Are energy, water, hardware and lifecycle impacts reported transparently?

A credible provider should be able to explain which models it uses, where workloads run, how tasks are routed, what its assessment includes and how efficiency changes across use cases.

The French competition authority has already identified frugality as a potential competitive parameter that can influence price, quality and innovation. [5]

These conditions could help Europe develop an AI model shaped by greater transparency, resource restraint and territorial responsibility, rather than measuring success only through capacity.

FAQ

Does every AI prompt consume the same amount of water?

No. The result depends on the model, task, hardware, location, cooling technology, electricity source and assessment methodology. A number calculated for one provider should not be generalised to all AI interactions.

Did the French court suspend the Alixan project because of water consumption?

No. The court found that the project required an environmental impact assessment and identified serious doubts about compliance with planning rules. The decision is an interim suspension, not a final cancellation. [1]

Are France’s data-centre water figures specific to AI?

No. Data centres support many digital activities. Current public reporting does not isolate AI workloads from the rest of a facility’s operations. [4][5]

Should companies use less AI?

The better goal is selective and measurable use. Companies should identify where AI creates meaningful value, choose an appropriate model and stop expanding workflows that produce activity without useful results.

Why should growth teams care?

Growth teams influence which workflows are deployed, how frequently models are called and whether those systems continue to scale. These decisions affect margins, customer experience and infrastructure demand.

Can efficiency become a competitive advantage for European AI?

Potentially. Efficient models, regional infrastructure, transparent methodologies and auditable reporting can support lower costs, stronger procurement cases and greater trust. European origin alone is not enough.

Conclusion

July 2026 has made the tension surrounding Europe’s AI ambitions unusually visible. A French court suspended an AI infrastructure permit because the required environmental assessment had not been carried out, ADEME called for greater transparency and lifecycle measurement, and the EU launched its call for up to seven AI gigafactories, backed by up to €10 billion in potential public funding and at least €20 billion in expected private investment. [1][2][3]

These developments are unfolding within a wider ecological reality. Earth Overshoot Day fell on 30 July, while France was experiencing its third official heatwave of the summer and nearly 495,000 hectares had been mapped as burned across the EU in 2026. These events were not caused by AI infrastructure, but they show how heavily land, water and energy systems are already being tested. Additional data-centre capacity can increase that pressure, particularly in water-scarce regions and during periods of extreme heat. [11][12][13]

AI can support research, medicine, accessibility, conservation and everyday work. Its usefulness should encourage more careful decisions about which applications deserve to scale and what each new facility asks of land, water and energy.

Water must remain a public and ecological priority

Water is both a human right and a shared ecological resource. French law gives priority to essential human needs while requiring the protection of aquatic ecosystems and natural water systems. These principles should define the conditions under which industrial and digital uses are authorised, particularly during droughts and heatwaves. [14][15]

Europe still has an opportunity to regulate before rising demand becomes difficult to challenge. This means careful siting, full environmental assessments, transparent lifecycle information, smaller or specialised models where they are sufficient, and the ability to refuse projects whose public value does not justify their local ecological cost.

Where growth teams can make a difference

Growth teams influence which applications become normal, which experiments continue and which automated processes multiply. Measuring useful outcomes can help distinguish applications that genuinely improve people’s work or lives from those that mainly produce more volume.

AI is already part of our economic and social systems, which makes strong regulation essential. European leadership should be judged not only by the computing capacity it builds, but by the care with which it decides what deserves scarce land, water and energy.

💌 If you want more essays on ethical growth, trust-led marketing and practical B2B growth systems, you can join Conscious Growth Dispatch here.

Sources

[1] Grenoble Administrative Court, Suspension du projet de Datacenter à Alixan, 10 July 2026. The court temporarily suspended the permit for an AI computer centre requiring more than 60 MW at full capacity.

[2] ADEME, IA générative, comment quantifier les impacts ?, 22 July 2026. ADEME reviews the direct and indirect environmental effects of generative and agentic AI and recommends standardised measurement, greater transparency and attention to rebound effects.

[3] European Commission Representation in France, L’UE lance un appel d’offres pour les giga-fabriques d’IA, 30 July 2026. The call supports up to seven gigafactories with up to €10 billion in public funding and at least €20 billion in expected private investment.

[4] ARCEP, Enquête annuelle Pour un numérique soutenable, 17 April 2025. ARCEP reports that the French data centres covered by its survey withdrew 681,000 cubic metres of water in 2023, primarily drinking water.

[5] Autorité de la concurrence, Étude sur les enjeux concurrentiels liés à l’impact énergétique et environnemental de l’intelligence artificielle, 17 December 2025. The study covers energy costs, direct and indirect water use, environmental transparency and AI frugality as a competitive parameter.

[6] Mistral AI, Our contribution to a global environmental standard for AI, 22 July 2025. Mistral reports the lifecycle footprint of Mistral Large 2 and the estimated marginal impact of a 400-token Le Chat response.

[7] European Commission, Energy performance of data centres, accessed 31 July 2026. The page explains the European reporting database, its water and energy indicators and the planned Data Centre Energy Efficiency Package.

[8] European Union, Commission Delegated Regulation (EU) 2024/1364, 14 March 2024. The regulation establishes reporting requirements and sustainability indicators for data centres with installed IT power demand of at least 500 kW.

[9] Office français de la biodiversité, Lutter contre l’artificialisation des sols. The OFB explains that soils host close to 60% of terrestrial biodiversity and that artificialisation damages their biological, hydrological and climatic functions.

[10] European Environment Agency, Water scarcity conditions in Europe, updated 2026. Water scarcity affected 28% of EU territory and 32% of its population during at least one season in 2023.

[11] Global Footprint Network, Earth Overshoot Day 2026 falls on July 30, 5 June 2026. The calculation estimates that humanity is using ecological resources and services 73% faster than Earth can regenerate them, equivalent to 1.73 Earths.

[12] Météo-France, Orages et fortes chaleurs à l’est, 30 July 2026. Météo-France confirms that the heatwave beginning on 28 July was the third national heatwave of summer 2026. It followed two earlier summer heatwaves and an exceptionally early May heat episode that did not meet the national heatwave threshold.

[13] European Forest Fire Information System, EFFIS Statistics Portal, accessed 31 July 2026. EFFIS had mapped approximately 495,000 hectares burned across EU countries during 2026, including 91,057 hectares in France. The figures are updated regularly and principally cover fires of approximately 30 hectares or more.

[14] UN-Water, Human Rights to Water and Sanitation. The United Nations recognises access to sufficient, safe, acceptable, physically accessible and affordable water for personal and domestic use as a human right.

[15] Légifrance, French Environmental Code, Articles L210-1 and L211-1. French law defines water as part of the nation’s common heritage, recognises access to drinking water for essential uses and requires the preservation of aquatic ecosystems, wetlands and natural balances.

[16] RTE, Les data centers en chiffres clés, 2026. RTE estimates that data centres currently consume around 10 TWh, approximately 2% of France’s annual electricity consumption. This could rise to between 23 and 28 TWh, around 4% of national consumption, by 2035.

The post The Hidden Cost of AI Growth appeared first on SAMIN GHIASI | GROWTH MARKETING CONSULTANT | FR/EN.

What B2B marketers can learn from NOUS anti-gaspi, Too Good To Go and Back Market about positioning, proof and conversion.

There is something quietly interesting about walking into a shop that sells products other retailers did not want to keep.

A packet may have a short date, a fruit may look less polished, a product might come from surplus stock, or a phone may have had a previous life before being refurbished and sold again.

The product may still have real value, while asking the customer for a little more trust before they choose it.

• Is this still good?
• Is it safe?
• Is the lower price hiding a weaker experience?
• Am I making a smart choice, or accepting a compromise?

That question is what makes anti-waste commerce such a strong marketing case study.

“Brands like NOUS anti-gaspi, Too Good To Go and Back Market sell products that need trust before they can be chosen. Their growth depends on making products with an initial trust gap feel understandable, credible and worth choosing.”

In B2B marketing, buyers also make decisions under uncertainty. They may be interested in a product, but still unsure about implementation, risk, quality, data, support, compliance or internal buy-in. In categories like this, conversion is not just persuasion. It is risk reduction.

The value of “imperfect” depends on how it is framed

A short-dated product is not automatically a bad product. A wonky vegetable is not less nutritious. A surplus item is not less useful. A refurbished phone is not necessarily less reliable.

The meaning attached to the product often matters as much as the product itself.

Modern commerce has spent years making quality feel synonymous with freshness, abundance and newness. A full shelf feels reassuring, a pristine product feels safer, and a new device often feels easier to trust than one with a previous life.

Positioning matters here because it shapes whether the customer sees imperfection as a risk or as part of the value.

Many B2B products also carry a perceived flaw. They can be complex, technical, regulated, expensive, hard to implement or difficult to compare. Weak marketing tries to smooth over that friction. Stronger marketing explains why the friction exists and what value it creates.

Anti-waste commerce has grown because it solves several tensions at once

Food waste is not a small problem. The European Commission states that more than 58 million tonnes of food waste are generated annually in the EU, with an associated market value estimated at €132 billion. [1]

In France, anti-waste and food destocking have become a more visible commercial category. Xerfi estimated that the turnover of food destocking and anti-waste specialists reached €520 million in France in 2024. [2]

Those figures explain the economic relevance of the category. The buying decision itself is more layered.

The best models make the buying decision feel both rational and emotionally acceptable. The customer can say: “I saved money, avoided waste, and still got something useful.”

It also connects with something I explored in From Scarcity to Stewardship: The New Psychology of Conscious Consumers. Responsible consumption is no longer only about values. It is also about proof, affordability, usefulness and trust.

Awareness does not remove hesitation

Most people already understand that food waste is a problem, and many consumers know that buying refurbished can reduce environmental impact. Awareness creates interest; confidence turns that interest into behaviour.

“At the moment of purchase, the buyer needs more than awareness. They need to feel that the product is safe, the price reflects real value and the sustainability message is not hiding a weaker experience.”

A similar pattern appears in B2B SaaS, fintech and marketplaces.

Buyers may understand the problem and agree with your category narrative, but they still need practical reassurance: will the product work, can the team deliver, is implementation manageable, is the data reliable, and can the decision be defended internally?

That is why the most interesting anti-waste brands do more than create demand. They design the buying experience around the hesitation itself.

NOUS anti-gaspi makes anti-waste feel like normal shopping

NOUS anti-gaspi is interesting because it brings anti-waste into a familiar retail format.

From the outside, the model is simple: sell everyday products that would otherwise risk being wasted, often at lower prices, through physical stores. The marketing challenge is more subtle. NOUS anti-gaspi sells discounted products, but its real marketing challenge is to normalise a different way of shopping.

For the model to work, customers need to feel that buying anti-waste products is not strange, risky or second best. It has to feel like a practical grocery choice.

The brand connects several layers of value at once: price, everyday usefulness, waste reduction, local shopping habits and a familiar retail experience.

According to Plan Bio, NOUS anti-gaspi raised €3 million in 2024 and reported 28 stores and €41 million in turnover for 2023. [3] In January 2026, Linéaires reported that the brand planned to open eight new stores in 2026, including a Paris opening on Boulevard Richard Lenoir, with further development planned in Paris, Bordeaux and Lyon. [4]

The exact figures will evolve, but the direction is clear: anti-waste is no longer only a niche activist behaviour. It is becoming a more structured retail proposition.

For B2B marketers, the useful lesson is about adoption. A new behaviour becomes easier to accept when it is embedded in a format the buyer already understands.

If your product asks buyers to change how they work, report, buy, analyse or decide, the experience cannot feel completely foreign. You need to make the new behaviour legible through familiar cues: clear categories, simple next steps, transparent pricing, recognisable workflows, proof points and onboarding that reduces uncertainty.

Too Good To Go makes waste reduction feel small enough to act on

Instead of putting anti-waste into a physical grocery format, it turns surplus food into a small, repeatable action. The customer opens the app, finds a nearby partner, reserves a discounted surprise bag and collects it during a specific time window.

The surprise element could have been a barrier. People do not always know exactly what they will get, and that uncertainty creates friction. Too Good To Go gives that uncertainty a structure.

Too Good To Go says it has grown to more than 120 million registered users and 180,000 active business partners and has helped save more than 500 million meals from being wasted. [5] As this is company-reported data, it should be treated as self-reported impact rather than independent verification. Still, it shows the scale of the behaviour the company is trying to build.

That is why the first action matters. It gives the customer a way to test the promise without needing to believe the whole story upfront.

Back Market shows why proof has to appear before conversion

Refurbished electronics carry one of the clearest trust problems in circular commerce.

“The value proposition is attractive: cheaper devices, less waste, less pressure to buy new. But the buyer may still worry about battery life, hidden defects, seller quality, warranty, support and what happens if the device fails.”

Back Market has to make refurbished feel safe enough to choose, with sustainability as part of the value rather than the whole argument.

The company does this through visible trust mechanisms. Its own site highlights refurbishment standards, up to 100-point quality inspections, free 30-day returns and a 1-year warranty. [6]

In 2024, The Guardian reported that Back Market had expanded to 18 countries, raised more than $1 billion and worked with around 1,700 traders and refurbishers. The same article quoted CEO Thibaud Hug de Larauze describing the “trust gap” between refurbished products and new devices. [7]

A trust gap closes when quality control, guarantees and seller standards become visible before the customer feels too much risk.

Many companies have strong operational proof, but they hide it too deep in the journey. The best evidence sits in sales decks, onboarding calls, legal documents, security questionnaires, help centres or customer success processes. By the time the buyer sees it, they may already have hesitated.

When operations answer a real objection, they should be part of the story. In B2B, that proof can come from onboarding processes, data governance, customer support, partner vetting, security standards or implementation frameworks.

Before the buyer says yes

Across these examples, the important work happens before the customer says yes.

The brand changes how the buyer interprets the perceived flaw, then adds enough proof to make the decision feel reasonable.

The pattern is simple: identify the hesitation, make the trade-off clear, show proof early, and make the first step feel low-risk. Anti-waste brands do this because they have to. B2B marketers should do it because buyers are already asking these questions before they convert.

A lot of marketing becomes too thin at exactly this stage. It jumps from claim to CTA without doing the confidence-building work in between.

A company may say it is more sustainable, more efficient, AI-powered, future-facing or built for growth, while the buyer is still wondering whether the claim is credible, what will actually change, how difficult adoption will be and whether the decision can be defended internally.

The experience still has to hold

This is also where the limits of anti-waste commerce become important. These models can easily become romantic stories about better consumption, but they are operationally difficult businesses.

Supply can be unpredictable, margins can be thin, quality may vary, and operations are often complex. There is also a deeper paradox: if society becomes better at preventing waste, businesses built around surplus need to keep evolving.

Poor quality, unclear impact or inflated claims can make consumers question whether the model truly reduces waste or whether it simply turns surplus into another polished commercial story.

Ethical intent may create the first reason to care, but repeat behaviour still depends on the experience. A weak product, a disappointing basket or an unreliable warranty quickly remind customers that mission alone cannot carry the relationship.

The brands that hold up over time combine mission with practical value. Price, convenience, reassurance, local access, guarantees and repeatable behaviour all matter.

What B2B marketers can take from this

Anti-waste commerce gives B2B marketers a useful logic: study the hesitation before trying to accelerate the conversion. A SaaS company does not need to behave like a grocery store, and a fintech platform does not need to imitate an anti-waste app.

Make the trade-off visible

B2B buyers need to understand what they are choosing.

A clear trade-off is easier to trust than a polished promise. Setup time, specialisation, structured workflows or higher pricing do not have to weaken the offer, as long as the buyer understands what each constraint protects, clarifies or helps avoid.

Turn abstract claims into customer jobs

A lot of B2B messaging starts too high up. Words like growth, innovation, efficiency, transformation and AI-powered performance may be relevant, but they often fail to explain what the buyer can actually do next.

Move proof closer to the first moment of doubt

If a buyer has to book a demo before they understand your methodology, security posture, onboarding process or customer results, the journey is asking for trust too early.

Move proof into the places where hesitation already appears: landing pages, comparison pages, FAQs, pricing pages, case studies, product explainers, demo follow-ups, nurture emails and onboarding content.

This is where anti-waste commerce connects with circular growth loops: the strongest models do not simply acquire users. They create repeat behaviours around value, trust and participation.

Growth is what happens when the customer has enough confidence to come back.

Look for trust leakage before adding more traffic

Trust leakage happens when a buyer understands the offer but still does not feel safe enough to move forward. More traffic will not fix that. The friction sits inside the proof, the reassurance, the onboarding story or the perceived risk.

This is where B2B marketers should look before assuming the problem is demand.

This is a more useful way to diagnose growth in trust-sensitive categories. Not every conversion problem is a CTA problem. Sometimes the buyer simply does not have enough confidence yet.

FAQ

What is anti-waste commerce?

Anti-waste commerce is the sale, redistribution or revalorisation of products that are still usable but at risk of being wasted, discarded or undervalued. This can include surplus food, short-date groceries, imperfect products, unsold meals, refurbished electronics and other circular commerce models.

Why are anti-waste brands relevant to B2B marketing?

Because they sell under imperfect conditions. Their products often carry a perceived flaw, which makes them useful examples for B2B categories where trust, risk and proof shape conversion.

What can SaaS companies learn from Too Good To Go or Back Market?

SaaS companies can learn how to reduce perceived risk before conversion. Too Good To Go turns a big problem into a simple, repeatable action. Back Market makes quality control and reassurance visible. Both show that trust should be designed into the customer journey, not added at the end.

Is anti-waste commerce only about sustainability?

Sustainability sits alongside price, convenience, quality reassurance, trust signals and repeat behaviour.

How can B2B marketers reduce perceived risk before conversion?

By making proof visible earlier: customer evidence, methodology, security information, onboarding clarity, transparent FAQs and operational credibility.

Conclusion

Anti-waste commerce is interesting because it begins with products that can raise doubts, then builds a buying experience around those doubts.

NOUS anti-gaspi does it through familiarity. Too Good To Go does it by making uncertainty feel small and actionable. Back Market does it by making proof visible before the customer has to take the risk.

For B2B marketers, the useful lesson is to notice where buyers hesitate before trying to accelerate the conversion. Sometimes the problem is not demand, positioning or traffic in isolation. Sometimes the buyer simply does not feel safe enough to move.

In anti-waste commerce and B2B growth, trust is part of the system that makes conversion possible.

💌 If you want more essays on ethical growth, trust-led marketing and practical B2B growth systems, you can join Conscious Growth Dispatch here.

Sources

[1] European Commission, Food waste, accessed May 2026. The Commission states that over 58 million tonnes of food waste are generated annually in the EU, with an associated market value estimated at €132 billion.

[2] Xerfi, Le marché du déstockage et de l’anti-gaspi alimentaire, published 27 June 2025. Xerfi estimates that the turnover of food destocking and anti-waste specialists reached €520 million in France in 2024.

[3] Plan Bio, L’enseigne NOUS anti-gaspi lève trois millions d’euros et ouvrira 3 magasins d’ici fin 2024, published 2024. The article reports NOUS anti-gaspi’s €3 million fundraising, 28 stores and €41 million turnover in 2023.

[4] Linéaires, NOUS anti-gaspi ouvrira 8 magasins en 2026, published 15 January 2026. The article reports the brand’s plan to open eight new stores in 2026, including a Paris opening on Boulevard Richard Lenoir.

[5] Too Good To Go, Too Good To Go Accelerates U.S. Expansion, published 27 August 2025. The company states that it has 120 million registered users, 180,000 active partners across 19 countries and more than 500 million meals saved from waste.

[6] Back Market, Back Market: refurbished tech that’s better for the planet, accessed May 2026. Back Market highlights refurbishment standards, up to 100-point quality inspection, 30-day returns and a 1-year warranty.

[7] The Guardian, “It should be a right to fix your phone”: the boss of booming secondhand tech firm Back Market, published 11 June 2024. The article discusses Back Market’s international growth, refurbisher network and the trust gap around refurbished electronics.

The post How Anti-Waste Brands Make Imperfect Products Easier to Trust appeared first on SAMIN GHIASI | GROWTH MARKETING CONSULTANT | FR/EN.

For years, the direction seemed clear. Sustainable finance was moving towards greater disclosure, more ESG products and stronger climate reporting.

Companies, investors and financial institutions were being pushed to prove that sustainability was not just a brand narrative, but part of how capital moved, risk was priced, and decisions were made.

Now the mood is different.

Europe is trying to simplify parts of its sustainability rulebook. The scope of corporate sustainability reporting has been narrowed, due diligence requirements have been reduced, and the Sustainable Finance Disclosure Regulation is being reworked after years of criticism that it created too much complexity for investors. [1][2]

Regulators have not stopped caring about greenwashing

The European Commission has acknowledged that SFDR Articles 8 and 9 were being used as de facto product categories, even though they were not designed to function as simple labels. In France, the AMF has updated its policy to align with ESMA guidelines on ESG fund names, with the explicit aim of preventing misleading sustainability communication. [3][4]

So the story is not that Europe has moved from strict to soft. The more interesting shift is that sustainability rules are being simplified while sustainability claims are being questioned more carefully.

For climate fintechs, carbon accounting platforms and B2B ESG software companies, this changes the growth problem. It is no longer enough to make sustainability sound desirable. Claims now have to be verifiable, understandable, proportionate and useful to real decisions.

In this article, sustainable fintech marketing means the way financial technology companies communicate products linked to climate data, ESG reporting, carbon accounting, responsible investment or transition finance.

In 2026, the strongest version of this marketing is not built around broad impact language. It is built around evidence: data sources, methodology, limits, customer proof and clear next steps.

The question is not only:

How do we make people care?

It is:

Can we make sustainability claims easier to trust?

That is a harder marketing problem, and a more useful one. In climate finance, trust does not come from saying “impact” more often. It comes from showing what the claim means, where the data comes from, what the method includes, and where the limits are.

The thesis: sustainable fintechs will not win 2026-27 by using louder ESG language. They will win by making their claims easier to verify, their data easier to trust, and their products easier to act on.

1. The ESG mood has changed

A few years ago, ESG language still carried commercial weight. Words like sustainable, green, responsible, transition, climate-positive and impact-led helped buyers make sense of a new market. They gave investors, customers and internal teams a quick way to understand what a product was trying to do.

That shorthand was useful until it became too easy to use.

“In parts of the ESG market, the vocabulary is now everywhere, but trust is weaker. Buyers have seen too many vague claims, too many polished impact pages and too many sustainability labels that do not clearly explain what sits behind them.”

This does not mean sustainability has stopped mattering. It means sustainability marketing has matured. The market is less impressed by the presence of ESG language alone and more interested in the evidence underneath it.

That creates a real challenge for fintech and climate-tech companies. Many were built around genuine problems: carbon accounting, sustainable investment, green banking, ESG reporting, supplier data, portfolio transparency, climate risk and regulatory readiness. But good intentions do not remove the need for evidence. In a market shaped by scrutiny, they increase it.

2. Europe is simplifying sustainability rules, but scrutiny is not disappearing

The current European context is easy to misread.

In February 2026, the Council of the European Union approved simplification measures for ESG reporting and due diligence. The CSRD scope is narrowed to companies with more than 1,000 employees and above €450 million in net annual turnover. The CSDDD scope is also narrowed, applying to larger companies with more than 5,000 employees and above €1.5 billion in net annual turnover. [1]

The logic is competitiveness. The EU wants to reduce the reporting burden, especially where smaller and mid-sized companies are being pulled into complex sustainability processes through supply chains, investors or customer requirements.

That argument deserves to be taken seriously. Complexity does not automatically create better accountability. In practice, it can push companies into expensive reporting exercises they barely understand, produce documents that are technically compliant but not decision-useful, and create space for vendors to sell fear instead of clarity.

For SMEs, this matters. A sustainability framework that feels impossible to apply will not create better climate action. It may simply create fatigue.

“But simplification also carries risk. If fewer companies are required to report, the market may receive less consistent data. If transition planning requirements are reduced, it may become harder to assess whether companies are taking credible action or simply telling a better story.“

This is the tension sustainable fintechs need to understand. Simplification can make sustainability more usable, but it can also make weak claims easier to hide. The marketing implication is not that regulation is lighter, so proof matters less. It is that credible voluntary proof becomes more valuable for the companies that still want to lead.

3. Why ESG labels are no longer enough

SFDR is one of the clearest examples of how climate vocabulary can become commercially powerful, then commercially risky.

The regulation was designed to improve transparency around sustainable investment products. But in practice, Articles 8 and 9 became shorthand. Funds were often described as “Article 8” or “Article 9” as if those categories were simple quality labels.

The European Commission has acknowledged this problem directly. In its SFDR review, it said that the use of Articles 8 and 9 as de facto product categories had created confusion and greenwashing concerns. [3]

This matters beyond asset management because it shows what happens when a technical framework becomes a marketing badge. At first, the label is useful: it gives buyers a shortcut, makes the product easier to understand and reduces friction in a complex market.

Then the shortcut becomes overused, and buyers start to wonder what the label really means. Regulators step in. Journalists investigate. NGOs challenge the claims. Genuine players are pulled into the same trust problem as weaker ones.

That is the risk for any sustainable fintech or climate platform. A label can open the door, but it cannot carry the whole trust burden.

Words like green, ESG, net zero, impact and sustainable can help people understand the category. But they cannot replace the harder work: methodology, data quality, limits, governance, customer proof and practical outcomes.

4. Why does France make the trust question sharper?

France is an especially useful market for this discussion because sustainable finance is both visible and heavily scrutinised.

The AMF has long taken a position on ESG communication. Its doctrine aims to prevent greenwashing and sets minimum standards for investment products distributed in France when they communicate on non-financial criteria in their names, legal documents and marketing materials.

In 2025, the AMF updated its policy after deciding to comply with ESMA guidelines on the names of funds using ESG or sustainability-related terms. [4]

This is not just a technical issue for asset managers. It reflects a broader market mood. Sustainability claims are no longer judged only by whether they sound good. They are judged by whether they are proportionate, understandable and supported.

That matters for fintechs because many of them already operate in risk-sensitive categories.

A CFO evaluating carbon accounting software is not only asking whether the platform looks modern. They are asking whether the data can be trusted, whether the numbers can be explained internally, whether finance, legal and sustainability teams can agree on them, and whether the tool will reduce complexity rather than create another dashboard nobody uses.

A consumer looking at a green neobank has a different version of the same question. Where does the money actually go? What is excluded? Who verifies the claim? Is the difference real, or is it simply better branding?

In both cases, the growth challenge is not only acquisition. It is confidence.

5. The real growth problem is confidence, not awareness

Green marketing still behaves as if the main problem is awareness. The market does need education, but awareness alone is not enough. Most companies now understand that sustainability, carbon data, ESG reporting and climate risk matter in some way. The harder question is what to do next.

This is where climate and ESG platforms face a difficult positioning problem.

Compliance, purpose, automation and urgency all have a role to play, but none of them is strong enough on its own. Compliance can explain why a company needs to act. Purpose can explain why the category matters. Automation can reduce friction. Urgency can move a project up the priority list.

But the buyer still needs something more basic: confidence that the product will help them make sense of the data, defend the numbers internally and decide what to do next.

The stronger positioning connects sustainability to usable decisions.

That difference matters. The first version sells sustainability as a virtue. The second sells sustainability as a decision system.

6. Greenly and proof-led growth

Greenly is an interesting case study because it operates inside this trust problem.

The company is not selling a simple consumer product. It operates in carbon accounting and climate management, where claims have to be backed by data, methodology and process. Greenly positions itself as an all-in-one climate solution for greenhouse gas disclosure, product carbon footprint and ESG compliance, and says it supports more than 3,500 clients. [5]

From a growth perspective, that is interesting because the product is not just software. It is a way to make an unclear sustainability problem more structured.

A company may know it needs to understand its emissions. But it may not know how to collect the data, how to estimate missing information, how to handle Scope 1, 2 and 3, how to work with suppliers, how to prepare for reporting, or how to turn the result into a credible action plan.

In a category like carbon accounting, the buyer is not only buying a tool. They are buying confidence that the numbers will be usable, that the process will be manageable, and that the output will not embarrass them in front of leadership, auditors, customers or regulators.

That makes Greenly a useful example of proof-led growth.

The strongest marketing assets in this category are not only ads, taglines or landing pages. They are things like methodology pages, customer stories, benchmark reports, explainers on carbon accounting, guides for different buyer roles, evidence of data quality, partner validation, onboarding journeys, product education, and clear limits around what the platform can and cannot prove.

This is where ethical growth marketing becomes practical: not softer marketing, but marketing designed to build trust without hiding the commercial goal.

7. The Proof Stack

Often, the proof exists. It is just scattered.

A company might have a methodology page, a customer case study, an impact report, a compliance guide, a product demo, a sales deck and a help centre. Each asset may be useful on its own, but the buyer still has to assemble the trust story alone.

That creates friction.

A useful way to think about this is The Proof Stack.

This structure is useful because it prevents vague messaging. It forces every sustainability claim to carry its own weight.

“Climate impact” is not enough. The buyer needs to understand what kind of impact is being claimed, how it is measured, what it is compared with, which assumptions are being used, who has verified it, and which decision it is meant to support.

“These questions may feel uncomfortable for marketers. But in sustainable finance, they are exactly the questions serious buyers are already asking. The task is not to avoid them. The task is to answer them before they become objections.”

This also connects to the wider ethical martech stack. The tools a company uses to collect, store, segment and activate sustainability data shape how credible its marketing can be.

Proof should not live in a PDF at the end of the funnel. It should shape the entire customer journey.

8. Why ethical marketing means fewer, better claims

There is a temptation in sustainability marketing to say too much.

There is always a temptation to turn every positive signal into a claim: every initiative into a campaign, every metric into a headline, every partnership into proof of impact, every customer story into a transformation narrative.

But when the market is sceptical, more claims do not always create more trust. Sometimes they create more doubt.

A stronger approach is to make fewer claims, but make them stronger.

This is especially important for fintechs and climate platforms because the buyer group is rarely simple. The CMO may like the mission. The CFO will ask about cost, risk and usefulness. Legal will ask about wording. Procurement will ask about vendor reliability. Sustainability teams will ask about methodology. Operations will ask whether the process is realistic.

That means sustainable fintech marketing cannot be written only for the believer. It has to survive the sceptic.

A good sustainability claim should still make sense when read by someone who does not want to be convinced. That is a useful standard because it pushes marketing away from purpose-washing and towards clarity.

It also protects the brand. Overclaiming may create short-term interest, but it damages trust when customers, regulators or NGOs start pulling on the thread. In this market, conservative wording can be a strategic advantage.

9. Why fear-based compliance marketing damages trust

There is another trap in this category: fear.

When regulation changes, some vendors turn every update into a panic campaign. A new deadline, a new obligation, a new risk, a new reason to book a demo.

This can work in the short term. But it is not a healthy growth strategy. It trains the market to associate sustainability with stress, bureaucracy and vendor opportunism. It also makes buyers defensive, especially when regulation is being simplified at the same time.

A better approach is to help buyers understand what actually applies to them, what may affect them indirectly, what is changing, what is still uncertain, and what they can prepare without overreacting.

“For example, a mid-market company may no longer be directly in scope for certain reporting obligations, but it may still face sustainability questions from enterprise customers, banks, investors or public procurement processes.“

That is a more honest and useful message than: “You must comply now or fall behind.”

It also opens a better commercial conversation because the value is not only compliance. It is readiness.

10. Why trust is built through lifecycle marketing

Trust rarely happens in one visit.

A buyer might first find the company through an article about reporting rules. Later, they compare platforms, look for methodology, ask a sustainability colleague, join a webinar, request a demo, involve finance, check data security and ask for references.

If marketing only optimises for the first conversion, it misses the part of the journey where trust is actually built.

This is where impact-focused fintechs need better lifecycle thinking. Not every lead needs the same content. A CFO may need a business case. A sustainability manager may need methodology depth. A procurement lead may need vendor reassurance. A founder may need a simple path to start. A large enterprise may need integration, governance and auditability. A smaller company may need simplicity and cost control.

This should shape segmentation, nurturing and sales enablement. The goal is not to bombard people with ESG content. The goal is to help each stakeholder move from doubt to clarity.

This is what growth looks like in a trust-sensitive category. Not just more leads, but better movement from uncertainty to confidence.

If serious buyers are dropping off because methodology, proof or risk answers are hard to find, the issue is not traffic. It is trust leakage, and it deserves the same scrutiny as any funnel waste audit.

11. What B2B fintech marketers should do now

For B2B marketers working in fintech, climate tech, ESG software or any trust-sensitive category, the next playbook needs more discipline. Not colder marketing. Not more corporate marketing. More precise marketing.

Replace vague ESG language with customer jobs

A lot of sustainability messaging still starts too high up: green finance, positive impact, ESG transformation, climate action. Those words are not useless. They just do not tell the buyer what the product helps them do.

A more useful approach is to translate sustainability into specific customer jobs:

Good positioning starts with the decision the buyer needs to make.

Make methodology visible earlier

Methodology should not be hidden after the demo. If the market is sceptical, methodology is part of the trust layer.

That does not mean overwhelming everyone with technical detail. It means creating different depths:

• a simple explanation for executives
• a practical explanation for users
• technical documentation for experts
• legal-approved wording for claims
• clear caveats around uncertainty

This reduces sales friction because it answers serious questions before they become blockers.

Build an internal claims register

Every sustainability claim should have a home.

A claims register should track:

• the claim
• the evidence behind it
• the approved wording
• the source
• the owner
• the date reviewed
• the channels where it appears
• any limitations or required disclaimers

This sounds operational because it is. In a regulated, scrutinised market, ethical marketing needs a process.

Stop overusing fear

Regulation can be a real buying trigger. But fear-only messaging is lazy.

A better approach is to explain:

• what changed
• who is directly affected
• who may be indirectly affected
• what is still uncertain
• what buyers can do now
• what they should not overreact to

That builds trust faster than exaggerated urgency.

Use proof as a growth asset

Proof should not be saved for enterprise sales calls. It should appear across the journey: homepage, landing pages, blog articles, sales decks, demo flows, nurture emails, webinars, case studies, FAQs, AI-search-friendly content and customer onboarding.

Proof is not a conversion accessory. It is the foundation of the conversion.

Measure trust, not only demand

MQLs and demo requests still matter. But they are not enough. In this category, useful growth metrics might include:

This is how marketing becomes more commercial without becoming less ethical. Sustainable fintech marketers need to build systems that make trust measurable, repeatable and useful.

FAQ

What is sustainable fintech marketing?

Sustainable fintech marketing is the communication of financial technology products linked to climate, ESG, carbon accounting, responsible investment or sustainable finance. The strongest version is not based on broad green claims, but on evidence, methodology, transparency and customer trust.

Why are ESG labels not enough anymore?

ESG labels can help buyers navigate a complex market, but they can also become shortcuts. When labels are used without enough explanation, buyers and regulators start asking harder questions about the data, methodology and real-world impact behind the claim.

What should sustainable fintechs communicate instead?

They should communicate the customer’s job, the evidence behind the claim, the methodology used, the limits of the data and the next decision the customer can make. This makes sustainability claims easier to verify and act on.

How can B2B fintech marketers avoid greenwashing?

They can avoid greenwashing by making fewer, better claims, linking each claim to evidence, showing methodology clearly, avoiding exaggerated impact language and building an internal claims register for marketing, sales and product communications.

Conclusion

ESG finance in Europe is not becoming simple. It is becoming demanding in a different way.

The EU is simplifying parts of the regulatory framework, but that does not remove the need for credibility. SFDR reform shows that labels can become confusing when they are used as marketing shortcuts. AMF and ESMA guidance show that responsible finance language is being watched more carefully.

Buyers are more sceptical, more informed and more likely to ask what sits behind a claim. [1][3][4]

For climate finance platforms, this is not bad news. It is a chance to build better marketing.

The next stage of growth in this category will not come from louder ESG language, prettier impact pages or more urgent compliance campaigns. It will come from companies that can connect claims to evidence, evidence to methodology, methodology to limits, and limits to practical next steps.

That is the real opportunity for sustainable fintech: not to make sustainability sound easier than it is, but to make it easier to understand, verify and act on.

The brands that win will not be the ones that promise the cleanest future in the broadest terms. They will be the ones that help customers make better decisions with clearer data, stronger proof and less confusion.

In impact finance, trust is not a brand value. It is the growth system.

💌 If you want more essays on ethical growth, B2B marketing and trust-led systems, you can join Conscious Growth Dispatch here.

Sources

[1] Council of the European Union, Council signs off simplification of sustainability reporting and due diligence requirements to boost EU competitiveness, 24 February 2026. The Council states that the CSRD scope is narrowed to companies with more than 1,000 employees and above €450 million net annual turnover, while CSDDD applies to companies above 5,000 employees and €1.5 billion net annual turnover.

[2] European Commission, Commission simplifies transparency rules for sustainable financial products, 19 November 2025. The Commission says its proposed SFDR changes aim to make sustainable finance disclosures simpler, more usable for investors and less costly for financial product providers.

[3] European Commission, Questions and answers on the Sustainable Finance Disclosure Regulation review, 19 November 2025. The Commission notes that the complex disclosure requirements under Articles 8 and 9 are being deleted after causing uncertainty and being misused as product categories.

[4] Autorité des marchés financiers, The AMF updates its policy following its decision to comply with the ESMA guidelines on the names of ESG funds, January 2025. The AMF explains that DOC-2020-03 has been amended following ESMA guidelines on funds using environmental, social, governance or sustainability-related terms.

[5] Greenly, Discover Our Sustainable Suite, accessed May 2026. Greenly describes its platform as an all-in-one climate solution for GHG disclosure, product carbon footprint and ESG compliance, and states that it supports more than 3,500 clients.

The post When ESG Labels Are Not Enough appeared first on SAMIN GHIASI | GROWTH MARKETING CONSULTANT | FR/EN.

Search is now two experiences stacked on top of each other.

• The first is the classic one: a list of results, rankings, clicks.
• The second is where attention increasingly goes: an AI answer that summarises, then shows a handful of sources.

If you are a founder, your content can be used without being visited. And that changes what “visibility” means.

That is where GEO comes in.

1. GEO, in one sentence

Generative Engine Optimisation (GEO) is the practice of increasing the chance your pages are selected and cited inside AI-generated answers, while staying compatible with long-term, human-centred SEO. [1]

The term was formalised in academic work in late 2023, with a very pragmatic framing: generative engines are black boxes, so you optimise by improving the odds that your content is retrieved, understood, and considered “safe” to cite. [1]

2. Why founders should care now

AI summaries reduce friction for users. That is the whole point.

Google presents AI Overviews as a way to help people “dig deeper” and discover a wider range of sources. [4]
But when an AI summary appears, real user behaviour can shift towards fewer clicks. Pew’s research found users were less likely to click on traditional results when they encountered an AI summary. [8]

Both can be true:

• Some links inside AI answers may get high-intent clicks.
• A lot of informational intent gets satisfied without any click.

So the founder question becomes: if someone never visits your site, can they still meet your thinking, your credibility, your definitions, your proof?

Citations are now part of brand distribution.

3. How AI answers choose sources (the boring mechanics that matter)

Most AI answer systems follow the same pattern:

1. The question gets expanded (often into sub-questions)
2. The system retrieves candidate pages
3. Those pages are ranked and filtered
4. A model writes a synthesis
5. A subset of sources is shown as citations

Google is unusually explicit about one key mechanism: query fan-out. AI Overviews and AI Mode may run multiple related searches across subtopics, then pick supporting pages while generating the answer. [2]

This matters because it creates a founder-relevant opportunity: you can be cited for a sub-question even if you are not ranking #1 for the original query.

4. What the platforms officially say (and what they carefully avoid saying)

a) Google: no special GEO trick, just “be eligible” and be useful

Google’s documentation is consistent on one point: there is no special markup you add to “be in AI Overviews”. You need to be indexed and eligible to appear in Search features, then you win by having useful, reliable content. [2][3]

If you want a simple mental model for Google:

• GEO is largely “SEO done properly”, with stricter expectations on clarity and trust. [3]

If you want the long version of this idea, I broke it down here: SEO for Good: Content That Powers Purpose and Growth.

b) Microsoft: citations have become a measurable surface

Microsoft introduced AI Performance in Bing Webmaster Tools, which reports how often your site is cited across Copilot, Bing AI answers, and some partner experiences. It includes total citations, cited pages, and grounding queries. [5]

This is important even if you do not care about Bing’s market share. It is the first major platform saying, in product form, that citations are a real visibility metric.

c) ChatGPT Search: citations exist when search is used

OpenAI’s help documentation states that when ChatGPT uses search, responses contain inline citations and a “Sources” button that lists cited sources. [6]

Founder implication: you are still playing a web visibility game. Being citeable starts with being accessible, indexable, and credible.

d) Perplexity: citation-forward by design

Perplexity’s own help centre describes a workflow that searches the internet in real time and produces a summary backed by sources. [7]

In practice, this rewards pages that are easy to verify: clear definitions, primary sources, and clean structure.

5. The founder playbook: 5 moves in 30 days

This is not a content calendar. It is a visibility system. This approach fits the broader system mindset I described in How to Build a Sustainable Growth Engine Without Burning Budget (or the Planet).

a) Create 5 “source pages” you actually want to be cited for

Pick 5 topics that sit at the centre of your category, then write pages built for retrieval and citation:

• A short definition near the top
• Clear H2s that match real questions
• “How it works” and “Where it fails”
• 3–5 FAQs based on sales calls

This aligns with how Google frames success in AI search: unique, valuable content that helps people. [3]

b) Make every important page verifiable

Add:

• An author or reviewer line (real person, real role)
• A “last updated” date where the topic moves
• A sources section with primary references

This is not a trick. It is how you look serious, both to buyers and to systems trying to avoid citing nonsense.

c) Use structured data, but only when it matches visible content

Google’s guidance stays conservative here too: structured data should reflect what is actually on the page. [3]

For founders, the practical version is:

• Article markup for deep explainers
• FAQ markup only if you visibly include FAQs
• No “schema cosplay”

4) Track citations like a leading indicator, not like revenue

Use:

• Google Search Console for classic performance
• Bing Webmaster Tools AI Performance for citation visibility [5]

A citation is not a conversion. But it tells you which pages the discovery layer is willing to use.

5) Publish one asset that cannot be summarised away

AI answers compress generic content. They struggle to replace original assets.

Examples that work in B2B:

• A small benchmark (even if niche)
• A compliance checklist mapped to a specific regulation
• A dataset, glossary, or teardown that is genuinely useful

You still want to be cited, but you also want to earn the click when it matters.

6. “Without ruining your SEO” is mainly a taste and integrity problem

Bad GEO looks like content written for extraction.

Templates, inflated word counts, filler paragraphs, fake authority. That is short-term optimisation for a system that changes fast.

Good GEO is simpler:

• Clarity beats length
• Proof beats vibes
• Structure beats “thought leadership”
• Honest limits beat overconfident claims

Google’s own advice basically says the same thing in polite language. [3]

Conclusion

GEO is not a new channel. It is a new surface.

Your job as a founder is to decide what you want the surface to repeat about you, even when nobody clicks.

If you build a few pages that are genuinely source-worthy, you do not just improve your SEO. You increase the chance that the new layer of search carries your definitions, your framing, and your credibility forward.

💌 If you want the next piece, you can join Conscious Growth Dispatch here.

Sources

[1] Aggarwal et al., GEO: Generative Engine Optimization (arXiv, 2023).
[2] Google Search Central, AI features and your website (includes “query fan-out”).
[3] Google Search Central Blog, Top ways to ensure your content performs well in Google’s AI experiences on Search (May 21, 2025).
[4] Google Blog, Generative AI in Search: Let Google do the searching for you (May 14, 2024).
[5] Bing Webmaster Blog, Introducing AI Performance in Bing Webmaster Tools Public Preview (Feb 10, 2026).
[6] OpenAI Help Center, ChatGPT search (inline citations and Sources button).
[7] Perplexity Help Center, How does Perplexity work? (real-time search + cited sources).
[8] Pew Research Center, Google users are less likely to click on links when an AI summary appears in the results (Jul 22, 2025).

The post GEO: Get Cited in AI Answers appeared first on SAMIN GHIASI | GROWTH MARKETING CONSULTANT | FR/EN.

Over the last few years, I’ve worked with and around early-stage startups in France. Highly ambitious teams, usually product-strong, moving fast because they must. And a tension I see repeatedly is simple.

Growth is expected before everything is clear.

Marketing is asked to perform before the metrics are stable.
The organisation grows while roles, priorities, and processes are still mostly implicit.

This is not a talent problem. It is a sequencing problem. And in 2026, it has become harder to ignore because the market has normalised and funding is more selective. [1]

The French Tech context: normalisation, tighter funding, higher expectations

A more accurate word for 2026 is not “collapse” but “selection”. Capital still exists, but access is harder than it was in 2021–2022, and expectations are sharper. [1]

One data point captures the shift. The France Digitale x EY barometer has referenced 27% of French startups that raised funding over the previous 12 months, while 10% explicitly chose not to raise. [2] So for a large share of teams, “we’ll raise” is no longer the default plan. The operating model must carry more of the weight, through clearer unit economics, tighter prioritisation, and execution that compounds. [2]

That reality pulls leadership back to fundamentals: CAC, retention, gross margin, runway, and the company’s real capacity to deliver today.

The scale-up gap in France: not just structure, but founder profile and operating habits

France is good at launching startups. Scaling them into durable organisations remains harder.

The generic explanation is well-known: what works for 8 people breaks at 40. The France-specific question is why so many teams delay formalisation until the pain is already expensive.

A defensible part of the answer is the founder profile. Institut Montaigne’s research on unicorn founders points to a strong presence of engineering and business school backgrounds among French founders. [3] In practice, this often creates a very specific early strength: product depth, technical credibility, and strong problem framing.

But it also creates a predictable late-stage risk: if the organisation grows while decision rights and ownership remain implicit, coordination costs surge. Not because “French founders are bad at management”, but because early-stage execution rewards being everywhere at once, and that habit does not break automatically when headcount doubles.

You can see the symptom in hiring. The France-focused synthesis cites 57% of recruitment being considered difficult. [4] Part of that is the labour market. Part of it is organisational readiness: hiring is one thing, integrating people fast enough to reduce coordination costs is another.

This is the scale-up gap in its most practical form: the company does not fail because it cannot recruit, but because it cannot convert recruitment into autonomous throughput quickly enough.

Profitability becomes the compass, not an afterthought

The most significant cultural shift in France since 2024 is the return of profitability as the primary goal.

We can witness that 80% of French startups are profitable or aim to reach profitability in the short to medium term. [4] Investor expectations tighten in the same direction. One shorthand you see more often is the Rule of 40, typically expressed as:

Revenue growth rate (%) + EBITDA margin (%) > 40 [2][5]

The point is not to chase a score. The point is that growth alone is no longer enough. In a tighter capital market, decision-makers want a credible path to durable value creation and cleaner financial steering. [1][2]

Growth amplifies fragility: a practical diagnostic

Growth does not fix what is unclear. It amplifies it.

If the system is coherent, growth accelerates what works. If the system is fuzzy, growth magnifies friction, waste, and bottlenecks. You see this more clearly in a more selective market, where consolidation and failures become visible faster. [1]

Here is a compact diagnostic you can actually use.

1) The founder bottleneck test

• How many decisions per day require one person’s approval?
• If the answer is “too many to count”, you do not have a scaling problem. You have a decision architecture problem.

2) The context switching tax

• How many active “top priority” initiatives exist right now?
• If it is more than three, execution speed will often collapse without anyone noticing why. The organisation feels busy, but output becomes inconsistent.

3) The ownership clarity check

Pick three areas: pricing, roadmap, and hiring plan.
Ask: “Who owns this end-to-end, and what is their decision scope?”
If the answer is complicated, you are accumulating hidden coordination debt.

4) The culture dilution signal

Can a new hire at 90 days explain how decisions are made here, without guessing?
If not, culture becomes accidental, and cross-functional work turns into negotiation instead of collaboration.

One question summarises the whole framework:

What, exactly, would acceleration amplify in our system right now?

If the honest answer is “confusion”, scaling acquisition or headcount will not rescue you. It will surface the problem faster.

Ethical marketing in 2026: an operating discipline, not a moral label

In many early-stage teams, marketing becomes the reflex when pressure rises: more leads, more traffic, more campaigns. Sometimes it works short term. In 2026, it is riskier because the runway is harder to extend through fundraising. [2]

For me, ethical marketing is not a moral posture. It is a concrete alignment rule:

Align the promise, the operational capacity, and the economic model.

That alignment usually implies sequencing discipline:

• Do not scale acquisition faster than your ability to deliver value.
• Treat activation and retention as foundations before volume.
• Use marketing to clarify value, not to cover gaps.

Example: a team increases paid acquisition to compensate for weak retention. Top-of-funnel metrics rise, but cash pressure increases because value is not being retained. In this situation, “more marketing” amplifies the weakness. Responsible marketing redirects effort towards onboarding, value realisation, and retention before increasing spend. [2]

This is how “ethical” becomes operational: less waste, more truth, more trust.

AI and consolidation in 2026: not extra trends, but proof of the thesis

Two themes stand out: consolidation dynamics and AI framed through operational ROI. [1] Both are consequences of the same underlying shift; markets now reward clarity and discipline.

• Consolidation is what selection looks like in practice. Companies with clearer economics and operating models become buyers, or survive long enough to find strategic combinations. [1]
• “Useful AI” (including generative AI and agentic workflows) forces clarity because you cannot automate what you have not defined. If your metrics, decision rights, and feedback loops are vague, automation does not hide the chaos. It exposes it.

Conclusion

In 2026, startup growth in France looks less like a race for speed and more like a design problem.

The teams that scale sustainably are not always those that accelerate earliest. They are the teams that structure before amplifying, steer with readable metrics, and treat marketing as a responsible lever in service of real value. [1][2]

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Sources

[1] Le Monde (June 27, 2025), reporting on the French Tech funding slowdown and the shift from rapid growth to profitability.
[2] Goweez (Sept 15, 2025), citing the France Digitale x EY barometer: 27% raised, 10% chose not to raise; more selective fundraising and emphasis on metrics.
[3] Institut Montaigne, Innovation: France’s Got Talent (founder background distribution among French unicorn founders).
[4] Maddyness, citing the France Digitale / EY barometer on startup profitability objectives. France Travail, Besoins en Main-d’Œuvre (BMO) survey on recruitment difficulties
[5] Wall Street Prep, “Rule of 40” definition (growth rate + EBITDA margin).

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