I spent two days at HumanX 2026 in San Francisco covering 27 sessions across AI infrastructure, enterprise adoption, agentic systems, security, creative, retail, and finance.

I heard about AI in marketing. AI in brand strategy. AI in customer service. AI in software development. AI in creative production. AI in financial infrastructure. AI in cybersecurity. AI in retail design. AI in financial crime. AI in compliance.

I did not hear a single session about the supply chain.

Not one.

For someone who came up through operations and supply chain, who has personally sat with demand forecasting spreadsheets that were wrong more often than right, navigated supplier disruptions without early warning, and made inventory decisions that were really just educated guesses dressed up in numbers. For an industry that sits at the core of the global economy, the silence was striking.

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What we mean when we say “supply chain.”

To be precise, this is not about last-mile logistics or route optimization—the areas that do get occasional attention. The Supply Chain word gets used loosely, usually.

This is about upstream decision-making: demand forecasting, supplier risk management, inventory positioning, and procurement intelligence.

Demand forecasting: predicting what you’ll need to sell or produce before you’ve sold or produced it.

Supplier risk management: identifying which suppliers are fragile before they fail you.

Inventory positioning: deciding how much of what to hold where, across a network that doesn’t sit still.

Procurement intelligence: understanding pricing patterns, lead time trends, and supplier behaviour across hundreds of relationships simultaneously.

These are decisions that get made every week in every company that makes or moves physical goods. They are made by people working with incomplete information, under time pressure, using tools that were not designed for the complexity of the problem. And they are decisions where being systematically better — not perfect, just better — compounds into enormous financial advantage over time.

Yet at a conference dedicated to AI’s transformation of business, they were absent. AI should be solving the industry’s problem right now.

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What I did hear

Here is what I did hear, to be fair.

P&G’s CIO Seth Cohen spent time on supply chain automation — specifically, unattended manufacturing scaled across nine locations, and molecular discovery work that cut development timelines from years to months. For a company like P&G’s scale, those results are genuinely significant. But they are the results of a decade-long data infrastructure investment that most companies, including most large companies, have not made.

Walmart’s Daniel Danker gave one supply chain example: a remote Canadian store used an internal tool called Code Puppy to combine weather data and ferry schedules for inventory planning. It’s a good story — a frontline associate solving a local operations problem with a tool headquarters gave them. But it’s a point solution, not a framework, and it was mentioned in passing in a session primarily about AI democratization.

That was essentially it. Two examples, mentioned briefly, in sessions about something else. For an industry representing somewhere between $15 and $20 trillion in global economic activity, that’s a remarkably thin presence.

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Why the supply chain is the most AI-ready industry nobody is talking about

What makes this gap surprising is that the supply chain is, on paper, one of the most AI-ready industries in existence.

It runs on data. Sensor data, transaction data, demand signals, supplier data, logistics data, and financial data. The data infrastructure in large supply chains is often more mature than in the marketing and creative functions that dominated the HumanX agenda.

The problems are structured. Unlike brand strategy or creative judgment, supply chain decisions are historically modelled, mathematically defined, and directly measurable. A demand forecast is either accurate or it isn’t. A stockout either happened or it didn’t. An inventory position either matched demand, or it left cash sitting on a shelf or a gap in a customer’s order. The feedback loops are tight, and the outcomes are concrete.

The stakes are direct. Having spent time inside these decisions, I can tell you that even small improvements in forecast accuracy at a meaningful scale translate into real money — in working capital, in margin, in customer retention. A 2% improvement in forecast accuracy at a company running substantial inventory isn’t a rounding error. It’s a financial event.

So why wasn’t it on stage?

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Three reasons for the gap

The first is audience composition. HumanX skews toward technology leaders, marketing executives, founders, and investors. Supply chain and operations leadership tends not to show up at general AI conferences. They attend industry-specific events — operational forums, ERP user conferences, sector trade shows. The gap in the room creates a gap in the agenda.

The second is vendor invisibility. The companies building AI for demand sensing, supplier risk, and logistics optimization are largely invisible outside their industry. They’re specialized, often enterprise-only, and they sell through procurement channels rather than developer communities. They don’t generate the kind of press coverage that gets sessions programmed at a conference like HumanX.

The third — and this is the one that matters most — is that the enterprise supply chain is genuinely hard to disrupt quickly. The data is siloed across ERP systems, warehouse management platforms, and procurement tools built over decades. Integration alone is a multi-year project. The risk tolerance for AI-driven decisions in the supply chain is low because the consequences of errors are operational and financial. You can course-correct a bad marketing campaign. You cannot easily recover from a production halt caused by a procurement decision that an AI made without the right context.

This is exactly why AI hasn’t disrupted the supply chain at the pace it’s disrupted other functions. And it’s exactly why, when it does, the value creation will be disproportionately large.

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What the conference did tell me

The sessions I attended gave me frameworks that apply directly to this gap, even though none of them were about the supply chain.

Databricks CEO Ali Ghodsi said something that stayed with me: current models are sufficiently capable, but they fail in enterprises because they lack context. The bottleneck isn’t the AI. It’s the organizational and data infrastructure around it. He expects enterprise AI adoption to take five to ten years.

That timeline maps almost exactly to where supply chain AI is in its adoption curve. The context problem is especially acute here. A demand forecasting model trained on historical sales data without context about upcoming promotions, competitor pricing moves, macroeconomic shifts, or supplier lead time changes will produce outputs that experienced planners will correctly distrust — because they know what the model doesn’t know. The data exists. The integration and context layer doesn’t.

The building of a trustworthy agentic AI session reinforced this from a different angle. Panellists from Dataiku made a distinction that matters enormously for supply chain: back-office decision agents — the ones affecting clinical outcomes, credit decisions, or supply choices — require far stronger testing and explainability than personal productivity tools. A demand forecasting agent that informs procurement isn’t a chatbot. It needs to be auditable, explainable, and designed to fail gracefully. Most current AI deployments are not built to that standard.

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What finance figured out that supply chain hasn’t yet

What I found most instructive at HumanX wasn’t what was said about supply chain. It was watching what happened to industries that got forced into AI governance before they were ready — and what that forced them to build.

The finance sessions were the clearest example. Multiple panels addressed AI compliance frameworks in regulated financial services — what one session called the “compliance flywheel.” The argument was that embedding compliance, risk, and governance early in AI product development actually accelerates innovation rather than slowing it. Shared semantic definitions, data lineage, and auditability become infrastructure that compounds over time. Organizations that treat compliance as an early-stage design constraint end up with more durable systems than those that bolt it on later.

The financial crime session added a sharper edge to this. Jonathan Levin of Chainalysis described how generative AI has dramatically lowered the barrier to entry for financial fraud — enabling impersonation, automation, and scale that wasn’t previously possible for lower-skill actors. The response from defenders has been to build proactive threat-hunting systems, intelligence-sharing networks, and AI agents that can process evidence and flag suspicious patterns faster than any human analyst.

I kept thinking about procurement fraud while sitting in those sessions. Supplier impersonation. Fake invoices. Bid manipulation. Ghost vendors. These are supply chain problems that have existed for decades and are about to get significantly harder to detect as AI makes fraudulent activity more convincing and more automated. The financial services industry is building defensive infrastructure right now because regulation and litigation forced the conversation. Supply chain hasn’t been forced there yet. But the same pressures — fraud escalation, operational failure, regulatory scrutiny, and eventually litigation — are coming.

The compliance frameworks being built in regulated finance are the template for what supply chain AI governance will eventually need to look like. The difference is in the timeline. Finance is building it now under pressure. Supply chain will build it later, under more pressure, starting from further behind.

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The investment angle

The publicly traded companies most exposed to the supply chain AI wave are not the model providers. They are the enterprise software platforms that own the data: the ERPs, the warehouse management systems, the supply chain visibility platforms, the procurement analytics tools. Every one of those companies is currently navigating the same question: do they build AI natively into their platforms, partner with AI providers, or get disrupted from below by AI-native startups that don’t carry decades of integration debt?

That question is not answered. The window where it remains unanswered is the window where the investment opportunity is most interesting — both in the incumbents navigating the transition and in the new entrants who might make the integration question irrelevant.

I track this closely because I sit at the intersection of it. I understand the operational problem from having lived inside it. I understand the AI capability layer from building tools on top of it. Those two lenses together are what make the gap visible.

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I came back from HumanX with a lot of material about where AI is moving and what serious operators and investors think. Most of it confirmed what I already believed about infrastructure, reliability, and the gap between demo performance and production reality.

The most valuable thing I came back with was silence.

Nobody talked about the supply chain. Not because it isn’t ready. Not because the problem isn’t large enough. Because the people who understand the problem and the people building the tools are not yet in the same room.

Finance got there first because it was forced. Supply chain will get there when it is forced to.

The question worth asking now — before the forcing event — is which companies are building the governance, the data infrastructure, and the AI capability to be ready when that moment arrives. Because the ones who are will look obvious in hindsight. They always do.

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Jinlu Wang has a background in supply chain, ERP implementation, and enterprise operations. She now builds automated trading systems and web applications and covers AI infrastructure, fintech, and enterprise technology for Trade with Harp, a paid investment research and trading community.

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There’s a moment every builder knows. You’ve shipped the thing. It’s live. Real people are using it. And then it breaks in a way you never designed for, at the worst possible time, in front of everyone.

Mine was a duplicate alert loop during a live trading session. Forty minutes of my bot firing the same wrong signal while my members watched, and the market moved without us. I was in the logs, my phone was blowing up, and I had the specific sick feeling of someone whose confidence has just been stress-tested in public.

I fixed it. Rebuilt the deduplication logic, added safeguards I should have put in from the start. But those forty minutes are the reason I don’t trust any AI pitch that doesn’t account for failure.

I’ve spent the past year building automated trading systems and web applications -a dual-timeframe swing bot, a pivot-level tracker, an options flow monitor, and dashboards that pull live brokerage data and track dividend recovery across multiple accounts. None of it is academic. These systems run continuously on cloud infrastructure, and the members of my paid trading community use them to make real decisions in real markets. When something breaks, I hear about it immediately. That feedback loop has taught me more about AI in finance than anything else I’ve encountered.

I’m sharing this because I’m about to say some things about AI and investing that will sound skeptical, and I want to be clear: the skepticism comes from the inside, not the outside.

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The fintech industry has a problem nobody wants to say plainly: most of what’s currently being sold as “AI-powered” is a model wrapper bolted onto a product that existed before, marketed to investors who are understandably eager to find the right horse in this race.

I understand why it happens. The pressure to speak the language of the moment is real, and the language of the moment is AI. But when you’ve spent months building systems that fail unpredictably, fixing them at 11 pm, and rebuilding them better, your tolerance for vague capability claims drops to zero.

The question I ask about any fintech company claiming an AI edge is no longer “what can your AI do?” It’s “what happens when it’s wrong?” Because it will be wrong. In my experience, how a company answers that second question tells you almost everything about whether you’re looking at a real business or an expensive experiment dressed up for a fundraiser.

Most can’t answer it cleanly. That gap is where a significant amount of the current mispricing lives.

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The jump from a bot that alerts you to a system that reasons, plans, and acts is not a software upgrade. It’s a different problem entirely.

I’m currently building toward that -an agentic system designed to work across multiple data sources and execute without me in the loop. The process has been humbling. You’re asking the system to handle ambiguity at every step, to make judgment calls in sequences where one wrong assumption compounds through everything downstream, and to fail in ways that are recoverable rather than catastrophic. In a trading context, that last requirement is the whole game. Markets don’t pause because your agent made a wrong assumption at step two.

What I keep learning is that the companies that will actually win in agentic AI are solving a reliability problem, not a capability problem. Reliability doesn’t demo well. It doesn’t make headlines. But a system that behaves predictably under conditions nobody anticipated is worth more than one that performs brilliantly in controlled environments -and the gap between those two things is where most AI projects currently live.

This shapes how I evaluate companies in this space. An impressive demo is not the signal. The boring, unglamorous work of engineering for failure -that’s the signal. And it’s genuinely hard to see from the outside.

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Here’s my investment view, stated plainly.

The application layer of AI is exciting and nearly impossible to underwrite with confidence at current valuations. The space moves too fast, competitive advantages compress too quickly, and the half-life of any specific product edge is short enough to make long-term positioning feel more like speculation than investing.

The infrastructure underneath is a different conversation.

I’ve been researching optical infrastructure extensively -the companies building transceivers and coherent technology that physically connect AI data centers at the speeds these workloads require. These aren’t household names. They don’t have consumer products. But hyperscalers cannot build without them, and this buildout cycle has years of runway remaining.

The same logic extends to energy. The data centers being planned and funded right now need reliable baseload power at a scale that has quietly made nuclear a serious investment conversation again -not for ideological reasons, but purely practical ones. I’ve tracked that theme developing for over a year. It isn’t a consensus yet. That’s the point.

The investors who find real returns in this cycle won’t be the ones who moved fastest on the most visible names. They’ll be the ones who asked what those names couldn’t exist without and positioned themselves there instead.

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What am I expecting while covering at HumanX 2026 in San Francisco?

The most important conversations in AI don’t happen on stage; they happen between people who are actually building these systems, talking to each other without the performance layer that comes with a keynote slot. The reliability problem in agentic AI, the real economics of AI infrastructure, the tension between long-term inevitability and short-term valuation chaos, these are exactly the questions I’m working through in my own builds, and they’re the questions my audience is asking me.

There is no shortage of AI coverage, that describes what’s happening. There’s a real shortage of coverage written by someone who has also been in the logs at midnight fixing a broken system before markets open.

That’s the perspective I’d bring to this event. And it’s the perspective I think is missing from most of what gets published about AI and investing right now.

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*Jinlu Wang is an AI Editorial Strategist with Ubiq Broadcasting Corp and builds automated trading systems and web applications for financial markets. She runs Harp’s Trading, a paid investment research and trading community, and publishes institutional-style research covering AI infrastructure, energy, and commodity-linked technology themes. *

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I want to start with a confession.

I built trading bots that my community relies on every day. I’ve spent more hours than I can count debugging alert logic, fixing duplicate signals, tuning parameters, and rebuilding systems from scratch when they broke in ways I didn’t anticipate. I believe in what I’ve built.

And I still make bad trades.

Not because the tools don’t work. They work. But because somewhere between the alert firing and my finger hitting the button, I stopped being a systematic trader and became a human being with opinions, emotions, a position that was already down, and a completely irrational certainty that this next one was going to recover everything.

The tools didn’t fail me in those moments. I failed myself. And I’ve watched enough traders in my community make the same mistake often enough to know that what I’m about to say is the conversation the AI trading tools industry is actively avoiding — because it doesn’t help them sell subscriptions.

So let’s have it.

The alert is not the trade

This sounds obvious. It isn’t, apparently.

I built my swing bot to scan multiple timeframes, identify setups based on criteria I defined when I was calm and thinking clearly, and push alerts to my Discord. It does that job well. But I’ve sat in live sessions and watched members receive an alert and enter a position within seconds — no volume check, no broader market context, no consideration of whether they’d already taken two losses that morning and maybe shouldn’t be trading at all.

The bot flagged a setup. They heard a decision.

Those are not the same thing. An alert tells you something worth examining just happened. It says nothing about whether you should act, how large your position should be, what your stop is, or whether this particular day is a good day for you to be pulling triggers at all. A VWAP reclaim on above-average RVOL in the context of a strong sector tape means something different than the same signal on a news-driven spike in a choppy overall market. The bot can’t know that. You have to know that.

This is the gap nobody in the AI tools space wants to talk about. The tools are built to look impressive in demos — fast, confident, clean. What they’re not built to do is slow you down when you should be slowing down. And the moments that cost traders the most money are rarely the ones where they had bad information. They’re the ones where they had good information and moved on it before they’d thought it through.

AI amplifies what you already are

This is the thing I had to learn the expensive way.

There was a stretch where my swing bot’s signal quality was genuinely good — the setups it was flagging were clean, the follow-through was there, and the logic held up. And my account still underperformed during that period. Not because of the bot. Because I was also day trading separately, making impulsive entries on tickers that had nothing to do with my system, averaging into losers, ignoring the rules I’d set for myself and justifying every single exception in the moment.

The bot was doing its job. I wasn’t doing mine.

What that experience clarified — painfully, over multiple sessions — is that an AI tool is not a floor that catches you when you’re trading badly. It’s a multiplier on whatever you’re already doing. If your discipline is solid, your risk management is consistent, and you’re genuinely following a process, the right tool can sharpen your edge in real ways. If you’re chasing, overtrading, letting losses make you reckless — the tool just helps you do all of that with more information and faster execution.

I have a rule I keep for myself: by 10:30 AM on Fridays, I’m out of everything. No new entries. The week is done. It took a while to build that discipline. No bot gave it to me. I built it because I’d seen too many times what happened when I didn’t have it.

Rules like that — the ones you make when you’re clear-headed and enforce when you’re not — are the real edge. The tool is secondary to the trader running it.

What these tools are actually good for

I don’t want to be all doom about it, because there’s genuine value here if you understand what you’re working with.

The honest case for AI trading tools comes down to three things.

Consistency. My system runs the same scan in hour four of a session as it ran at open — same criteria, same logic, same output. I don’t. By mid-afternoon, I’m tired, I’ve already made decisions I’m second-guessing, and my pattern recognition is quietly degrading in ways I can’t fully perceive in the moment. The bot doesn’t have that problem. It doesn’t have a bad day. In a domain where emotional fatigue kills performance, that’s nothing.

Coverage. I physically cannot watch 200 tickers across multiple time frames simultaneously and catch every relevant setup. The bot can. It misses nothing on its watchlist, which means I catch opportunities I would have missed and pass on noise I would have rationalized into something.

Record-keeping. Every alert my system generates is logged. Every condition that triggered it is documented. Over time, that data becomes something genuinely valuable — an honest performance record, stripped of the selective memory that leads most traders to overestimate their win rate because they remember the good trades more vividly than the bad ones. Automated systems don’t have selective memory. Mine has shown me setups I thought were working that weren’t, and setups I’d been dismissing that were consistently cleaner than I’d given them credit for.

All of that is real. None of it decides for you.

A word on bad products

I know traders who have been burned badly enough by AI tools that they’ve written off the whole category. Some of those experiences were completely legitimate — there are genuinely bad products in this space. Backtests built on look-ahead bias. Parameters curve-fitted to a specific period of market history that fall apart the moment conditions change. Marketing copy that implies a documented edge the underlying system has never actually demonstrated in live conditions.

Healthy skepticism toward specific products is rational. Dismissing everything because one tool was garbage is like avoiding all cars because one had faulty brakes.

The right question about any tool isn’t “can AI work in trading” — the evidence that systematic, data-driven approaches generate an edge is extensive and goes back decades. The right question is whether this specific tool, built on this specific logic, by these specific people, actually does what it claims when real money is on the line. Ask for a live track record. Not a backtest. A live track record, with drawdowns, with losing streaks, with all the ugly periods included.

If they don’t have one, or won’t show you one, that’s your answer.

The part nobody wants to hear

AI is not going to make trading easier.

The market at the margin is zero-sum. The participants on the other side of your trades are also using AI — many of them with better data, more compute, lower latency, and teams of quants who do nothing else. Retail AI tools don’t close that gap. They never will.

What they can do, in the right hands, is make you more consistent. More disciplined. Better at identifying the setups that actually fit your process and filtering out the ones that don’t. That’s real, and for the kind of systematic swing and position trading where I do my best work, it genuinely matters.

But it only works if you show up as the trader the system needs you to be. Not the trader who sees an alert and goes straight to the order ticket. Not the trader who blames the bot when a setup doesn’t follow through. The trader who built the rules understands why they exist and has enough self-awareness to know when they’re about to break them.

I built the tools. I still have to do that work.

So do you.

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*Jinlu Wang is AI Editorial Strategist with Ubiq Broadcasting Corp, builds automated trading systems and web applications for financial markets. She runs Harp’s Trading, a paid investment research and trading community, and publishes institutional-style research covering AI infrastructure, energy, and commodity-linked technology themes.*

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Harjot Singh, a 21-year-old male, has been charged with one count of discharging a firearm into a place contrary to section 244.2(1)(a) of the Criminal Code.

Taranveer Singh, a 19-year-old male, has been charged with one count of discharging a firearm into a place contrary to section 244.2(1)(a) of the Criminal Code.

Dayajeet Singh Billing, a 21-year-old male, has been charged with one count of discharging a firearm into a place contrary to section 244.2(1)(a) of the Criminal Code.

SPS authorities informed that the investigation is ongoing  and additional charges may be forthcoming.

On February 1, 2026, at approximately 3:50 am,  SPS members assigned to Project Assurance, working in collaboration with SPS’s Major Crime Section, were patrolling in Surrey’s Crescent Beach neighbourhood when reports came in of shots fired and a small fire outside a residence near Crescent Road and 132 Street. The three accused were arrested by SPS officers a short time later near 28 Avenue and 140 Street after getting into a rideshare vehicle.

SPS’s Major Crime Section took over the investigation and the three men have now been charged with Criminal Code offences.

All three have been remanded in custody until February 5, 2026. SPS has confirmed they are all foreign nationals and has engaged Canada Border Services Agency.

SPS  has  released  the photos of Harjot Singh, Taranveer Singh, and Dayajeet Singh Billing after determining that the disclosure is necessary to assist with the ongoing police investigation.  SPS hopes that the public release of images can prompt additional witnesses, victims, or associates to come forward with relevant information regarding the activities of one or more on the morning of or before February 1, 2026.

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This week’s offerings will add to the $15.5 billion of Canada green bonds issued since March 2022 under five previous transactions that issued bonds with maturities from 7 to 30 years. For this issuance, the government plans to issue a new 10-year green bond. This follows the October 2025 issuance of a new $1 billion 30-year bond and the $1.5 billion re-opening of the 7-year bond that was first issued in February 2025, both of which saw robust demand.

In June 2025, the government released the Green Bond Allocation and Impact Report 2023-24. Canada releases allocation and impact reports for investors and interested parties on an annual basis until full allocation of the net proceeds is achieved. These allocation reports detail the green eligible expenditures funded with the proceeds of each bond, while impact reports outline the environmental benefits of the expenditures and related social impacts where data is available.

Canada’s green bond program, launched in March 2022, is advancing Canada’s investments in clean growth, renewable energy, climate action, and environmental protection. Green bonds unlock private capital to speed up projects such as green infrastructure and nature conservation.

The government remains committed to regular green bond issuances. Projects funded by green bonds will grow Canada’s economy and create jobs across the country. Mobilizing capital through green bonds is an important tool of Canada’s strategy to achieve net-zero emissions by 2050.

The Government of Canada’s green bonds will meet demand from investors seeking green investment opportunities backed by Canada’s AAA credit rating, while contributing to the development of a stronger sustainable finance market both domestically and globally.

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San Francisco:

Artificial intelligence investment surged to a record $211 billion in 2025, nearly doubling the $114 billion deployed in 2024, according to a new report released by HumanX in partnership with Crunchbase. The figure now represents roughly half of all global venture capital, underscoring AI’s dominance in the technology investment landscape.

The AI Funding Report 2025 signals a structural shift in investor behaviour—from speculative experimentation to what analysts describe as a “disciplined march to value,” with capital increasingly directed toward infrastructure and enterprise-grade applications.

At the center of this transformation is the San Francisco Bay Area, which the report identifies as the world’s “global control center” for artificial intelligence. The region attracted approximately $126 billion—60% of total global AI funding—while an overwhelming 81% of all startup capital within the Bay Area flowed into AI ventures.

Megadeals and Market Maturity

Large-scale funding rounds dominated the market. Deals exceeding $100 million accounted for $163 billion, or 77% of total AI investment, reflecting a concentration of capital among fewer, high-confidence bets.

Meanwhile, companies developing foundation models—such as OpenAI and Anthropic—saw funding jump 180% to $87 billion, reinforcing their position at the core of the AI ecosystem.

However, investment is no longer limited to model development. Nearly 59% of total funding flowed into the broader AI stack, including infrastructure (19%), deep tech and robotics (11%), and sector-specific applications in healthcare and security (15%).

Diversity Gains Ground

The report also highlights growing momentum among female-founded companies. In North America and Europe, 47% of AI funding—equivalent to $84.7 billion—went to startups with at least one female founder, signalling incremental progress in a historically male-dominated sector.

IPO Wave on the Horizon

Using predictive analytics, Crunchbase forecasts a significant wave of exits in 2026. Of the 138 private companies scheduled to appear at this year’s HumanX summit, 27 are classified as “probable or very likely” IPO candidates, while another 30 are considered strong acquisition targets.

“Every AI cycle brings speculation about bubbles, but the data tells a more nuanced story,” said Stefan Weitz, co-founder and CEO of HumanX. “Capital is increasingly flowing toward companies solving complex, high-value problems with long-term durability.”

Jager McConnell, CEO of Crunchbase, added that the market is entering a more disciplined phase. “Investors are no longer funding anything labelled AI. But our data suggests many companies in this ecosystem are poised for significant growth rounds—and potentially public listings—as early as 2026.”

HumanX 2026: A Launchpad for AI Leaders

More than 130 companies are set to present at HumanX in San Francisco this year, collectively raising over $72 billion since 2018. Featured participants include industry heavyweights such as Databricks, Cerebras Systems, and CoreWeave, alongside innovators like Runway, Synthesia, Cohere, and Inflection AI.

Also taking the stage are high-profile technology firms, including Figma, Chime, and Replit, reflecting the growing convergence between AI and mainstream digital platforms.

A Market Still in Early Innings

Despite record-breaking investment levels, industry leaders caution that the AI boom is far from maturity.

“We’re only in the first inning of the AI game,” McConnell said, pointing to the accelerating pace of innovation and the increasing role of predictive intelligence in shaping investment decisions.

The full report is available via HumanX.

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At approximately 3:50 am, members of SPS  assigned to Project Assurance, working in collaboration with SPS’s Major Crime Section, were in the area of 129 Street and 84 Avenue when they heard what they believed was a gun shot.

SPS officers quickly located a suspect vehicle and stopped it, taking the driver and a passenger into custody. During the arrest,  a loaded handgun was discovered and seized. Project Assurance is an initiative in which SPS proactively patrols neighbourhoods  and business areas,  targeted by extortion and extortion-related shootings.

SPS’s Major Crime Section took over the investigation and two men have now been charged with Criminal Code offences.

Harshdeep Singh, a 20-year-old male, has been charged with one count of dangerous operation of a motor vehicle and one count of occupying a vehicle knowing a firearm is present.

Hanspreet Singh, a 21-year-old male, has been charged with one count of occupying a vehicle knowing a firearm is present.

The investigation is ongoing, and additional charges may be forthcoming.

Both Harshdeep Singh and Hanspreet Singh have been remanded in custody until January 30. They are both foreign nationals and SPS has engaged Canada Border Services Agency.

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As per survey,  Physicians in  Canada spend an average of nine hours per week, or nearly one-fifth of their total working time, on administrative tasks, amounting to about 42.7 million hours per year nationwide. Of this time, respondents estimate that 47% is spent on unnecessary tasks, representing approximately 19.8 million hours. This unnecessary time is equivalent to the work of 9,093 full-time physicians, or about 9% of Canada’s active physician workforce.

“Health care challenges, such as long wait times, emergency department closures, and staffing shortages, affect everyone, including family doctors that own practices. Doctors are spending too much time on work that could be eliminated entirely or done by someone else. Cutting red tape isn’t optional anymore, it’s a critical solution we can’t afford to ignore,” said Corinne Pohlmann, CFIB executive vice-president of advocacy.

Eliminating the 20 million hours of unnecessary paperwork and administrative tasks,  doctors face annually would free up the equivalent of 9,000 full time physicians, according to Losing doctors to desk work: Canadian physicians lose 20 million hours each year to red tape Opens survey.  The findings were released today as part of CFIB’s 17th annual Red Tape Awareness Week.

Most physicians (85%) said unnecessary work stems mainly from health-system processes, insurance companies (76%), government forms (59%), pharmacies (58%), and electronic record systems (51%). The most demanding tasks include insurance paperwork, referrals and test requisitions, and electronic documentation. The Disability Tax Credit, private insurance forms, and Canada Pension Plan Disability are among the most time-consuming forms.

The impact goes beyond lost time. Almost all (93%) of doctors say it disrupts work-life balance, 95% feel less fulfilled professionally, while 90% link it to burnout. More than half plan to cut their hours because of administrative burden, and 25% are even considering early retirement.

For individual doctors that means reclaiming up to 199 hours a year, more than a full month of working time.

If the administrative burden on physicians is reduced,  most doctors (79%) would reinvest the freed-up time to improve their work-life balance, 44% would spend more time with existing patients, and 43% would take on new ones , a strong majority (72%) support eliminating some administrative tasks and better system integration, particularly through the interoperability of patient care records (71%), Other top recommendations include simplifying insurer processes, delegating duties to other health professionals, and providing protected, paid administrative time and  Artificial Intelligence (AI) is another potential solution to save time and free up resources, with 28% of physicians currently using at least one AI scribe tool and another 42% expressing interest.

“Reducing paperwork would ease stress and give doctors more time for quality patient care, professional growth and personal well-being. Even small cuts to the physician administrative burden can make a big difference for millions of Canadians,” said Keyli Loeppky, CFIB director of interprovincial affairs.

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The new judges are Micah Rankin, Charles Hutchison  and Jodi Michaels.

Rankin will be assigned by the judiciary to Victoria. Rankin has more than 18 years of legal experience, having served as chair of the board of directors of Access Pro Bono and as pro bono counsel for several organizations, including the BC Civil Liberties Association. In 2011, Rankin was a founding member of Thompson Rivers University’s faculty of law, returning to practice in 2018 with the Ministry of Attorney General, before transitioning to the BC Prosecution Service’s Criminal Appeals and Special Prosecutions Unit in 2020. In recognition of professional excellence, Rankin was appointed King’s Counsel in 2023.

Hutchinson, who will be assigned by the judiciary to Prince George, brings more than 12 years of legal experience. Upon being called to the bar in 2013, Hutchinson opened a private practice in Prince George, focused on criminal defence, family law and child-protection matters. Since 2017, Hutchinson has also served as a local agent for Legal Aid BC in the Vanderhoof and Fort St. James area, overseeing intake services and working and travelling throughout northern B.C.

Michaels will be assigned by the judiciary to Dawson Creek. Michaels brings more than 13 years of legal experience across criminal, family and civil litigation. After being called to the bar in 2012, Michaels opened a practice handling criminal defence, family litigation and parents’ counsel work, as well as a broad range of civil matters, including civil forfeiture proceedings, ICBC plaintiff litigation, and residential tenancy and property disputes.

Judges are appointed after a multi-step process that starts with interested lawyers applying and the Judicial Council of B.C. reviewing the candidates. The council is a statutory body made up of the chief judge, an associate chief judge, other judges, lawyers and members from outside the legal profession. The council recommends potential judges to the attorney general, with the final appointment made through a cabinet order-in-council. Although judges are assigned to a judicial region, many use technology, such as videoconferencing, for court proceedings, enabling the court to provide timely access to justice for all British Columbians. Judges also travel regularly throughout the province to meet demands.

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Vancouver: The governments of Canada and Quebec are strengthening their collaboration to accelerate housing construction and support community development.

“Too many families in Quebec are still looking for a home that meets their needs. Municipalities need reliable, well‑adapted infrastructure to make that possible. By working closely with the Government of Quebec, we’re creating the conditions to speed up homebuilding, remove barriers, and deliver real solutions for communities.”  Said  Gregor Robertson, Minister of Housing and Infrastructure.

In order to ensure the harmonized deployment of Build Canada Homes in Quebec, in line with its priorities and jurisdictions, the governments of Canada and Quebec have signed a memorandum of understanding to guide their collaboration. Through a joint Collaboration Table, the two governments will work together to fund affordable housing projects aligned with shared priorities, simplify and accelerate approval processes, and ensure better coordination between government, municipal, and community partners.

The Government of Canada announced a $6 billion pan Canadian envelope for the Canada Housing Infrastructure Fund (CHIF) to accelerate the construction and upgrading of infrastructure that is essential for housing: drinking water supply, wastewater treatment, stormwater management, and certain solid waste solutions.

“The agreement announced  a major step forward in housing. It is significant and fully respects Quebec’s jurisdiction, priorities, and legislative framework. ”said Caroline Proulx, Minister Responsible for Housing and Minister Responsible for the Status of Women.

Recognizing that accelerating residential construction necessarily requires major infrastructure investments, the two governments also announced the signing of the Agreement on the Canada Housing Infrastructure Fund (CHIF). Under this agreement, the federal government will invest nearly $1 billion, which Quebec will be able to use in accordance with its guidelines and territorial needs to modernize and develop essential infrastructure—particularly in the areas of drinking water, wastewater, and stormwater—necessary for the completion of new housing projects.

CHIF helps communities secure the infrastructure capacity needed to support more housing and increase density. Funding can be invested in projects that improve drinking water, wastewater and stormwater infrastructure, as well as initiatives to preserve existing capacity, enhance network reliability, or implement waste diversion measures to reduce landfilling.

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