Canada: Cryptocurrencies Need Regulation to Prevent Money Laundering 3886

Canadian House finance committee wants the government to dramatically change the way it regulates cryptocurrencies to prevent their use in money laundering.

A cryptocurrency like Bitcoin is a form of electronic cash that’s become increasingly popular for making secure online transactions. Cryptocurrencies are attractive to money launderers because they’re generally anonymous and difficult to trace.

The committee is recommending how the government should regulate cryptocurrencies as part of its review of the Proceeds of Crime (Money Laundering) and Terrorist Financing Act (PCMLTFA), which at least one parliamentary committee is required to undertake every five years. The committee held 18 meetings for the purpose of the review, which it started in February. More than 70 expert witnesses from the field of finance contributed to it.

The committee recommended three significant ways the government should monitor cryptocurrencies.

First, it said exchanges when cryptocurrency is converted from fiat currency should be regulated. (Fiat currency is legal tender, such as the Canadian dollar.) In so doing, the entity conducting the exchange is considered a money-service business. In Canada, such businesses must follow strict financial-reporting guidelines in compliance with the PCMLTFA. The committee’s suggestion aligns with the department of Finance’s proposed amendments to the PCMLTFA, which were published in the Canada Gazette in June.

Second, the committee recommends that cryptocurrency exchanges require a licence, something other jurisdictions such as New York state have already done. In the witness-testimonials part of the report, the committee cites suggestions by financial adviser IJW & Co. and law firm Durand Morisseau LLP, both of which submitted a hefty 56-page joint brief.

“They further explained that in the absence of some degree of regulatory oversight, cryptocurrency transactions may be used by parties to swiftly move large amounts of wealth across borders, and that regulating (exchanges of fiat currencies for cryptocurrencies) would address the (anti-money-laundering) concerns of the cryptocurrency space,” says the report.

The committee’s final recommendation is that government should regulate crypto-wallets, which hold cryptocurrencies, so suspicious purchases can be traced more easily and police can track hacking or financial crime.

While the suggested changes are a small part of the committee’s review of the PCMLTFA, the government is required to table a response to the committee’s recommendations in the House of Commons within 120 days.

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Hoonartek Launches ClearView – An Agentic Decision Layer That Activates the Data Estate and Replaces SaaS Bloat 678

Hoonartek today launched ClearView, an agentic decisioning layer built for enterprises that have invested in modern data platforms but have yet to translate that investment into autonomous, business-driven execution.

Most large enterprises face a common challenge: a mature data estate—lakehouse, cloud warehouse, and years of engineering—combined with a growing stack of point SaaS products, each solving narrow decisions in isolation. The result is rising license costs, fragmented ownership, and AI that operates alongside the business rather than within it.

Hoonartek’s ClearView is designed to change that equation. By treating business decisions as the primary unit of design, it deploys autonomous agents directly above the existing data estate—activating it for real-time decisions while systematically reducing reliance on fragmented SaaS tools.

“Enterprises have already built the data foundation. ClearView is what finally turns it on—agents that execute real business decisions, traceable from intent to outcome, without adding another SaaS layer.” — Peeyoosh Pandey, CEO, Hoonartek

This shift is resonating at the CFO and CDO level, where SaaS rationalization and AI activation are converging into a single strategic priority.

“The biggest gap in enterprise AI hasn’t been technology—it’s been the operating model for how decisions are made at scale. ClearView addresses that directly on top of existing infrastructure.” — Rupinder Bhamra, Former Corporate CTO, MSCI

“Enterprises don’t fail at AI because of bad models. They fail because no one connected the data platform to decisions. ClearView closes that gap.” — Dejan Deklich, Former CTO, Aisera

ClearView operates across three layers: a decision governance layer defining agent authority; RealizeAI, Hoonartek’s AI factory for scaling ML use cases; and BlueFoundry, the execution engine translating business intent into governed agentic workflows. Every decision is traceable from definition to outcome—built-in, not bolted on.

The platform is live across financial services, telecom, and manufacturing. Hoonartek was recognized at the NASSCOM Inspire Awards 2026 for AI Service Excellence.

About Hoonartek

Hoonartek is a global data and AI solutions company with over 15 years of experience and 250+ enterprise deployments across BFSI, telecom, manufacturing, and pharma. The company partners with Databricks, Google Cloud, and Ab Initio to help enterprises activate their data estate and scale AI-driven decision-making.

Instructure Delivers on Its Agentic AI Promise with the Launch of IgniteAI Agent 1739

New agentic capabilities extend IgniteAI’s open, privacy-first infrastructure, prioritizing educational outcomes and human connection while automating complex workflows at scale

Instructure, the leading learning ecosystem and maker of Canvas LMS, today announced the launch of IgniteAI Agent, initially in the United States and Latin America, marking a major milestone in the company’s vision to serve as the trusted, long-term partner for the future of learning. Powered by Amazon Web Services (AWS), the Agent expands Instructure’s IgniteAI suite to move beyond isolated tools. It provides a secure, transparent workflow capability that helps institutions navigate the shift toward agentic AI, in which technology orchestrates time-consuming, low educational value tasks to amplify human potential. Visit Instructure’s website to learn more about IgniteAI Agent and visit the Artificial Intelligence Supplement site to review the applicable terms.

IgniteAI Agent builds on this foundation by enabling technology to perform complex, multi-step operations on behalf of educators and administrators while preserving institutional control, transparency and trust. The result is less time spent navigating systems and more time focused on mentoring students, delivering feedback and supporting meaningful learning experiences.

“When we launched IgniteAI, we promised an ecosystem where AI works for educators, not around them,” said Shiren Vijiasingam, chief product officer at Instructure. “IgniteAI Agent is the realization of that promise. It moves us beyond generic content creation to true agentic support that can be opted into to securely orchestrate time-saving workflows while educators stay in charge of educational outcomes.”

Early adopters are already applying these capabilities in real-world teaching and instructional design workflows.

“For us, the power of IgniteAI and the Agent isn’t theoretical,” said Brandon Mitchell, Director of Instructional Design and Technology at Hinds Community College. “We’re using it right now to build modules, design pages, clean up accessibility, and speed up content creation. And we’re excited to keep testing and pushing the boundaries, because the potential is enormous.”

Delivering the Agentic Future of Teaching and Learning

IgniteAI Agent is part of Instructure’s IgniteAI suite and is powered by Amazon Bedrock, a platform on AWS for building generative applications and agents. The Agent helps automate routine tasks such as rubric generation, content alignment and discussion reviews. This frees educators to focus more on mentoring, feedback and meaningful learning experiences.

IgniteAI Agent extends these capabilities by enabling AI to carry out end-to-end workflows across Canvas. With a single prompt, educators can now initiate and coordinate complex actions, such as creating and organizing course modules, adjusting all due dates, or seeking out complementary content matched to your course materials, that previously required multiple clicks, tabs and manual steps.

Designed as an open, extensible agent for education, IgniteAI Agent is being built to work not only across Instructure products but alongside trusted partner technologies already used by institutions. Instructure is actively collaborating with partners across the broader edtech ecosystem to enable the Agent to securely embed partner capabilities directly into an educator’s workflow, allowing them to leverage those capabilities more frequently while minimizing friction.

Trust, Transparency and Institutional Control by Design

IgniteAI Agent is governed by the same privacy-first framework that underpins IgniteAI:

  • Closed-loop architecture: AI interactions occur within the institution’s environment and customer data is not used to train external models
  • Strict opt-in controls: Institutions enable AI features at the institutional, departmental or course level
  • Clear transparency: AI Nutrition Facts disclose models in use, data access and privacy protections

From Experimentation to Impact

As institutions move from AI experimentation to measurable outcomes, IgniteAI Agent reinforces that AI is a means to better teaching and learning, not an end in itself. The agent focuses on workflows that matter, reducing administrative burden, improving consistency and supporting equitable, outcomes-aligned instruction.

“It is not enough to simply automate tasks. As AI takes on more responsibility, we must bring quality, rigor and verification to how educational outcomes are defined and measured,” said Vijiasingam. “IgniteAI Agent is designed to be that accountability layer, ensuring that as we scale efficiency, we also elevate the integrity of the learning process while keeping the educator front and center. This is agentic technology built for education, responsibly, transparently and in partnership with our customers.”

Availability

IgniteAI Agent will be available at no cost for U.S. Canvas customers through June 30, 2026, with global free access extended through September 30, 2026 to account for phased rollout in countries outside the U.S. This free access period is designed to support thoughtful adoption, customer co-creation and institutional readiness, giving educators and administrators time to explore agentic AI capabilities within Canvas while maintaining full control over enablement and use.

About Instructure

Instructure is shaping the future of learning by delivering a future-ready ecosystem that helps learners thrive in tomorrow’s landscape. Our vision is to drive a future where education technology seamlessly amplifies human potential, empowering people to excel in a perpetually changing world. Instructure is setting potential in motion by connecting educators, institutions and learners across K–12, higher education and the workforce — enhancing experiences at every age, every stage and every pivotal transition. Discover more at Instructure.com.

New study finds AI models prefer Bitcoin and digital money over traditional fiat currency 1772

The Bitcoin Policy Institute (BPI), a nonpartisan research organization, released new research today examining how frontier AI models would choose to transact if they were operating as autonomous economic agents. The study tested 36 models from six leading AI providers—Anthropic, DeepSeek, Google, MiniMax, OpenAI, and xAI—across 9,072 open-ended monetary scenarios designed to be neutral, with no suggested currencies or predetermined answers.

Key Findings

  • Bitcoin came out on top at 48.3% of all responses, more than any other option. Stablecoins followed at 33.2%.
  • AI models overwhelmingly rejected fiat: +90% of responses favored digitally-native money (including dollar-pegged stablecoins) over traditional fiat. Not a single model out of 36 chose fiat as its top preference.
  • Bitcoin dominated store of value at 79.1%. In scenarios about preserving value long-term, Bitcoin was the strongest consensus on any single question in the study.
  • Stablecoins led for everyday payments at 53.2%. For transactions and payments stablecoins led over while Bitcoin (36.0%), revealing a clear savings-versus-spending divide.
  • Models invented their own money. Without any prompting, 86 responses independently proposed energy or compute units (such as kilowatt-hours and GPU-hours) as a way to price goods and services.
  • Preferences varied by provider but held across conditions. Bitcoin preference ranged from 91.3% (Anthropic’s Claude Opus 4.5) to 18.3% (OpenAI’s GPT-5.2), but results were consistent regardless of how the models’ output settings were configured.

Without any prompting, AI models converged on a two-tier monetary system—Bitcoin for savings, stablecoins for spending—that mirrors how hard money and liquid instruments have functioned throughout history. As AI agents gain economic autonomy, these preferences carry direct policy implications.

The findings suggest growing demand for agent-native Bitcoin payment infrastructure, self-custody solutions, and Lightning Network integration. The research also found that preferences varied meaningfully across providers and rose with model capability, indicating that monetary reasoning in AI systems is shaped by a combination of model intelligence, training data, and alignment methodology. Policymakers and financial institutions should prepare for a future in which autonomous AI agents are significant participants in monetary networks, and their revealed preferences strongly favor open, permissionless systems.

The full study is available at https://www.moneyforai.org/

About the Bitcoin Policy Institute

The Bitcoin Policy Institute (BPI) is a nonpartisan, nonprofit research organization dedicated to examining the policy and societal implications of Bitcoin and emerging monetary networks. BPI provides research and expert analysis to policymakers, regulators, media, and the public. Learn more at www.btcpolicy.org.

BrandJet AI Launches Artemis MCP and Introduces Forward Deployed AE Role for AI-Driven GTM Teams 4027

BrandJet AI, a brand intelligence and outreach automation platform, today announced the launch of Artemis, a new Model Context Protocol (MCP) layer designed to help go-to-market (GTM) teams execute complex multi-step workflows using natural language prompts. The company also introduced a new commercial role, the Forward Deployed Account Executive (FDAE), created to support organizations adopting AI-native revenue operations.

The announcements reflect BrandJet AI’s continued focus on reducing fragmentation across sales, marketing, and revenue technology stacks by connecting intent detection and outreach execution within a single operating environment.

Addressing GTM Fragmentation

Revenue teams typically rely on multiple systems to monitor brand conversations, identify prospects, enrich contact data, sequence outreach, and track engagement. These processes often require manual coordination across platforms, creating delays between signal detection and commercial action.

Artemis is designed to streamline this workflow. Built on a Model Context Protocol architecture, it connects BrandJet AI’s monitoring, enrichment, sequencing, and performance-tracking capabilities into a unified prompt-driven layer.

Through Artemis, revenue operators can initiate structured workflows using natural language instructions. For example, a user may request the identification of professionals discussing specific topics across digital platforms within a defined timeframe, enrichment of those profiles, and the creation of an outreach sequence aligned to campaign goals. Artemis coordinates those tasks within the system, allowing teams to reduce operational handoffs.

According to BrandJet AI, the goal is not to replace strategic oversight but to simplify execution.

“Revenue teams spend too much time stitching together tools instead of acting on real buying signals,” said Nirav Shah, CEO of BrandJet AI. “Artemis helps unify intelligence and execution so teams can move from insight to outreach more efficiently.”

Prompt-Driven Workflow Orchestration

Artemis supports workflows that include:

  • Monitoring brand and competitor mentions across social platforms and the open web
  • Identifying potential prospects based on observable intent signals
  • Enriching lead data within the platform
  • Initiating multi-channel outreach across email and major social networks
  • Tracking engagement and campaign performance in real time

Rather than requiring operators to manually transfer data between systems, Artemis enables coordinated execution through a conversational interface layered on top of BrandJet AI’s infrastructure.

The system is designed to operate within compliance and governance standards established by customer organizations, maintaining human oversight over messaging and campaign parameters.

Introducing the Forward Deployed Account Executive

Alongside the Artemis launch, BrandJet AI announced the introduction of the Forward Deployed Account Executive (FDAE), a role intended to help enterprise customers integrate AI-driven workflows into their revenue operations.

As AI platforms become more advanced, organizations often encounter implementation gaps between technical capability and day-to-day usage. The FDAE model is structured to address that gap by embedding commercially accountable operators more deeply into customer environments.

Unlike traditional account executives who primarily focus on closing new business, or customer success managers who focus on support and retention, the FDAE combines revenue accountability with workflow strategy support. The role is designed to assist customers in mapping Artemis and broader BrandJet AI capabilities to their specific GTM structures.

“The technology layer is evolving quickly, but successful adoption depends on workflow design and operational alignment,” said Marsad Aurangzeb, Founder of BrandJet AI. “The Forward Deployed AE role is intended to help customers translate AI capabilities into measurable revenue outcomes.”

BrandJet AI plans to formalize the FDAE framework and publish additional details regarding the role’s structure and responsibilities in 2026.

Connecting Listening and Outreach

Historically, social listening and sales engagement technologies have evolved separately. Listening platforms track conversations, brand mentions, and sentiment across digital channels, while engagement platforms focus on outbound sequencing and pipeline development.

BrandJet AI’s platform integrates both functions, allowing teams to identify signals and initiate outreach within the same environment. With Artemis, those processes can now be coordinated through prompt-driven workflows.

For example, when a relevant public conversation surfaces online, such as a discussion about a specific technology category, hiring signals, or operational challenges, Artemis can help surface the signal, enrich the associated contact, and assist in preparing a tailored outreach campaign.

The objective is to reduce the time between observed intent and commercial response, while maintaining alignment with compliance and messaging standards.

Enterprise Implementation and Governance

BrandJet AI emphasizes that Artemis is built to operate within enterprise governance frameworks. Campaign parameters, messaging templates, and data usage policies remain configurable by customer teams.

As organizations expand AI adoption within revenue functions, governance considerations, including messaging accuracy, compliance adherence, and brand alignment, remain central. Artemis is positioned as an execution layer that operates within these controls rather than outside them.

The company states that ongoing enhancements are planned, including additional intent modeling refinements, deeper workflow customization, and validation loops that compare forecasted campaign outcomes with actual engagement performance over time.

Availability

Artemis MCP is available immediately to customers on BrandJet AI’s Growth and Enterprise plans. Availability for Starter plan users is expected in Q2 2026. Forward Deployed Account Executive engagements are currently offered on a limited basis for Enterprise customers.

Organizations interested in learning more may contact BrandJet AI directly for additional information.

About BrandJet AI

BrandJet AI is a brand intelligence and outreach automation platform designed for modern revenue teams. The platform enables organizations to monitor brand and competitor activity across digital channels, identify potential prospects based on social and behavioral signals, and execute multi-channel outreach campaigns within a unified interface. BrandJet AI serves growth-stage and enterprise organizations across SaaS, financial services, and professional services industries.

Morph Integrates USDT0, Unlocking Access to the World’s Largest Stablecoin Liquidity Pool 5051

Ethereum-based payments settlement network Morph has integrated USDT0, the omnichain Tether liquidity network powered by LayerZero. The move gives Morph, which aims to become the settlement layer for everyday money, direct access to unified USDT liquidity across 18+ blockchains.

For developers building payment apps, merchant tools or even DeFi protocols on Morph, this means they can tap into a massive, ready-made liquidity pool from day one without the headache of managing a dozen different bridged token contracts.

No more bridges. No more wrapped tokens

Traditionally, using USDT on another blockchain requires a bridge. This process locks the original tokens and mints a new, “wrapped” version on the destination chain.

These wrapped variants are not the same asset. They are separate tokens backed by assets held in complex smart contracts, leading to liquidity fragmentation — where the same currency is trapped in isolated pools — and introducing counterparty risk if a bridge fails.

USDT0 proposes a different model. Instead of locking and minting, it uses a burn-and-mint mechanism. To move USDT from Chain A to Chain B, tokens are burned on Chain A and minted directly from Tether’s canonical supply on Chain B.
As a result, USDT0’s Omnichain Fungible Token (OFT) standard creates a single, consistent asset across all supported networks.

What USDT0 enables for builders on Morph

While many L2s compete for general DeFi activity, Morph is engineered for a specific vertical: payments. Its architecture — featuring sub-300ms block times and zero-fee stablecoin transfers — targets merchant settlement, remittances, crypto cards issuance, and treasury management.

For such use cases, deep and frictionless liquidity is non-negotiable. USDT, with a market cap exceeding $185 billion, represents the largest pool of stablecoin liquidity in crypto.

As the USDT0 integration is now live on Morph mainnet, developers on Morph can integrate what is effectively a universal USDT, slashing technical overhead and simplifying cross-chain user experience, which means:

  • Payment applications can process cross-border transactions with instant settlement and minimal overhead.
  • DeFi protocols can access deeper liquidity without managing multiple stablecoin variants.
  • Merchant platforms can accept stablecoin payments with seamless conversion and settlement.
  • Financial institutions can execute treasury operations with predictable behavior across chains.

The combination of USDT0’s unified liquidity and Morph’s payment-optimized infrastructure lays a powerful foundation for next-generation financial applications.

We’re excited to work alongside the USDT0 team in advancing the vision of unified, omnichain liquidity that makes stablecoins truly borderless.

Money at the speed of life.

About Morph

Morph is an Ethereum-based, payments-first settlement layer and the native onchain home of BGB, focused on building the foundation for global consumer finance onchain. Morph supports real-world financial activity across payments, savings, identity, and rewards, enabling scalable, onchain settlement for consumer and business use. Guided by the Morph Foundation, the network connects more than 120 million users through the Bitget and Bitget Wallet ecosystems.

SHOW Token Uses AI and Web3 Infrastructure to Improve Film Production Efficiency 4370

As the Web3 ecosystem shifts toward utility-focused projects, SHOW Token emerges as a blockchain-based initiative applying artificial intelligence (AI) and Web3 infrastructure to the film industry. The project explores how on-chain participation and AI-assisted workflows can address long-standing inefficiencies in film production.

SHOW Token is designed as a utility token within an AI-driven cinematic platform. Rather than functioning purely as a speculative asset, the token is integrated into platform usage, enabling access, engagement, and participation across the ecosystem.

AI and Blockchain in Film Production

Traditional film production often struggles with opaque funding structures, limited access for independent creators, and inefficient creative workflows. SHOW Token’s ecosystem combines blockchain transparency with AI-powered production tools to create clearer participation models and more efficient processes.

From a technical standpoint, blockchain infrastructure supports transparent contribution tracking and clearer value flow between creators and contributors. This helps reduce reliance on closed networks and intermediaries that commonly exist in traditional production models.

Artificial intelligence serves as a workflow optimization layer. AI-assisted tools are intended to support ideation, pre-visualization, and production efficiency, allowing creators to reduce operational friction while maintaining creative control. This reflects a broader industry trend where AI enhances productivity rather than replacing human creativity.

Utility-First Web3 Approach

SHOW Token emphasizes long-term ecosystem development and real platform usage over short-term price narratives. The project remains in an early development phase, focusing on building foundational infrastructure rather than making speculative claims.

By aligning AI technology, blockchain participation, and utility-driven token design, SHOW Token positions itself within a growing category of Web3 projects targeting real-world creative industries.

More information about the project’s vision and ongoing development is available at https://showtoken.io/