Millions of People in Canada Are Finding AI-Enabled Support for Mental Health Effective Amid Ongoing Questions Around Trust.
(OTTAWA) July 8, 2026 — New polling shows approximately six million people in Canada used AI-enabled tools for mental health support in the past year and most find them effective. Today, the Mental Health Commission of Canada (the Commission), in partnership with Mental Health Research Canada (MHRC) and Pollara Strategic Insights, releases the first nationally representative data on how people in Canada engage with digitally supported mental health tools, including AI and virtual care, across every province and demographic.
Quick Facts:
- 1 in 7 people in Canada used AI mental health tools in the past year
- Three out of four who used AI and virtual mental health services found them effective for their well-being
- Only 14 % trust AI tools, just 2% trust them completely
- 40 % of AI users said they were more likely to seek professional care
- Nearly half (45%) who accessed mental health care did so virtually, in whole or in part
WHY IT MATTERS
People in Canada are turning to AI as a convenient way to access mental health support. AI-enabled tools may offer greater convenience and accessibility. Among those surveyed, AI is being used because it is:
- Free or low-cost; 46% of AI users cite this as the reason they use it during a time when financial stress is itself a cause for anxiety.
- Always available; 44% of AI users cite 24/7 access.
- Immediate and convenient; it can be used from anywhere without travelling or waiting for an appointment. For someone in rural Canada, it saves time and travel costs.
- Seemingly private; 39 % of AI service users cite private, anonymous support as a reason for use, while privacy and data protection remain key public concerns.
AI is most used for general well-being (42%), companionship (36%), and mild-to-moderate stress (36%), and 40% of AI users said they were more likely to seek professional care.
WHO IS USING IT AND HOW MUCH DO THEY TRUST IT?
Use is higher among people in Canada under 35 (27%; 29% among men aged 25–34), newcomers to Canada (28%), racialized people in Canada (23%), and 2SLGBTQI+ communities (20%), populations that may experience greater barriers to traditional care.
Overall, trust remains low, particularly for AI-enabled tools, where only 2% of people in Canada trust them completely. People in Canada over 55 show the lowest adoption and trust.
VIRTUAL CARE: EFFECTIVE AND MORE TRUSTED BUT FALLS SHORT OF IN-PERSON SERVICES
45% of people in Canada who used mental health services in the past year did so virtually, with 75% reporting positive outcomes. However, nearly 1 in 3 prefer a hybrid model that combines virtual and in-person services. The data signals what people in Canada need: well-designed tools for safer digital mental health care that they can trust.
THE COMMISSION OFFERS GUIDANCE FOR THE DIGITAL MENTAL HEALTH ERA
The Commission is Canada’s trusted resource for safe digital mental health — assessing apps and tools, setting evidence-based standards, and leading the national conversation on guidance for AI in mental health and substance use health care.
As virtual services and AI-enabled tools continue to expand rapidly across the mental health landscape, there is a growing need for evidence-based insight into how people in Canada engage with, understand, and perceive them. The Commission partnered with MHRC to leverage their ongoing national polling initiative and provide timely insights into usage, attitudes, and concerns related to e-mental health and AI.
The polling is clear: people in Canada want to close the gap between availability and trust. The Commission is working with the Canadian Centre on Substance Use and Addiction and collaborators, provincial governments, technology developers, and health system partners to establish guidance for AI.
“Six million people in Canada have already used AI for mental health support and most found it convenient and effective for their well-being. It is critical that AI is safe and equitable to increase public trust and reduce harms.” – Lili-Anna Pereša, President and Chief Executive Officer, Mental Health Commission of Canada
“The people turning to digitally-supported mental health tools are often those facing some of the greatest barriers to care. Making sure these tools are safe, effective, evidence-based and human-centred is a matter of equity. Ongoing research is essential to understanding where they help and where safeguards are needed.”– Akela Peoples, Chief Executive Officer, Mental Health Research Canada
About Mental Health Commission of Canada
As an independent, not-for-profit with charitable status, the Commission collaborates with leading experts and organizations nationally and internationally, including with people with lived and living experience, to develop national guidelines, standards and strategies, promote innovation and best practices, reduce stigma, increase mental health literacy, and support all levels of government to improve mental health outcomes for everyone living in Canada. The Commission is Canada’s trusted resource for digital mental health best practices with the e-Mental Health Strategy for Canada, app assessment, e-modules for e-mental health implementation, and AI guidance for mental health and substance use health.
About Mental Health Research Canada
As an independent national charity, MHRC works hard to enable a future where mental health in Canada is transformed using evidence, data and stakeholder engagement. We unite researchers, communities, and people with lived experience to bridge gaps in care through national population polling, rapid data reporting, and partnerships that inform policy to improve outcomes. Learn more at www.mhrc.ca
About the Polling
Conducted by Pollara Strategic Insights in partnership with Mental Health Research Canada and the Mental Health Commission of Canada, this national poll (n=3,519) is the first representative data on AI use for mental health in Canada. Full findings: https://mentalhealthcommission.ca/AI-polling-report
About the Funding
The views in this report solely represent the views of the Mental Health Commission of Canada. Production of this report is made possible through financial contribution from Health Canada.
Media Contact
Heather Bakken, Pendulum Group
email: heather@pendulumgroup.ca
cell: 613-406-5432
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Exploring the Experiences of Moderators From an Asynchronous Online Dementia Support Forum: Qualitative Interview Study
Background: Asynchronous online forums provide flexible, accessible peer support for many people living with dementia and carers. Moderators are central to the functioning of these communities, yet little is known about their experiences. Objective: This study explored the experiences of individuals moderating an online dementia forum, including their motivations, perceived benefits, challenges, and suggestions for improvements. Methods: Moderators from a UK-based online dementia support forum were recruited using purposive sampling via forum administrators. Between January and March 2025, 5 moderators, all with dementia care experience, participated in remote semistructured interviews. Interview topics included pathways into moderation, perceptions of the moderator role, experiences of supporting forum members, challenges encountered, perceived personal benefits, and views on the future development of online support communities. Interviews were transcribed verbatim and analyzed using reflexive thematic analysis. Results: Four themes were produced: (1) “from support seeker to support provider”: moderators primarily identified as community members rather than authority figures, following a trajectory from receiving support as a carer to actively facilitating community support; (2) “understanding through shared experience”: lived experience of dementia was seen as essential for empathy, credibility, and sensitive responses, though sometimes prompted strong emotional reactions; (3) “giving back and gaining in return”: moderation offered purpose, structure, and social connection, particularly postretirement and following transition out of caring; (4) “balancing growth with community preservation”: forum expansion increased workload, spam management demands, and safeguarding responsibilities, and moderators were cautious about social media–style features and artificial intelligence–generated content undermining the effectiveness of support exchanges. Conclusions: Moderators play a crucial, value-driven role in sustaining dementia support forums, extending beyond administrative duties. The findings suggest that moderators occupy a distinctive position as both recipients and providers of peer support, drawing on experiential expertise to maintain trust and community cohesion. Forum growth and technological innovations present opportunities and challenges, highlighting the need to balance scalability with authenticity in online support communities.
<img src="https://jmir-production.s3.us-east-2.amazonaws.com/thumbs/7de433b448ae15efb6eafec0d1e8d504" />
The foundational elements of AI architecture that IT leaders need to scale
With the rapid progress of AI capabilities and the move to agentic systems, organizations are expanding their use cases as the technology continues to grow. That constant evolution also introduces risk, leaving IT leaders to wonder which investments will prove valuable even six months into the future.
Returning to the foundational elements of AI architecture—the structural framework required for deploying and managing reliable, integrated AI systems at scale—allows technology leaders to make astute decisions today while supporting a future of AI agents that can retrieve information, make decisions, and execute complex workflows across systems.

Four elements of AI architecture you can count on
The following capabilities provide a stable compass on the path to production-ready deployment, regardless of how the underlying technology evolves.
1. Prepare data for AI at scale
Models are only as reliable as the data they can access, and poor data quality leads to AI hallucinations, bias, and unreliable outputs.
Most enterprises rely on legacy systems, inconsistent data structures, fragmented ownership, and incomplete datasets, making it difficult to scale AI effectively. Powerful as it is, AI itself cannot solve these underlying data problems.
As Adnan Adil, CIO of Elastic, explains: “The data is a durable part of AI architecture because without it, these models won’t run, won’t provide the right context, or won’t give the right level of services that we’re looking to implement.” Industry surveys consistently cite data quality as one of the greatest barriers to AI success. “The data quality has to be good; otherwise, the user loses confidence in the system,” says Adil.
An effective AI strategy begins with connecting data across the organization and ensuring it is organized, accurate, governed, and accessible in real time. These considerations are most effective when built into models and architecture from the start. Scalable data architecture allows AI systems to evolve alongside the business and connect reliably to the internal information needed to deliver meaningful value.
Gartner predicts that companies will abandon 60% of all AI projects through 2026 if they are not supported by AI-ready data. Avoiding that outcome includes clear data standards and ownership, clean and labeled data, and pipelines that support real-time retrieval.
2. Use context engineering to deliver the right data to every AI query
Context engineering ensures that the model draws on the most pertinent information for each query, selecting and organizing the data needed to produce accurate answers efficiently.
Effective context engineering shapes the inputs that guide AI reasoning and action. While prompt engineering focuses on how a request is worded, context engineering designs the entire information environment around the model: retrieving the right data and presenting it in a structured, machine-readable way. Many organizations are discovering that reliable AI depends as much on context quality as on the strength of the model.
Context engineering relies on a modernized, unified data foundation as well as retrieval and memory systems such as retrieval augmented generation (RAG) and vector databases. It also requires careful prioritization to determine what information matters most, what should be excluded, and when different types of information should be used. Feeding models too much context can dilute relevant details, increase costs, and slow response times.
“Minimum context, correct and current data, and machine-readable information are critical to effective context engineering,” Adil says.
3. Build AI governance and LLM observability in from the start
Strong governance and LLM observability help organizations maintain control over how AI systems use data, monitor system performance, and identify problems before they affect operations.
In the absence of clear controls around retrieval, workflows, and model usage, AI systems often process far more information than necessary. This inefficiency also drives up operating costs by requiring additional computing resources, often reflected in higher token consumption and API charges.
Governance also works in tandem with robust security. AI expands the attack surface, introducing risks such as prompt-based data leakage, model vulnerabilities, and adversarial inputs. Protecting sensitive information requires strong access controls, monitoring, and oversight.
Adil notes that essential controls — including those related to security, granular cost management, project controls, data security, and architecture—are frequently insufficient.
For governance systems to support transparent, compliant, trustworthy, and cost-effective AI, organizations cannot leave them as a layer to add later. Governance structures need to be embedded into architecture, workflows, and decision-making processes from the outset.
When governance is established from the start, it enables robust observability. Observability helps organizations understand how AI applications are performing in practice. Mechanisms for LLM observability and benchmarking allow teams to assess accuracy and utility over time, monitor adoption patterns, and adjust systems as conditions change. Observability also helps organizations gain trust by increasing visibility of model performance, behavior, and failure points.
Furthermore, observability is essential to get ROI of AI initiatives, as the benefits of it are often indirect and business value depends heavily on how systems are adopted and used. Real-time visibility into AI behavior allows organizations to measure performance against expectations, identify gaps between intent and reality, and continuously refine systems as requirements evolve.
In a 2026 report from Elastic, 85% of IT decision makers expect to enable LLM observability for their internal generative AI apps.
“Observability is actually huge. We can use observability data for cost control, decision-making, and engineering efficiency,” Adil says.
4. Keep humans in the loop
The thoughtful design, integration, and governance that maximize AI value demand specialized in-house expertise. Nearly 70% of respondents in Deloitte’s 2025 Tech Executive Survey report plan to grow teams in direct response to generative AI, a clear contrast to widely reported AI-related cuts. Adil agrees: “We think the people aspect is largely what’s going to make AI impactful going forward.”
As AI systems become more embedded in operations, organizations need people who can govern workflows, evaluate outputs, redesign processes, and adapt systems as conditions change. Evolution toward increasingly autonomous tools requires teams skilled in prompt engineering, orchestration, and change management.
Talent adept at critical thinking and prepared to adapt with technology’s rapid advances will be in high demand. Although turnover brings in fresh thinking, it also presents high costs in system continuity, institutional understanding, and innovation. Human-centered strategy needs to be built into AI execution stages to ensure smooth implementation.
As Adil says, “Many aspects of the stack are moving very, very fast, but institutional knowledge and the ability to adapt remain durable.
Thoughtful AI investment for future growth
As AI systems evolve from single-task assistants to increasingly autonomous agents, the organizations best positioned to benefit will be those that invest in the underlying systems, governance, and expertise that make AI reliable at scale.
Tech leaders who focus on these fundamentals can move effectively from experimentation to reliable, production-level deployment in the medium term, confident that these elements will remain relevant and adaptable amid constant advancements.
“We fundamentally believe that with these tools, velocity of work will get much faster,” Adil says. “We are really focused on how we can do work with these tools in ways we had not thought of before.”
Learn more about how Elastic is building an AI-first enterprise with these core foundational components.
This content was produced by Insights, the custom content arm of MIT Technology Review. It was not written by MIT Technology Review’s editorial staff. It was researched, designed, and written by human writers, editors, analysts, and illustrators. This includes the writing of surveys and collection of data for surveys. AI tools that may have been used were limited to secondary production processes that passed thorough human review.
Technology-Enhanced Peer Support for Depression in Older Adults: Single-Arm Mixed Methods Feasibility Study
Why worms (and microbes) are catching on as a manure pollution solution
Anthony Agueda, a third-generation California dairy farmer, pulls a rake through a bed of dark, wet wood chips on his family’s land in Hickman, a tiny town in the state’s agricultural heartland.
He reaches down with both hands and pulls up a clump of muck, turning it over to reveal a half-dozen squirming red earthworms. There are likely hundreds of thousands more wriggling just under the surface of the three-foot mound of wood and crushed river rock before us, which stretches across the equivalent of six football fields. These natural materials form a biofilter that may dramatically cut the methane, nitrous oxide, and water pollution generated by the massive amounts of manure that hundreds of Holstein cows produce each day.
Agueda’s family business, the Alberto Dairy, was one of the first cattle operations in California to adopt this approach to manure treatment, developed and patented by the Chilean company BioFiltro. Eight more of these so-called vermifiltration systems are already operating on US dairies, according to the company, while another 16 are under construction or set to be next year, nearly all of them in California.
Vermifiltration is just one of a variety of methods that farmers, companies, and scientists are employing to drive down manure pollution as the livestock industry faces growing pressure to address the environmental harms from one of the smelliest parts of the business. California, easily the nation’s largest milk producer, has established a handful of programs to promote their adoption, including one initiative that has funneled more than a billion dollars to farms.
Researchers stress that much more work needs to be done to determine the most effective approaches, the trade-offs between them, and their success over the long term, under actual farm conditions.
Agueda says that he and his family recognized the need to adopt new practices as environmental rules tightened. They were drawn to vermifiltration because it’s simple and relatively cheap compared with other, higher-tech options.
“California daily farmers are constantly facing more and more regulation,” says Agueda, standing alongside one of the farm’s free-stall barns. “This makes me excited, because it shows how we are part of the solution.”
The growing manure problem
Manure is responsible for a significant portion of the climate pollution from livestock operations. The World Resources Institute estimates that manure management on dairy and swine farms accounts for 1.6% of the US’s greenhouse-gas emissions. Globally, manure storage and processing makes up about 10% of the livestock industry’s contributions to climate change.
“Farms have become larger in the past two decades or so, so there’s much more manure—and that has to be stored somewhere,” says Swati Hegde, the organization’s global manager of agricultural methane.
Typically, cattle and swine farms spray manure into lagoons or tanks, creating a foul-smelling, low-oxygen slurry in which microorganisms known as methanogens thrive. They gobble up hydrogen, carbon dioxide, and other compounds and produce methane as a by-product. Other microbes in the mix produce smaller amounts of nitrous oxide.

Both are particularly potent greenhouse gases, with as much as 30 to nearly 275 times the warming power of carbon dioxide, respectively, over a century.
The slurry is often spread onto fields to add nutrients to the soil. When it’s done excessively or improperly, this part of the practice can pollute soil or groundwater with drug residues, pathogens like salmonella and E. coli, and nitrates. Nitrates that leach into drinking water have been linked to a variety of human health risks. And those that flow into rivers, lakes, and coastal waters can spawn algae blooms that poison fish, block sunlight, suck up oxygen, or form large coastal dead zones devoid of marine life.
Policy drivers
A number of regions, nations, and states have passed regulations or offered subsidies designed to limit the pollution from livestock manure, but so far, most of the major initiatives have focused on water contamination rather than greenhouse-gas emissions.
The European Union, for instance, restricts the amount of manure that farmers can apply to fields and requires member nations to monitor nitrate levels in ground and surface water. The US’s Clean Water Act requires large livestock operations to obtain permits and develop manure management plans that limit pollution.
But California has arguably done the most to use government policy specifically to drive down the methane emissions from livestock. The dairy industry accounts for about 45% of the state’s pollution from the potent greenhouse gas, and more than half of that comes from manure, according to the government’s estimates.
In 2016, the state enacted a law that requires dairies, landfills, and other businesses to cut methane emissions 40% below 2013 levels by 2030, as part of a broader effort to reduce pollution from powerful but short-lived greenhouse gases. The measure directed the California Air Resources Board, the state’s main climate regulatory agency, to set up various incentive programs to encourage these industries to shift to cleaner practices.
“In terms of bang for your buck, short-term benefits, methane can go a long way toward reaching climate goals,” says Tawny Mata, director of California’s Office of Agricultural Resilience and Sustainability.
Between these various programs—and falling livestock numbers in the state—the dairy sector is on track to reduce annual methane emissions by the equivalent of 5 million metric tons of carbon dioxide by 2030, the state estimates. That would still fall about 4 million tons short of the target under the 2016 law.
The downsides of dairy digesters
Excluding the decline in herd populations—which has been driven by growing international competition and rising costs—the vast majority of California’s estimated methane reductions come from the use of what are known as anaerobic digesters. This technology entails covering the slurry lagoons to prevent methane from leaking into the air and then piping the biogas into separate vessels, where it’s cleaned and converted into natural gas.
Under California’s Low Carbon Fuel Standard program, dairies that use digesters to produce gas delivered into pipelines can earn credits and sell them to petroleum refineries and other major polluters, as a means of helping those companies meet their own emissions reduction requirements.
The gas can then fuel power plants, produce hydrogen, or power natural-gas vehicles. These uses still release carbon dioxide, but the state considers it a climate win because it avoids the release of methane, which traps even more heat.
The rich revenue stream from California’s program has spurred hundreds of US farms to install anaerobic digesters over the last decade. Since 2020, it has produced more than $1 billion for farms, Cal Poly researchers noted in a paper last year.
But there are a variety of concerns about this approach.
The first is that it’s viable only for farms with about 2,000 cattle or more, because the equipment is very expensive to install, says Frank Mitloehner, a professor and chair of the Department of Animal Science at the University of California, Davis.
“For the lion’s share of dairies, digesters will not be a solution,” he says.
Since the manure is often still spread across fields, digesters also do little to address the water pollution problems—and can even exacerbate them because of some of the chemistry that occurs during that process.
Yet the huge subsidies flowing to digesters have steered money, energy, and attention away from other solutions that may offer better overall environmental outcomes, says Danny Cullenward, a senior fellow with the Kleinman Center for Energy Policy at the University of Pennsylvania, who has closely studied the California program.
“That is really not a solution at scale, and it’s diverting a huge fraction of precious resources to what I think is mostly not the right answer,” he says.
Alternatives
The high up-front costs and limitations of digesters have spawned growing interest in alternative solutions—many of which work by reducing the formation of methane in the first place instead of turning that methane into a sellable fuel.
One of the cheapest, easiest, and most popular approaches, known as solid separation, uses simple machinery like a screw press to squeeze much of the water out of the manure slurry. The remaining solids are dry and exposed to open air, shifting away from the oxygen-free conditions in which methane is readily produced.
Other methods include increasing acidity in lagoons, bubbling air through them, or adding methane-eating microbes to the slurry, all of which alter the chemistry in ways that promise to reduce the amount of methane released. One company, Sedron Technologies of Sedro-Woolley, Washington, has also developed a sort of high-tech solid separation approach that extracts several marketable products from the animal waste, including a liquid organic fertilizer.
The state of California set up a pair of additional programs to help smaller farmers adopt some of these other approaches, dubbed the Alternative Manure Management Program and the Dairy Plus Program.
The bulk of the funds have gone to solid separation systems. But the state has provided more than $18 million to support 15 vermifiltration projects. The Alberto Dairy has received nearly $2 million between the two programs.
Oreo cows
As I drove down a dusty road bordering the dairy, black-and-white bovines, affectionately known as Oreo cows, stretched their heads through the rails of an open barn, nibbling on golden silage scattered along the structure. Agueda’s grandfather Antonio Alberto founded the dairy 45 years ago in nearby Atwater, California, but eventually settled in Hickman, population 604, in 1989.

It was mid-March but already above 80 °F in the Central Valley, which is walled off from the cool Pacific air by the coastal mountain range. Knee-high oat stalks swayed in fields that stretched to a line of almond trees in the distance.
Agueda, who graduated from Fresno State last year and now helps lead the operations on the farm, met me and UC Davis’s Mitloehner, who has studied the effects of vermifiltration, along the side of the barn. (UC Davis has no affiliation with the farm, but the university helped facilitate the meeting.)
He led us along dirt lanes as he explained the workings of the vermifiltration system, which they began using in October 2024.
As before, a flush system washes manure from the floors of the barns into a large collection pit. But now a set of pumps funnels it through a series of large V-shaped metal contraptions standing on a nearby concrete pad, where mechanical screens separate most of the solids from the water.
A conveyor belt takes away the solids, which the farm composts for cow bedding or fertilizer. The remaining liquid moves through a system of pipes, first to settling ponds and then on to an irrigation system suspended above the vermifiltration beds. The long, tubular structure runs over the mounds on wheels set in gravel tracks, wetting the wood chips as it goes. The worms and various microbes residing in the biofilter then set to work consuming much of the remaining solid material, according to BioFiltro.

“Once the water is sprinkled on top, it takes about four hours from beginning to end for it to percolate through and drain to the end,” Agueda says.
He then defers to Mitloehner to explain the science of what happens as it does, adding, “I’m just the dairyman.”
The science
Mitloehner says he was skeptical of BioFiltro’s claims when he first heard them, particularly the assertion that the system could nearly eliminate nitrogen and, with it, the various forms of pollution it can produce, including ammonia and nitrates.
So he decided to study a similar setup at the Fanelli Dairy, an operation in Hilmar, California, about 20 miles to the south. He and colleagues monitored the emissions from wastewater samples that were taken from the system before and after the liquid moved through the filter. In a paper published in 2018, the researchers concluded that vermifiltration reduced ammonia emissions from the resulting water by about 90%.
BioFiltro, whose tagline is “worm-powered solutions,” states that its technology “catalyzes the digestive power of worms and microbes to remove up to 99% of wastewater contaminants.”
But Mitloehner questions how big a role the invertebrates play in the process, calling it “kind of a catchy narrative.”
His take is simpler: The rocks and wood chips form a porous filter that replaces the anaerobic environment of a manure lagoon with an aerobic one. And in that oxygen-rich environment, different types of microbes thrive.
His study suggests that these microbes are highly effective at converting nitrogen compounds in manure into nitrogen gas—a benign gas that makes up 78% of Earth’s atmosphere—instead of ammonia. That’s notable because while ammonia in manure acts as a fertilizer when it’s applied to fields, it also converts into the nitrates that can leach into groundwater.
Several more recent studies, which were partially or fully funded by BioFiltro and one of its regional distribution partners, Organix, produced similarly promising results. For instance, a 2022 study in Bioresource Technology Reports, also conducted at the Fanelli Dairy, concluded that the filter removed nearly 85% of the nitrogen in the operation’s wastewater.
But a befuddling wrinkle is that when it came to methane, those studies and Mitloehner’s independent one found nearly opposite results.
While both the company- and partner-supported studies concluded that the filter eliminated the vast majority of methane pollution, Mitloehner’s study found that methane emissions were nearly 85% higher than those from the lagoon.
In a follow-up email exchange, Mitloehner stressed that it’s not appropriate to compare his results with those that emerged from the other study at the same dairy, because the teams used very different methods, instruments, and measurement periods. Moreover, the focus of his research was the effect on nitrogen.

He said it’s “entirely reasonable” and “biologically plausible” that vermifiltration could substantially reduce methane emissions, simply by creating that aerobic environment.
“That said, I would be cautious about calling the magnitude of the reduction a fully settled issue,” he added. “While the available studies, including those you mentioned, point in the same general direction, the number of independent studies remains relatively limited, and results can vary.”
Patrick Beckett, BioFiltro’s vice president of quality and R&D, also stressed that there were crucial differences in the methodology of Mitloehner’s study that could have affected his methane findings.
In addition, he said the Organix funding came by way of a Washington state grant and described that study and the one BioFiltro supported as “high quality, peer reviewed” research that “has been submitted to other technical third parties for review and acceptance.”
Beckett says he agrees that additional independent reviews of BioFiltro’s systems is “fair and necessary” and notes that other studies have occurred or are underway.
“That said,” Beckett wrote in an emailed response to questions from MIT Technology Review, “it seems unreasonable that BioFiltro would be held to a standard of not being allowed to invest in technical research by qualified third parties to learn more about the capabilities of our technology, and use the results of that research to enter new markets and to understand the value we can bring to projects or entire industries beyond water treatment.”
Milk money
BioFiltro is already building a business model around the available findings.
The company, founded in 2009, has been selling its vermifiltration systems or services to other industries around the world for years. It says there are around 225 operating in nine countries, at sites including municipal wastewater facilities, wineries, fruit processors, and other industrial operations.
But BioFiltro, whose US headquarters are in Davis, California, is seeing increasing demand among dairies as the industry faces growing pressure to address manure pollution. Late last year, it raised $35 million that the business says it will use, in large part, to accelerate its growth across the sector.
In an interview, Sarah Ploss, the company’s senior vice president of agriculture, explains the basic financial template for how it works with dairies: BioFiltro pays for, owns, installs, and operates the system. The farm, in turn, covers a share of the additional electricity, operations, and maintenance costs.
Ploss says the dairy gets back clean water and the ability to focus on what it does best: producing milk. For its part, BioFiltro can generate carbon credits from the reduction in greenhouse gases, which it can then sell to makers of consumer packaged goods that are looking for ways to address the emissions throughout their supply chains, she says.
BioFiltro says that Verra, which sets standards for and assesses greenhouse-gas crediting projects, has registered two of its projects: the Royal Dairy and Moxee Dairy, both in Washington.
The Swiss confectionary giant Nestlé has bought more than 150,000 credits generated by the Royal Dairy’s vermifiltration system, according to an offsets database managed by CarbonPlan, which assesses the scientific integrity of climate action programs. Ploss said that BioFiltro has sold more than 200,000 credits from the project so far, and adds that it secured a different buyer for a project in California, which she said she couldn’t name.

Three additional projects involving BioFiltro systems took the initial steps to become registered through Verra but didn’t move forward and weren’t built, Ploss said in an email. The request for registration for the Alberto Dairy estimates that the system there will reduce emissions by the equivalent of more than 30,000 metric tons of carbon dioxide per year.
BioFiltro could take advantage of another revenue source as well: selling what it calls vermicompost, a rich soil additive composed of the leftover materials in the biofilter, including worm castings—a combination of cocoons, excrement, and remains. At retail, worm castings can run more than $500 per ton.
Beckett says the company is still developing that market but notes that it could help the industry offset rising fertilizer costs.
“I think we’re going to enable a larger-scale use and adoption of it that could be meaningful to agriculture,” he says, adding: “These will become basically soil production facilities.”
Concerns
Determining how well vermifiltration and other manure management approaches work will require more time and more research, experts say.
Katharine Dickson, an agricultural emissions scientist who recently finished a postdoctoral program at UC Davis, says there should be in-the-field accounting to ensure that any of these methods are working as well as hoped—or to the degree government policy programs assume. All of which is tricky to achieve given the dynamic biological processes playing out in live animals and microbial communities on open farms, she adds.
“Vermifiltration, for example, depends on a live earthworm population whose performance is sensitive to temperature, moisture, and toxicity, and can shift with seasonal conditions or changes in herd size and manure characteristics on a given farm,” Dickson said in an email.
The use of carbon credits to earn money from vermifiltration projects raises a different set of potential concerns. Most notably, if the methane decreases aren’t as significant as assumed, the projects could receive more credits than they deserve.
There are more complicated issues as well. For the carbon credit system to make any real difference in the net amount of greenhouse gas in the atmosphere, it must produce emissions reductions that wouldn’t have occurred without that financial incentive. If it was going to happen anyway—as a result, say, of rich grants, legal pressures, or looming policies—the buyer of the credits can’t legitimately claim to have made any progress on its own climate emissions, says Grayson Badgley, a research scientist at CarbonPlan.
On that point, if California agriculture doesn’t meet its looming methane reduction targets, the carrots the state offers could be replaced by sticks: The California Air Resources Board recently began discussing rules that would force, rather than nudge, the sector to meet the 40% reduction required under the 2016 law.
“If lots of dairies are cleaning up their act ahead of pending regulation, it really does seem like the regulation, not offsets, is driving that action,” Badgley wrote in an email. “Trying to collect as many offsets prior to that deadline might adhere to the rules of the market, while still raising questions about whether those rules have enabled real climate action.”
Investing in sustainability
Beckett disagreed that the possibility of forthcoming regulations undermines the case for generating carbon credits from current projects.
“It’s true the state has net reduction targets that it hopes to meet, but it’s clear the state of California has favored market-based solutions and tried to provide some support via grant programs,” he wrote. “I’m on the science side of our business, not the business development side, but still think I can tell you with complete transparency that we would not have systems installed on [California] dairies without the sale of voluntary carbon credits.”
Ploss also stressed that the company goes through a careful “validation and verification process” on the farms to understand how much vermifiltration reduces greenhouse gases.
“We’ve got sensors and cameras and all sorts of stuff so that we can look into any of our systems, 24-7,” Ploss says. “We know through sampling. We know through what’s going through the system, what came out of the system. We know by all the measurements on any given month: What did that system do in terms of generating carbon credits?”
Agueda also disputes the critique.
“The installation of the vermifiltration system would not have occurred without the ability to generate carbon credits,” he said in an email. “The project required a substantial capital investment, and the anticipated carbon credit revenue was a key factor in making the investment financially feasible.”

California decided to incentivize vermifiltration, along with other approaches, because it can offer multiple benefits, including cleaner water, less nitrogen, and lower greenhouse-gas emissions, while also creating economic value from manure, wrote Roberta Franco, a senior environmental scientist at the California Department of Food and Agriculture, in an emailed response to questions from MIT Technology Review.
She added that the decision was based on a number of studies as well as the 2022 recommendations from a task force composed of scientists, technical experts, and others.
Even if California has made missteps, most notably in funneling too much money to anaerobic digesters at the expense of other methods, it’s created a test lab that’s achieved real progress and provided lessons that other regions can learn from.
One way or another, more parts of the world will need to set up similar programs, offering greater support or creating stricter rules, if we hope to really drive down the emissions from manure, says Maria Bowman, who leads the Agricultural Nitrogen Transformation Program at Spark Climate, a San Francisco nonprofit.
For his part, Agueda says that the vermifiltration system has offered a number of benefits to his family’s farm, at little additional cost to them. By cleaning up the water that cycles back through their flush and irrigation systems, the biofilter has reduced clogging, decreased odors, and improved the health of the herd.
He says that each generation modernizes dairy farming in its own way. His father and uncle, for instance, incorporated computers and data management systems into the daily operations of the Alberto Dairy. He believes it’s the responsibility of his generation to make a similar effort to reduce the pollution that’s long plagued the sector.
“We knew that in the next generation we have to invest in environmental sustainability,” he says. “We didn’t know if it was gonna work or not, but we’re very happy with how it’s turned out.”
Structured Large Language Model Workflows for Motivational Interviewing in Health Behavior Change: Proof-of-Concept Study
Background: Motivational interviewing (MI) is an effective approach for supporting health behaviorchange, but face-to-face delivery is resource-intensive and difficult to scale. Rule-based conversational agents (CAs) can improve access; however, their scripted interactions and limited language flexibility constrain MI delivery. While large language models (LLMs) are increasingly being used for MI coaching, their conversational fidelity and quality compared with human coaches and rule-based CAs remain understudied. Objective: This study aimed to describe the development of an LLM-based CA, Artificially Intelligent Motivational Interviewing (Aimi), orchestrated with structured workflows, and to evaluate its feasibility, conversational fidelity, and user perceptions during MI coaching interactions. Methods: We developed Aimi using structured LLM workflows designed to enhance MI fidelity. We conducted a within-participants study, where 18 adults interacted with (1) Aimi, (2) a novice MI-trained human coach, and (3) a rule-based CA during live text-based role-play coaching sessions. Transcripts were independently evaluated by an MI expert using the Motivational Interviewing Skill Code, Version 2.0 (MISC-2), to assess MI competency and fidelity. Participants completed a user experience questionnaire to provide general feedback and to assess session alliance, dialogue relevance, empathy, engagement, linguistic quality, and perceived motivation to change. Feedback from users was thematically summarized and categorized under strengths and weaknesses for each approach. Results: Aimi achieved fidelity scores comparable to those of the novice human coach and higher than those of the rule-based CA on summary metrics, including higher reflection-to-question ratios (median 0.84, IQR 0.62-0.92 vs 0.62, IQR 0.42-0.74 vs 0.25, IQR 0.17-0.38), more complex reflections (median 66.67%, IQR 46.97%-76.92% vs 50%, IQR 34.38%-61.88% vs 0.00%, IQR 0%-50%), and greater elicitation of client change talk (median 90.83%, IQR 85.89%-100% vs 73.21%, IQR 63.10%-83.19% vs 66.67%, IQR 57.86%-81.94%). User experience ratings showed no significant differences across conditions. User feedback revealed distinct strengths and limitations across the coaching interactions. Participants described Aimi’s interactions as personalized, fluid, and adaptive, though sometimes overly reflective and lengthy. The novice human coach was viewed as empathetic and supportive but slow to respond, whereas the rule-based coach was viewed as efficient and structured yet limited in depth and personalization. Conclusions: This study demonstrates the technical feasibility of structured LLM-workflows for MI coaching and their capacity to maintain conversational fidelity comparable to that of a novice MI-trained human coach. Given the role-play paradigm, single-rater coding, and small convenience sample, these comparative findings should be interpreted as exploratory. Our findings serve as a foundational baseline for the development of scalable behavior change interventions in clinical settings.
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Your family’s $300 stake in OpenAI
This story originally appeared in The Algorithm, our weekly newsletter on AI. To get stories like this in your inbox first, sign up here.
OpenAI CEO Sam Altman’s oft-discussed promise that Americans will share in the wealth AI creates was in the news again last week. On Thursday, the Financial Times reported that Altman is in talks with President Trump about giving the US government a 5% stake in OpenAI.
In some ways, Altman’s plan is old news. He wrote about a more radical version of this back in 2021, proposing that all companies above a certain valuation (not just AI companies) pay 2.5% of their market value each year into a fund that sends Americans annual disbursements. In April this year, OpenAI described a narrower proposal that closely resembles what Altman is reportedly discussing with Trump now. And the notion has broad political appeal: Senator Bernie Sanders has proposed giving Americans a 50% stake in top AI companies.
What’s the logic here? For would-be recipients, it’s twofold. First, AI learns directly from human-generated work—books, movies, art—but AI companies generally never pay the authors of that work. A free equity stake could serve as a form of belated compensation. Second, the payout could mitigate the widespread anxiety that AI will cause a collapse of the labor market (even if economists disagree) by providing a safety net.
How large a safety net is up for debate. Details of OpenAI’s latest proposal are sparse, but let’s say the government were to distribute this equity stake directly to Americans. After its funding round in March the company was valued at $852 billion, making a 5% stake in OpenAI worth about $42.6 billion today (the company is reportedly delaying its IPO until it can reach a $1 trillion evaluation, a tall order given that it’s spending heavily on data centers and still has not turned a profit). Distributing that $42.6 billion equally among the roughly 133 million American households would give each about $320 in equity. But if it were to operate like other wealth funds, the government would not give equity directly to Americans but rather let the fund grow and then share a portion of the returns with everyone, perhaps delivering a bigger payout, if and when AI companies can ever start sustainably turning a profit.
If this dividend does materialize, what’s in it for tech companies? Altman might hope the promise of payouts could help swing public opinion a bit more back toward AI companies. (A majority of Americans don’t trust companies to use AI responsibly and oppose construction of data centers in their area, and half are more concerned than excited about the increased creep of AI into their daily lives.)
But the bigger prize for OpenAI might be that the Trump administration loves making tech deals—like its equity stake in Intel and its share of Nvidia’s sales to China, among others. Staying on the administration’s good side is pretty essential for AI companies right now (just ask Anthropic). It could mean not having your models deemed a supply chain risk, or getting more help from the White House in stopping your rivals from China.
My main takeaway is that these plans currently function more as a story than a policy. Altman has been talking about some version of this idea for five years and reportedly pitched it to President Trump soon after he took office, yet there is still little indication that a concrete plan is taking shape. The more ambitious proposal from Sanders is even less likely to gain traction.
But what these plans do reveal is just how up for debate the future of AI still is. Altman drew inspiration for his plan from the Alaska Permanent Fund, which was set up in the 1970s to give Alaskans a share in oil profits. The idea was based on two premises: that oil is a shared resource, and that eventually it will run out. Altman seems happy to concede the first claim about AI. But he’d balk at the second, having promised that AI will generate extraordinary wealth for decades to come. Whether Americans ever receive a check is beside the point; the proposal’s real purpose may be to convince them that the AI boom will be large enough to share.

