Daniel Keller, a prominent figure in the cryptocurrency industry, advocates for the increasing relevance of DePin and Flux as solutions in the evolving crypto landscape.

According to Keller, as challenges in the current crypto infrastructure continue to emerge, DePin and Flux offer innovative pathways to address and potentially resolve these issues.

DePin, a decentralized infrastructure protocol, and Flux, a blockchain ecosystem focused on scalability, are positioned to become critical elements in the future of decentralized networks. Their ability to provide enhanced security, scalability, and efficiency in blockchain operations highlights the transformative potential they hold.

Industry experts agree with Keller’s insights, emphasizing the necessity for adaptive solutions like DePin and Flux in maintaining the growth and stability of blockchain systems as the sector matures.

Keller’s perspective on the imperative for responsible innovation within decentralized networks calls to mind his stance on broader ethical considerations in the digital sphere. In particular, his position aligns with earlier arguments against the celebration of deaths on social media, as detailed in Daniel Keller denounces celebration of death on social media, where he emphasized the vital balance between technological advancement and responsible discourse.

What made this alarming was not just the breach but the significance of this mishap. Data entered into a public large language model (LLM) cannot be retrieved or deleted; similarly, Samsung’s crown jewels, its semiconductor designs, were exposed.

The company responded decisively. It banned ChatGPT (registration required) and similar AI tools from company networks and devices, while simultaneously racing to build internal AI solutions that could offer the same productivity boost without compromising security.

Beyond the mistakes of an employee, this was the story of a global enterprise discovering, in real time, how AI without guardrails can become a liability.

The Gold Rush

This cautionary tale sits against a backdrop of explosive adoption. According to McKinsey, 78% of respondents are already using AI regularly in at least one business function.

From automating marketing copy to drafting legal contracts, companies see LLMs as the new productivity engine. The allure is hard to resist: faster turnaround, fewer bottlenecks and an always-on digital assistant that scales instantly.

But this speed comes at a cost. In the race to implement AI, many organizations adopt tools faster than they establish rules. Enthusiasm often eclipses governance, and the consequences can be as severe as leaking proprietary data or worse.

The Lurking Dangers

The risks tied to LLMs aren’t always obvious. They don’t appear in a dashboard or a quarterly report. Instead, they surface quietly: leaked IP, flawed legal filings or regulatory violations.

Some of the most pressing vulnerabilities include:

• Data Leakage: As Samsung discovered, employees may feed confidential information into third-party models, creating irreversible exposure.

• Hallucinations: LLMs can fabricate answers with supreme confidence. In 2023, two lawyers in New York submitted a legal brief generated by ChatGPT (registration required) that cited entirely fake cases. The court sanctioned them, and their firm’s reputation suffered a significant blow.

• Compliance Gaps: Data privacy rules such as GDPR and HIPAA require strict controls. Feeding personal or health data into an LLM could expose companies to fines or lawsuits. Italy even temporarily banned ChatGPT in 2023 over concerns about privacy and data collection.

The common thread is simple: AI amplifies both productivity and risk. Without human oversight, it is just as effective at creating vulnerabilities as it is at solving problems.

Guardrails For The Generative Age

Samsung’s ban may have been drastic, but it illustrates a fundamental truth: Enterprises need guardrails to use generative AI safely. These guardrails are not about stifling innovation; they are about ensuring that innovation doesn’t erode trust, IP or compliance.

A strong framework includes:

1. Access Control

Not every employee needs a direct line to an AI system. Limit who can use AI tools and under what circumstances. Samsung, in the aftermath of its leak, restricted input sizes to just 1,024 bytes as an emergency measure to reduce risk.

2. Data Classification

Clear rules must define what data is safe for AI input. Proprietary source code, financial records or personal information should be off-limits. This requires building a culture where employees instinctively ask, “Should I be sharing this with an external system?”

3. Red Teaming AI Systems

Much like penetration testing in cybersecurity, enterprises should red team their AI systems, intentionally stress-testing them against adversarial prompts, jailbreak attempts and data poisoning. This proactive approach reveals vulnerabilities before attackers exploit them.

4. Policy And Training

Technology cannot replace awareness. Regular training sessions, clear guidelines and scenario-based education ensure employees understand both the benefits and dangers of generative AI.

Why Humans Still Matter

Even with policies and technical safeguards, the human factor remains a decisive factor. AI cannot secure itself. Without human-in-the-loop oversight, enterprises risk overreliance on outputs that may be biased, false or legally hazardous.

The legal industry provides a stark example. Beyond the New York case, courts are now issuing standing orders requiring attorneys to disclose whether they’ve used AI in filings. Judges are signaling that human judgment is non-negotiable (registration required), no matter how advanced the tool.

AI is a partner, not a substitute. Its value maximizes only when combined with human ethics, scrutiny and responsibility.

The Competitive Edge Of Security

Securing generative AI is not just a defensive necessity; it’s a strategic advantage. Here’s why.

• Customer Trust: Clients are more likely to do business with organizations that protect their data rigorously. A 2023 PwC survey found that 85% of consumers won’t engage with companies if they have concerns about security practices.

• Investor Confidence: Boards and shareholders reward companies that balance innovation with risk management. According to a World Economic Forum report, firms with robust cybersecurity practices are more likely to outperform peers in long-term valuation.

• Regulatory Readiness: With frameworks like the EU AI Act moving closer to full implementation, compliance today prevents penalties tomorrow. The Act not only imposes fines for non-compliance but also creates new standards for “high-risk” AI systems. Companies that prepare early stand to win contracts in regulated industries, such as finance and healthcare, while laggards risk being excluded from lucrative markets.

Rounding Up

Samsung’s experience is not an outlier; it is a glimpse into the new reality of enterprise AI. The incident was avoidable, but it was also instructive: Guardrails are no longer optional.

On its own, AI offers undeniable promise. It can write, code, summarize and assist at speeds no human team can match. But without clear boundaries, it becomes a liability waiting to surface in the worst possible way, through a leak, a lawsuit or a reputational crisis.

The path forward is clear: Pair every innovation with security. For enterprises, the challenge is no longer whether to adopt AI and LLMs but how to use them responsibly.

FOR IMMEDIATE RELEASE
October 23, 2025

CHELTENHAM, United Kingdom — Following the announcement that Kadena LLC has ceased operations and the Kadena blockchain will transition to a community-led model, InFlux Technologies (Flux), a leading global decentralized technology company specializing in cloud infrastructure, artificial intelligence, and decentralized cloud computing services, has issued the following statement:

“In light of the management and development team’s departure from Kadena, the blockchain will transition to a fully decentralized, community-led initiative in which independent miners and community developers will maintain network integrity.
Kadena represents an essential idea in the Web3 space: a scalable Proof-of-Work network, and we at Flux intend to preserve this idea until it can flourish once again.
Flux vehemently supports the community during this transition; our resources are at your disposal.”

Flux has long championed open, decentralized networks that reduce single points of failure and empower independent operators. The company views this transition as a critical opportunity to reaffirm the Web3 community’s role in sustaining decentralized infrastructure.

Media Contact:
Shannon Blood
Chief Marketing Officer
shannon@runonflux.com
+1 208.216.9180

About InFlux Technologies:
InFlux Technologies (Flux) is a global decentralized technology company specializing in cloud infrastructure, AI, and decentralized computing services. Flux empowers independent operators and community-led networks to build resilient, distributed systems. For more information, visit www.influxtechnologies.com.

By Jon Stojan

Artificial intelligence is no longer a distant technology, but a strategy that defines the way companies approach business intelligence (BI) today. Whether it is predictive modeling or workforce analytics, AI can deliver once-unimaginable insights. Powered with advanced algorithms, organizations are discovering new ways to understand markets, optimize operations, and make faster, data-driven decisions.

The Rise of AI in Workflow Automation 

The most in-demand applications of AI at present are workflow automation and document intelligence. Companies are increasingly relying on these tools to manage unstructured data and eliminate inefficiencies. Flux AI, for instance, has emerged as a player in this space by providing document intelligence and custom AI agents that integrate with legacy systems.

“We’re utilizing AI to fix a problem, so to speak… to help minimize manual process, create more efficiency so that you can have those same folks doing something at a much higher level,” explained Donovan of Flux AI. His perspective highlights a growing trend: companies are not just digitizing processes but rethinking how work gets done altogether.

Human Resources Reimagined Through AI Analytics

The transformation extends beyond operations and into the workforce itself. Human resources leaders are leveraging AI to address pressing issues like attrition analysis, manager enablement, and career progression metrics. Platforms like illoominus are leading this shift, offering out-of-the-box HR analytics designed specifically for mid-market companies.

“We’re allowing you to get so much further faster… because we’ve normalized the data across these different sources, we can now get cross-journey learnings at our fingertips,” said Noelle London, founder of illoominus. Her remarks reflect how AI is enabling HR teams to not just track employee behavior, but also anticipate challenges before they escalate.

Becoming AI-First: Organizational Transformation at Scale

For some businesses, adopting AI means more than deploying new tools, which requires rethinking the foundational structure of the organization. IgniteTech offers a striking example. The company replaced 80% of its workforce in its drive to become AI-first and has since launched two patent-pending AI tools.

“We went from being worried that we were going to die if we didn’t change to becoming AI-first… Now, AI is in our DNA,” said Eric Vaughan, CEO of IgniteTech. The company’s drastic transformation illustrates how AI adoption can push firms toward bold and sometimes controversial decisions, ultimately redefining business strategy at the highest level.

Precision Forecasting in Supply Chain with Predictive AI

In volatile markets, forecasting remains one of the toughest challenges for businesses. Here, predictive AI is making a notable difference. Pecan AI has introduced its Demand Forecast tool, which identifies patterns in enterprise supply chain data that human analysts often miss. 

“We’re capturing nuanced statistical irregularities invisible to the human eye, enabling proactive and intelligent responses to demand fluctuations,” explained Zohar Bronfman, CEO of Pecan AI. Such technology allows companies to move from reactive to predictive strategies, gaining a crucial edge in industries where timing and accuracy are paramount.

AI for Property Management and Real Estate

AI’s reach also extends to property management, where both large firms and individual landlords are finding value. NILG.AI builds tools that help reduce vacancies, optimize listings, and pre-screen vendors. By lowering barriers to advanced analytics, the company enables even small investors to compete with larger property managers.

“With AI, even a small landlord can do what only large property managers could do before—optimize listings, identify trends, and match investments with the right people,” said Kelwin Fernandes of NILG.AI. His point reflects how AI democratizes access to intelligence, leveling the playing field across industries.

Final Thoughts

Across sectors, the role of AI remains constant. While once considered futuristic, it is now a practical tool delivering measurable, real-time ROI. Businesses adopting these technologies are not only keeping pace with the rapidly changing markets but also setting new standards in analytics-driven decision-making. As predictive models, HR analytics, and workflow automation gain momentum, AI is proving its worth as the backbone of business intelligence.

https://coinpedia.org/news/bitcoin-price-prediction-for-red-september-2025

Geopolitics, Inflation, and a Risk-Asset Reality Check

Global risks are piling on. Inflation in the U.S. is stuck at 3.1%, two major wars are disrupting supply chains, and trade tensions are heating up. “The contemporary state of global geopolitics perfectly positions BTC for a steep decline come September 2025,” said Daniel Keller of InFlux Technologies.

Story Highlights

• Bitcoin enters September at $108K after a 6.5% August drop, facing its historically weakest month.
• ETF outflows of $751M signal caution, while whale wallets hit a record 19,130 addresses, showing quiet accumulation.
• A 90% chance of a Fed rate cut and key U.S. economic data this month could spark major crypto market volatility.

Bitcoin is kicking off September under pressure. After a 6.49% slide in August, the leading cryptocurrency has opened the month at $108,253, staring down its historically weakest stretch of the year – a trend traders call “Red September.”

Since 2013, Bitcoin has posted an average loss of 3.77% in September, falling in eight of the past 11 years. The pattern isn’t unique to crypto; Wall Street has been plagued by September selloffs for nearly a century. 

But this year the situation is far more volatile, and traders know it.

The September Effect on Crypto 

Every year, September brings a familiar mix of selling pressure. The mood turns bearish, traders cut risks, and the market bleeds.

“The pattern is predictable: negative social media chatter spikes around August 25, followed by increased Bitcoin deposits to exchanges within 48-72 hours,” said Yuri Berg of FinchTrade. “We’re watching an entire market talk itself into a selloff based on history rather than current fundamentals.”

That fear is backed by market structure. Mutual funds lock in losses for tax purposes, liquidity shifts into bonds, and traders return from summer ready to rebalance. In crypto, these moves are amplified. 

Bitcoin trades 24/7, leverage kicks in, and whale-driven volatility often makes things worse.

Geopolitics, Inflation, and a Risk-Asset Reality Check

Global risks are piling on. Inflation in the U.S. is stuck at 3.1%, two major wars are disrupting supply chains, and trade tensions are heating up. “The contemporary state of global geopolitics perfectly positions BTC for a steep decline come September 2025,” said Daniel Keller of InFlux Technologies.

Today, BTC moves like a risk asset and that makes it vulnerable to every macro shock.

Charts Flash Red as Institutions Pull Back

Bitcoin has broken below $110K, a level that supported its rally since May. Resistance sits near $114K, while analysts are watching $103K as the next key line. A retest of $100K is possible.

ETF data shows $751M in outflows, suggesting institutional caution. At the same time, whale wallets have climbed to a record 19,130 addresses, a sign some big players are buying the dip.

• Also Read :
•   Bitcoin Price is Losing a Crucial Support Level: Time to Worry or Buy the Dip?

A Crucial Month Ahead

September is loaded with data releases and policy decisions that could shake crypto prices. Key U.S. economic updates, including jobs data, trade numbers, and manufacturing reports, will land in the first week of the month, leading up to the September 16-17 Federal Reserve meeting.

Fed Governor Christopher Waller is pushing for a 25bps interest rate cut, warning that the central bank “shouldn’t wait” for things to get worse before easing.

This is good as a rate cut could give Bitcoin and crypto markets a potential boost. The CME FedWatch Tool now shows a 90% probability of a rate cut.

For now, sentiment is shaky, history is against the bulls, and traders are bracing for volatility. Red September is here – we’ll keep you updated on Bitcoin’s next move. 

A new wave is reshaping how physical infrastructure and digital services interact. Decentralized physical infrastructure networks (DePINs) are connecting real-world assets through blockchain, while autonomous AI agents are taking over decision-making and coordination. When brought together, they can lay the groundwork for digital economies that can operate and adapt without human oversight.

Understanding What DePIN Is

DePIN refers to networks where individuals or organizations supply physical resources (e.g, wireless hotspots, energy devices or storage hardware) and receive tokens in return. The blockchain records every contribution and transaction, making the system transparent and verifiable. Smart contracts also automate the rules, so there’s no need for typical centralized authorities.

Projects like Helium show how DePINs can build robust, distributed networks. A centralized provider does not own these networks; instead, it relies on community participation, with incentives managed through tokens and automated protocols. But where do AI agents come in?

The Role Of AI Agents

AI agents are software programs that can analyze data, learn from patterns and make decisions on their own. In the context of DePIN, they handle tasks that would otherwise require human intervention. For example, an AI agent can monitor energy usage across a decentralized grid, predict demand spikes and adjust distribution accordingly. In a logistics network, agents can reroute shipments in real time to avoid delays or optimize storage allocation.

These agents interact directly with smart contracts and on-chain data. They can trigger maintenance, rebalance resources or even propose changes to network rules if they detect inefficiencies or security risks.

How DePIN And AI Agents Work Together

The convergence of AI agents and DePIN is more than a technological coincidence. If AI agents are to be truly autonomous, they need access to decentralized, programmable infrastructure for compute, storage, mobility and more.

DePIN provides the infrastructure and economic incentives while AI agents supply the intelligence and automation. When combined, these systems can coordinate logistics, energy or computing resources with minimal human input.

Let’s take a decentralized energy grid, for example. Community members install solar panels and batteries, then connect them to the network. The blockchain tracks each device’s output and distributes rewards. AI agents can come in to monitor weather forecasts, energy prices and local demand. If a storm is approaching, the agents can instruct batteries to store extra power. If prices spike, they can sell energy back to the grid at the optimal time.

In logistics, DePIN can connect independent couriers and warehouses. AI agents handle route planning, inventory management and real-time adjustments, all based on live data. Payments are settled automatically through smart contracts, reducing overhead and delays.

Technical And Economic Impact

The combination of DePIN and AI agents introduces new protocols and standards. The Modular Context Protocol (MCP) is one example, allowing AI agents to access decentralized compute resources on demand. Confidential computing and decentralized storage protect sensitive data, while token incentives keep participants engaged.

This model changes how value is created and distributed. Instead of relying on trust in a central provider, participants trust the code and the network. AI agents help maintain fairness and efficiency by adjusting rewards, detecting fraud and responding to network conditions.

Challenges And Limitations

Despite the potential, challenges remain. Technically, DePINs face scalability issues. Some blockchain networks can be slow and costly, limiting the speed of AI-driven transactions. AI agents also require significant computational power, which can strain decentralized systems. Solutions like layer-2 scaling aim to address this, but adoption is still evolving.

On the ethical side, decentralized AI raises concerns about accountability. If an AI agent makes a flawed decision, such as misrouting a delivery, it’s unclear who is responsible in a trustless system. Bias in AI algorithms could also cause inequalities, especially if training data lacks diversity. Regulatory uncertainty adds another layer of complexity. Governments are still grappling with how to regulate blockchain and AI, and conflicting laws could hinder global adoption of DePIN-AI systems.

Security is also a significant concern, as attackers can target both physical devices and digital protocols. Interoperability between different devices, networks and AI models is another technical hurdle. Governance remains a complex issue, since AI agents must reflect the interests of the community without becoming centralized themselves. Finally, data privacy is critical as well, especially when sensitive information is involved.

What’s Next?

Several projects are testing these ideas. Arkreen is building a decentralized energy network where AI agents manage device coordination and energy trading. In decentralized computing, protocols like Akash let AI agents allocate processing power across a distributed network of contributors.

The field is moving quickly. As standards improve and adoption grows, expect more industries to explore these models for everything from supply chains to public infrastructure.

The future of DePINs and AI agents is bright but requires innovation to overcome limitations. Advances in layer-2 solutions and sharding will likely make DePINs faster and cheaper, enabling AI agents to handle larger-scale tasks. Improved edge AI will further reduce latency, allowing real-time decisions in complex systems like autonomous vehicle networks. Over time, we could see a fully autonomous digital economy where AI agents manage global infrastructure logistics fleets, energy grids or compute marketplaces without human intervention.

This vision also holds promise for global inclusivity. In regions with limited access to centralized infrastructure, DePINs can provide resources like internet connectivity or computing power, while AI agents optimize their use. For example, a rural community could use a DePIN like Helium for decentralized Wi-Fi, with AI agents managing bandwidth allocation to prioritize critical tasks.

Conclusion

DePIN and AI agents are changing how physical and digital resources are organized. By combining decentralized ownership with autonomous coordination, they can offer a practical path to infrastructure that can run itself, adapt to new conditions and reward those who contribute. This approach is already being tested in sectors such as energy, networking and computing, and will likely expand in scope as the technology matures.

Companies like Alignable, Bill.com, InFlux Technologies, and OpenPhone are among those driving AI adoption for small business ventures to streamline everyday tasks. They offer tools that integrate seamlessly into existing workflows, proving that AI can be both powerful and approachable. As Daz, CEO of Flux AI at Influx Technologies, puts it: “You’ve got to utilize and jump on AI right now so you don’t get left behind. It’s a productivity enhancement—three, five, even ten times faster than before.”

Small businesses across industries, ranging from professional services to home improvement, are embracing artificial intelligence at an unprecedented pace. What was once a “nice-to-have” tool is quickly becoming a competitive necessity. Automation now underpins everything from client communication to operational efficiency, helping lean teams punch far above their weight.

What Are AI Agents and Why Do They Matter?

At the core of the massive shift toward AI integration is the use of AI agents. They are digital workers capable of executing multi-step tasks with minimal oversight. AI agents can effectively manage customer inquiries, schedule appointments, run marketing campaigns, and supervise day-to-day operations. For many small business owners, this technology is like a key to freedom from repetitive but critical tasks, which means more time to focus on a strategy that will help their businesses grow faster.

Case Studies of AI in Action

The impact of AI integration is measurable. Some businesses are now launching fully functional websites within hours, while others are utilizing AI agents to answer up to 90% of missed calls. This is helping them capture leads that might otherwise have slipped away. Finance teams are reclaiming as much as two workdays per week, thanks to automated reporting and reconciliation. Affordable “agents for hire” models are also emerging, bringing enterprise-grade automation into reach for small and mid-sized businesses that once considered such tools out of budget.

Privacy and Data Security Considerations

With greater capabilities comes greater responsibility. While small businesses are increasingly embracing the power of AI, they must also ensure that these tools handle their sensitive data securely. This is especially important for industries like healthcare and finance, where compliance and confidentiality are paramount. The rise of private AI deployments among small and mid-size businesses shows a growing awareness that convenience should not come at the cost of security.

From Support to Strategy

As businesses streamline their repetitive and critical multi-step tasks with AI agents, experts have already predicted future advancements. According to them, these digital workers will evolve from being support tools to complete business assistants, prioritizing tasks, qualifying leads, and making strategic recommendations in real time. This evolution could render some outdated outsourcing models obsolete, empowering businesses to exercise control over their operations while reducing reliance on third parties.

Industry Innovators Leading the Charge

Companies like Alignable, Bill.com, Influx Technologies, and OpenPhone are among those driving AI adoption for small business ventures to streamline everyday tasks. They offer tools that integrate seamlessly into existing workflows, proving that AI can be both powerful and approachable. As Daz, CEO of Flux AI at Influx Technologies, puts it: “You’ve got to utilize and jump on AI right now so you don’t get left behind. It’s a productivity enhancement—three, five, even ten times faster than before.”

Innovate or Stay Behind

Considering the power and impact of AI agents in small businesses, it can be said that ventures aiming to remain competitive in the present market should start experimenting with AI. Beginning with one or two AI-driven processes can deliver quick wins and build confidence. The key here is to treat AI as a partner at the workplace and not as a replacement for human teams. This technology should be utilized as a tool to enhance creativity, efficiency, and growth potential.

Data Stewardship and Workforce Training

We foster a culture of data stewardship to empower our workforce to adopt responsible data storage practices. This can be done by establishing clear standards for data retention and reducing redundant data storage practices.

Additionally, encouraging employees to prioritize cloud storage solutions that utilize energy-efficient, sharded storage environments ensures that less frequently accessed data is stored in a cost-effective and energy-efficient manner.

Furthermore, implementing regular data audits can empower employees to practise responsible data storage by making them aware of irrelevant or obsolete datasets that need to be removed from the cloud.

Extensive workforce training on the environmental impact of data storage can also incentivize employees to practice sustainable storage methods.

-Daniel Keller, CEO & Co-founder, InFlux Technologies

For businesses worldwide, unchecked data growth is no longer just a storage issue; it’s a direct hit to the bottom line. 

The spiraling costs of cloud services and the energy required to power data centers represent a significant and often overlooked operational expense. 

This “digital waste” is a direct drain on resources that could be better invested in innovation.

The most forward-thinking leaders are tackling this not as an environmental issue alone, but as a matter of operational excellence. 

They are empowering their teams to adopt responsible data practices, treating digital resources with the same discipline as financial ones to improve efficiency and cut costs.

To learn these practical, high-impact strategies, the Techronicler team consulted a panel of global business and tech leaders and asked for their expert advice:

“How do you influence or empower your workforce to implement responsible data storage practices to minimize resource strain and improve sustainability?”

Their responses offer a clear, actionable guide for any organization looking to reduce digital waste, boost its bottom line, and enhance its sustainability credentials in a competitive global market.

Read on!

Data Ownership Shift Reduces Storage by 30%

A few years ago, we hit a wall with our data lake—storage costs were ballooning, and no one could find anything useful anymore. That was the wake-up call. We launched a “Data Stewardship Week” where teams were given visibility into their data footprints and challenged to archive or delete unused datasets.

What made it stick wasn’t the cleanup—it was shifting ownership. We embedded lightweight dashboards that show real-time storage impact and tagged stale datasets with the owners’ names.

Suddenly, people started asking, “Do we still need this?”—which had never happened before.

What I learned is that responsible data storage isn’t a policy issue—it’s a cultural one. If you want people to act sustainably, give them context and agency.

We didn’t shame teams or mandate deletion; we showed the financial and environmental cost and invited them to take the wheel.

And it worked.

We reduced storage bloat by 30% in six months and achieved improved downstream data quality. The key was empowering people to see the impact of their choices, not just enforcing rules from the top.

Matt Mayo

Owner, Diamond IT

Quarterly Clean-Up Sprints Transform Digital Asset Management

What worked well for us was shifting the conversation from compliance to clarity. I remember walking through a shared network drive with one of our newer employees and realizing how little context there was about which files were important, outdated, or duplicative.

So we built a simple rule: if you create it, you own it.

That means labeling files clearly, setting expiration dates where possible, and flagging anything that could be archived. Once people understood that responsible storage wasn’t just about saving space but about making data useful and accessible, they started to take more initiative.

One practice that stuck was hosting quarterly “data clean-up sprints.”

We’d block off a couple of hours and treat it like a team activity—sort of like spring cleaning, but for our digital assets. I’d join in too, which helped reinforce that this wasn’t busywork—it was part of how we worked smarter.

Over time, this reduced our cloud storage requirements and helped our systems run more efficiently, particularly our backup and disaster recovery processes. And by tying it to sustainability goals, people felt like they were contributing to something bigger than just tidying up files.

Brian Fontanella

Owner, Keystone Technology Consultants

Human-Centered Approach Cuts Retrieval Time by 90%

I once saved a driver’s job just by optimizing how we stored their ride data — and it ended up saving us hours of admin time each week. At Mexico-City-Private-Driver.com, we don’t manage petabytes of cloud files like a tech company might, but even small inefficiencies in storing GPS logs, customer confirmations, and WhatsApp communications can become a resource drain.

To tackle this, I took a very hands-on, human-centered approach. I started by showing our team why it mattered: I shared real numbers—like how disorganized image folders were doubling our upload times when customers requested proof of pickup or billing clarification. Then, I implemented lightweight processes that felt more like empowerment than rules: for example, naming conventions that included date-client-code, and archiving old trips monthly using automated scripts.

But what really shifted the culture was creating a shared goal around sustainability. I showed how cutting down on unnecessary cloud bloat could reduce our carbon footprint as a local business — and that struck a chord. Now, every team member from dispatch to finance treats data hygiene like a shared responsibility.

The result? We reduced our average trip file retrieval time from 5+ minutes to under 45 seconds — while also cutting our storage needs by nearly 40% within the first three months.

Martin Weidemann

Owner, Mexico-City-Private-Driver

Quarterly Digital Decluttering Boosts Productivity and Sustainability

At Tall Trees Talent, one of the most effective and sustainable changes we’ve implemented is a quarterly Digital Decluttering.

It’s a dedicated time for teams to audit, clean, and organize their digital workspaces, from shared drives and email archives to CRM entries and outdated resumes.

The idea was simple: give everyone one uninterrupted hour each quarter to focus solely on responsible data management, aka, a company-wide pause to clean house. What made it stick was how we framed it: not as a chore, but as a way to reduce digital waste, improve system performance, and take real action toward our sustainability goals.

We provided light guidelines to help teams know what to delete, what to archive, and what to flag, but we also let departments personalize the process. Recruiters, for instance, focus on archiving expired candidate profiles, while admin teams clean up duplicate files and old documentation.

The results have been measurable. Our cloud storage usage has dropped, CRM load times have improved, and employees report a noticeable boost in productivity and focus after their systems are streamlined. Just as importantly, the initiative fostered a sense of ownership and personal pride in maintaining a clean digital footprint.

Jon Hill

Managing Partner, Tall Trees Talent

Automated Rules Create Better Data Storage Habits

Telling people to clean up their data is like asking teens to clean their room. They will pretend to, then shove everything in a corner. So I rewired the system. Instead of long-winded policies, we built scripts. Any file untouched for 45 days gets moved to a purge-ready folder. If no one flags it in a week, it vanishes. Poof. Gone. People learned fast.

There is no seminar. No Google Doc. Just an automated rule that does the shoving. And guess what? That pressure created habits. Folks now rename files on upload and add dates to folders. Data goes from digital landfill to searchable index. That kind of shift saves energy. Literally. We slashed cloud storage costs by 22 percent last quarter.

To be fair, the tech did the heavy lifting. But the trick was making deletion normal. Not scary. People care more when the stakes are silent. Storage rules should be quiet threats, not loud guidelines. Let the system do the teaching. Let the users adapt or lose their files.

David Struogano

Managing Director, Mold Removal Port St. Lucie

Integrate Sustainability Goals into Performance Reviews

Empowering workforce sustainability starts with education and clear policies, much like how effective grant programs require comprehensive training for staff to ensure proper implementation.

We establish data retention schedules that automatically purge unnecessary files, similar to how smart nonprofits create systematic record-keeping protocols that satisfy funder requirements while minimizing storage bloat. Regular audits help identify redundant data and inefficient storage practices, mirroring the evaluation processes that successful grant-funded organizations use to optimize resource allocation.

The key is making sustainability metrics visible through dashboards that track storage usage and environmental impact, giving employees concrete feedback on their conservation efforts just like how grant reports demonstrate measurable outcomes to funders.

We also incentivize responsible practices through recognition programs and professional development opportunities, understanding that behavioral change requires both accountability and positive reinforcement.

Most importantly, we integrate sustainability goals into performance reviews and team objectives, ensuring that environmental stewardship becomes part of our organizational culture rather than an afterthought. That’s how impactful grants fuel mission success.

Ydette Macaraeg

Part-time Marketing Coordinator, ERI Grants

Lead by Example to Inspire Sustainable Practices

I inspire my team by setting a strong example through my own actions. This means not only talking about sustainable practices but also incorporating them into my own daily routine.

By showing my team how easy and beneficial it can be to implement responsible data storage practices, they are more likely to adopt these habits themselves.

I regularly educate my team about the importance of sustainable resource usage and how their actions can make a positive impact on the environment.

This includes discussing the benefits of using renewable energy sources and reducing single-use plastics in our office.

Zach Shepard

Principal, Braddock Investment Group Inc

Raising Awareness and Providing Tools and Solutions

Empowering a workforce to adopt responsible data storage starts with clear education and practical policies that link individual actions to broader sustainability goals.

I focus on raising awareness about the environmental impact of excessive data storage, such as the energy consumption of data centres. Making this connection helps people see that deleting unnecessary files or archiving old data is not just housekeeping but directly reduces carbon footprint.

Providing easy-to-use tools and guidelines also matters. For example, setting quotas on storage, automating the deletion of redundant files, and encouraging cloud solutions with strong sustainability credentials. Leadership must lead by example, regularly reviewing data use and rewarding teams that maintain efficient practices.

Andy Callaghan

Founder, Jammed

Data Stewardship and Workforce Training

We foster a culture of data stewardship to empower our workforce to adopt responsible data storage practices. This can be done by establishing clear standards for data retention and reducing redundant data storage practices.

Additionally, encouraging employees to prioritize cloud storage solutions that utilize energy-efficient, sharded storage environments ensures that less frequently accessed data is stored in a cost-effective and energy-efficient manner.

Furthermore, implementing regular data audits can empower employees to practise responsible data storage by making them aware of irrelevant or obsolete datasets that need to be removed from the cloud.

Extensive workforce training on the environmental impact of data storage can also incentivize employees to practice sustainable storage methods.

Daniel Keller

CEO & Co-founder, Flux

On behalf of the Techronicler community of readers, we thank these leaders and experts for taking the time to share valuable insights that stem from years of experience and in-depth expertise in their respective niches.

https://www.forbes.com/councils/forbestechcouncil/2025/07/09/competitive-edge-17-affordable-ways-smbs-can-leverage-ai/?utm_campaign=Social%20Member%20EPs&utm_content=338119573&utm_medium=social&utm_source=linkedin&hss_channel=lcp-11075183

Lacking deep pockets, in-house expertise and expansive datasets, small and midsize businesses may believe they’re shut out of the AI revolution. However, AI isn’t out of reach for SMBs—in fact, it can provide an invaluable competitive edge. A growing range of accessible, low-cost AI tools can help even lean teams boost efficiency, uncover insights and improve the customer experience—without breaking the budget.

Below, members of Forbes Technology Council detail some specific, affordable ways smaller businesses can leverage AI to compete with larger companies. Read on to learn about practical AI applications that can power smarter, faster and more agile operations at any scale.

1. Tapping Into Broader Knowledge Bases

AI extends the reach of small teams, allowing SMBs to “go broad” by tapping into knowledge beyond their core as needed. Even small teams can access capabilities once reserved for larger organizations. Think of a one-person HR staff building training content, exploring new tech solutions without an in-house IT team, or a sales leader supplying a marketing asset without an in-house creative team. – Emily Lewis-Pinnell, Evaila

2. Transcribing Essential Conversations

Voice AI transcription services transform how SMBs handle meetings and customer calls. For the cost of a coffee subscription, tools like Otter.ai capture every conversation, making them searchable and actionable. SMBs can offer “recorded for quality” like big corporations but actually use the data. Sales calls become training materials, and client meetings become searchable records. – Marc Fischer, Dogtown Media LLC

3. Developing A Hyperlocal Presence

SMBs can use AI tools to automatically monitor, analyze and update local listings, reviews and keywords—helping them rank higher in “near me” searches. This hyperlocal presence drives foot traffic and online conversions without expensive SEO agencies, making it a smart, low-cost way to win visibility where larger players often overlook personalization. – Jagadish Gokavarapu, Wissen Infotech

4. Implementing Customer Service Chatbots

SMBs can use AI-powered chatbots trained on internal data for customer service, lead generation and FAQs. These highly cost-effective AI workers can automate responses, handle FAQs and qualify leads 24/7. This boosts efficiency, enhances customer experience and frees up staff—helping SMBs scale support and sales like larger companies, but at a fraction of the cost. – Henry Patishman, Regula

5. Automating Routine Tasks

SMBs can implement AI-powered automation to handle routine tasks like customer support, lead scoring or document processing. This allows smaller teams to increase efficiency, redirect human capital to strategic work and deliver enterprise-level performance without the enterprise-level cost. – Chris Brown, Intelygenz, a VASS Company

6. Scaling Data Management And Analysis

When working on a tight budget, it makes sense to use AI for what it does best, which is currently sifting through data and automating repetitive tasks. SMBs can utilize AI to scale data management and analysis, even without a huge team of in-house analysts. AI tools can help collect, parse, manage and retrieve insights from data, enabling smaller companies to compete with larger ones. – Julius Černiauskas, Oxylabs

7. Building Intelligent Workflows

SMBs can build a strong competitive advantage by automating business processes with no-code/low-code AI platforms. These tools enable intelligent workflows across IT, marketing, customer service and supply chain—boosting speed, efficiency and insight. This allows SMBs to compete effectively with larger players without massive budgets. – Stefan Gogoll, andagon Holding GmbH

8. Enhancing Niche Expertise

SMBs can leverage AI by combining affordable models like GPT with their niche expertise to offer differentiated solutions that stand out in the market. This fusion of deep domain knowledge and accessible AI enables SMBs to outperform larger firms that lack specialized insight, even with more tech resources. – Rodney Drake, VALID Systems

9. Automating Reporting

Use AI to automate routine reporting and data analysis. It is a low-effort way to reduce errors, save time and focus on real problem-solving. I have seen small teams gain visibility and uncover hidden inefficiencies just by automating reports. For smaller teams, that clarity is a game-changer. You don’t need a big team to get big results when automation’s doing the grunt work. – Bill Rokos, Parsec Automation

10. Analyzing Competitors

For competitive edge, build a bot to scrape and analyze your rivals’ reviews, find common complaints, and suggest product tweaks—it helps you fill market gaps fast. Train a small LLM to craft thank-you notes in your brand voice, then use tools like Calligrapher.ai to turn them into handwritten-style messages—it boosts loyalty at scale. – Priya Mohan, KPMG

11. Creating Personalized Email Marketing Campaigns

AI-driven email marketing tools enable SMBs to create personalized campaigns on a large scale. Small businesses gain a competitive edge through affordable tools that analyze customer behavior to deliver targeted content, which boosts both engagement and sales. – Saket Chaudhari, TriNet Inc.

12. Deepening Customer Relationships

One of the most significant expenditures for big enterprises is customer outreach. AI has set the stage for analyzing your existing customers and creating engaging marketing campaigns to target your niche audience. SMBs can leverage AI features from providers like GoDaddy.com, HubSpot and Mailchimp to deepen customer relationships on par with, or even better than, most enterprises. – Ashay Satav, eBay

13. Building Function-Based AI Agents

The concept of a “one person unicorn”—a startup valued at over $1 billion, run by one person—is gaining traction as AI becomes more capable. SMBs can benefit from the same capabilities that would enable a single person to leverage AI to handle all business functions. An affordable way to get started is to use a platform to build AI agents or multiagent systems for all possible business functions. – Michael Connell, Enthought

14. Minimizing Administrative Overhead

Automate your processes. This is pretty basic, but SMBs cannot afford to lose time to repetitive tasks and poor office productivity. Through agentic solutions like assistants and AI workflows, SMBs can drastically minimize administrative redundancy and overhead, saving them countless hours annually. – Daniel Keller, InFlux Technologies Limited (FLUX)

15. Generating High-Quality Content

SMBs can brilliantly compete by leveraging AI for hyper-efficient content creation. Affordable AI tools now generate high-quality marketing copy, social posts and even design ideas, democratizing compelling storytelling. This empowers lean SMB teams to amplify their brand voices and engage wider audiences effectively, leveling the playing field with larger competitors. – Anil Pantangi, Capgemini America Inc.

16. Analyzing Customer Feedback

One affordable and often underused AI strategy for SMBs is using AI to automatically analyze customer reviews and feedback across platforms. With minimal setup, AI can surface trends, flag pain points and suggest product or service improvements. This real-time voice-of-the-customer intelligence helps SMBs adapt faster than larger competitors and stay hyper-relevant. – Sandipan Biswas

17. Surfacing Key Insights

A practical and affordable way SMBs can stay competitive is by using AI to turn internal conversations into content gold. Record a sales call or support chat, then run it through free AI tools to pull out key insights, generate blog posts or create FAQ material. It’s a clever way to stretch resources—it’s like hiring a mini content team without the payroll. It just takes a bit of creativity and curiosity. – Umesh Kumar Sharma

By Nicholas Kitonyi

In this Q&A, Daniel Keller, co-founder and CEO of the decentralised compute marketplace InFlux Technologies, shares insights on the potential impact of decentralised computing in AI and how decentralised AI can be used to improve the healthcare sector.

Quick take:

• Keller is building a peer-to-peer decentralised cloud network, Flux, aimed at ensuring access and control of Web3 platforms remain decentralised.
• According to Keller, by leveraging Federated Learning, decentralised AI can be used to protect patient privacy and ensure accurate, high-quality medical outputs.
• Keller also believes the biggest challenge to integrating decentralised AI in healthcare systems is data migration.

Artificial intelligence (AI) is one of the biggest tech breakouts for the current decade, thanks to major breakthroughs in generative AI, driven by the emergence of applications like ChatGPT, DeepSeek, Meta AI and Google’s Gemini.

However, while such breakouts have been encouraging from a business perspective, they have been met by equally sceptical views relating to user privacy and the security of data. They have also faced criticism from the crypto community for the centralised economic models that only focus on rewarding the big corporations building the apps, not to mention the amount of power required to power such apps.

This is where Daniel Keller, the co-founder and CEO of the decentralised compute marketplace InFlux Technologies, spotted a weakness in the entire process of building, training and running AI agents.

“We rebranded to InFlux Technologies in 2018, and the focus shifted to building a peer-to-peer decentralised cloud network: Flux,” Says Keller, whose previous crypto project, Zel Technologies, focused on custodianship with the desktop-based Zelcore crypto wallet.

His company rebranded to InFlux Technologies, switching focus to building a peer-to-peer, decentralised cloud network.  Keller believes his company serves an important role in ensuring infrastructure access and control remain decentralised. He also expects that decentralised computing will take over cloud computing, especially in AI workloads.

Keller, who also has a background in healthcare, shared how AI could be used to improve healthcare systems without reinforcing existing biases in clinical data. 

“By incorporating fairness constraints—terms added to training objectives that penalise models whenever error rates differ too much between ethnic or cultural differences in patients—into the model logic, so that no patient is treated unequally,” he said, adding that the main challenge to integrating decentralised AI in healthcare systems is data migration.

1. Briefly, tell us about your journey in Web3 and what sparked your interest in crypto overall.

Well, I co-founded Zel Technologies a few years back, and we focused on custodianship with the Zelcore crypto wallet for desktop. We rebranded to InFlux Technologies in 2018, and the focus shifted to building a peer-to-peer decentralized cloud network: Flux. 

What sparked my interest in crypto initially was two-fold. Firstly, the ridiculousness of paying someone else to custody my money was a huge draw of crypto. I mean the very idea of you walking into a bank and asking someone permission to access your own money is ludicrous to say the least. 

Secondly, our money isn’t even worth the paper it’s printed on because the more of it they print, the more value it loses. Bitcoin is deflationary, as it has a fixed supply, meaning its value will only increase over time. Learning this, I realized that this is an asset class that I had to be involved with.

2. What is the most important role that InFlux Technologies plays in Web3, especially in decentralised AI?

Ensuring that infrastructure access and control remain decentralized. When participation is not only open but incentivized, especially at the hardware level, censorship is prevented. A censorship-free compute network enables faster, more innovative, and ultimately more resilient development. 

3. How do you see decentralised compute fitting in in the wider cloud computing and AI sector? Does it have a future?

I see decentralized compute taking over cloud computing, especially for AI workloads. So, not only does it have a future, it IS the future. Current centralized cloud computing for AI development consumes excessive computational power, resulting in constant over- or under-provisioning and the generation of massive e-waste. 

Decentralized computing enables more focused and local processing, ensuring that resources aren’t wasted and computations are executed closer to the source of the network. Additionally, decentralized compute can massively extend hardware lifespans as unused bandwidth is harnessed from existing GPUs instead of exclusively from brand-new ones. 

4. How can developers ensure AI systems used in healthcare improve patient outcomes without reinforcing existing biases in clinical data?

By incorporating fairness constraints—terms added to training objectives that penalize models whenever error rates differ too much between ethnic or cultural differences in patients—into the model logic, so that no patient is treated unequally. 

5. How can decentralised AI models be leveraged to protect patient privacy while still maintaining high-quality, collaborative medical insights across institutions? Does InFlux address this challenge in any way?

Federated Learning models can be leveraged to protect patient privacy and ensure accurate, high-quality medical outputs. These models would be hosted across multiple devices used within the same medical institution, all trained on local data, but without sharing the data. 

Therefore, instead of the data being centralized in a single location, each medical device trains a local model using its own data, with all models being centrally coordinated but decentralized in their training and data storage. 

6. One of blockchain’s biggest challenges relates to fragmentation and a lack of interoperability between chains. What are the key technical and operational challenges in integrating decentralised AI systems into existing healthcare infrastructure, particularly in terms of interoperability, model validation and real-time clinical decision support?

I would say that a lack of interoperability between chains is actually a huge misconception within distributed ledger technology. Design principles of modularity, composability, and abstraction are increasingly built into Blockchain architecture to enable seemingly isolated networks to interoperate in very complex ways. 

However, the biggest challenge of integrating decentralized AI into existing healthcare systems—both technically and operationally—regarding model validation and real-time support, is data migration. Legacy healthcare systems rely on heterogeneous data sources—from EHRs to medical imaging—that use different formats and schemas for annotation, making migration to a decentralized AI network complicated, to say the least. 

Furthermore, decentralized AI requires in-memory processing where data remains on local nodes. For large, low-latency data migrations, the nodes in the decentralized AI framework must be synchronized for model training, which can be very challenging to accomplish.