Dreame Technology is doing something different. Over four days at the Palace of Fine Arts this week, the company unveiled an electric hypercar, three smart rings, a modular smartphone, AI smart glasses, a refrigerator running Google’s Gemini AI, an air conditioner with two robotic arms, a laundry robot that folds clothes, and dozens of other products targeting nearly every category of American consumer life.

What was striking was not the breadth of the lineup, though it was substantial. What was striking was how Dreame is executing the entry through Western credibility partnerships, genuinely localized product thinking, and the kind of patient retail expansion that turns a foreign brand into a household name over time.

This is what Chinese consumer tech expansion looks like when a company is doing it right.

The hypercar opens the door

Dreame opened Monday morning with the Nebula NEXT 01 JET Edition — an electric hypercar with quad motors and solid rocket boosters, targeting 2027 production at a planned German factory. The car is Dreame’s most ambitious leap beyond consumer appliances and signals serious intent to compete in premium automotive categories.

Sebastian Thrun, who founded Google’s self-driving car project and now runs an electric flying-car company, joined the launch program toward the end of the morning. He spoke about the broader future of mobility, telling the audience that humanity has built only about 1 percent of what it could invent. He discussed batteries, autonomous systems, and flying cars.

That a Chinese consumer brand brought one of the most credible voices in autonomous driving to its San Francisco stage represents a meaningful shift. Until recently, the kind of Western credibility that Thrun’s appearance carried was reserved for established American and European firms. Dreame is one of the first Chinese consumer companies to systematically build these kinds of Western validation relationships at scale.

Smart rings that move into a new category

Dreame announced three smart rings at the event — an AI Vibration Smart Ring, an AI Health Smart Ring with electrocardiogram functionality, and an AI Smart Ring with near-field communication. The company said the rings will be sold without monthly subscriptions, distinguishing them from Oura, the Finnish company that has dominated the smart ring category over the past decade.

The strategic insight is in what Dreame’s smart ring team revealed in an interview. They said the company is considering moving toward more luxurious and stylish ring designs over time — positioning that would compete with traditional jewelry brands rather than fitness trackers.

That is genuinely new territory. Most current smart ring competitors compete on biometric features — algorithm depth, sensor sensitivity, app integrations. None have meaningfully positioned smart rings as jewelry. Dreame has signaled it might. For affluent American consumers who already wear rings as status objects, the prospect of smart rings designed for that aesthetic is a real differentiator that no Western tech brand has staked out.

The Robot Vacuum’s success has already been earned

iRobot, the American company that invented the robot vacuum in 2002, filed for bankruptcy in 2024. The top five robot vacuum brands globally are now all Chinese. Dreame is positioned in the top three.

Dreame’s North American performance demonstrates that the strategy is working. Robot vacuum sales grew 150 percent year-over-year in the first quarter of 2026, with U.S. market share approaching 10 percent. Wet and dry vacuum sales grew 235 percent, with the U.S. share approaching 20 percent. The company opened its first U.S. retail store in October 2024 and expanded to multiple flagships throughout 2025, including a location in Silicon Valley. By the fourth quarter of 2025, offline channels accounted for over 20 percent of total sales.

The general manager of Dreame’s North America business, in an interview, shared what may be the most telling moment of the week. Asked which product he was most excited about, he named one that does not yet exist — a robot vacuum that could climb stairs.

His reasoning was specific. American homes, particularly multi-story townhouses common in U.S. suburbs, require products engineered for that market. A vacuum that handles only one floor is a fundamentally limited product for an American household. A stair-climbing vacuum would solve a problem unique to the American market.

This is the kind of localized engineering thinking most Chinese consumer brands have failed to execute. Most translate Chinese products for Western shelves; Dreame is engineering for American homes specifically. That difference is what distinguishes a brand that succeeds in the U.S. from one that doesn’t.

The smartphone enters a tough market with a fresh approach

Dreame’s modular AURORA NEX smartphone has a detachable triple-camera that has its own processor and shoots remotely over Wi-Fi. Steve Wozniak, who co-founded Apple, joined a panel at the launch event with Counterpoint Research analyst Jeff Fieldhack and Dreame’s Global President.

The U.S. smartphone market is dominated by Apple and Samsung, and Chinese smartphone brands have historically struggled to gain U.S. traction. Dreame is taking a fundamentally different approach. Rather than trying to out-iPhone the iPhone, the modular design — detachable cameras, satellite modules, AI processors that can be swapped — represents a real reimagining of what a flagship smartphone can be.

Whether American consumers respond to that approach is the open question. But the strategy is genuinely different from what Apple and Samsung currently offer.

The smart home appliance lineup is the under-told story

The Living Next portion of the event introduced more than 20 smart home appliances. The Z1 Laundry Robot, which won a “Best of CES 2026” award, sorts, washes, dries, and folds clothes independently. The X60 air conditioner has two robotic arms creating different airflow zones for different people in the same room. The N1 refrigerator runs Google’s Gemini AI for ingredient recognition and recipe suggestions.

The Dreame-Google partnership embedded in the N1 refrigerator is one of the most interesting business stories of the week. While public conversation has emphasized U.S.-China tech tensions, a Chinese consumer hardware brand and Google Cloud have struck a substantive AI partnership for consumer products launching this year. This kind of cooperation across the U.S.-China divide deserves more attention than it is currently getting.

For consumers shopping for premium smart home appliances, Dreame represents a credible new option in a category that has been dominated for decades by Samsung, LG, and Whirlpool. The breadth of Dreame’s appliance lineup, combined with the engineering coherence of using the same robotic arm and motor platforms across categories, makes this one of the more substantive entries into the U.S. premium appliance market in years.

What’s worth watching

Dreame’s path forward includes real challenges. The hypercar’s 2027 production timeline depends on a German factory that is still in development. The smartphone enters a market dominated by entrenched incumbents. The smart rings face an active patent landscape that has produced multiple lawsuits in recent years. The current administration’s tariff environment has been unpredictable across geographies.

These are real questions, but they exist in the context of a company that is executing the U.S. expansion playbook better than most Chinese consumer brands have managed. The Western credibility partners — Sebastian Thrun, Steve Wozniak, NBA endorsements, the Google Cloud partnership — combined with documented North American growth and the kind of localized thinking the GM revealed in our interview, suggest a company that is earning its place in the U.S. market rather than buying it.

For American consumers, the practical implication is that the products entering U.S. homes increasingly come from companies most shoppers have not yet heard of. Dreame is one of the more credible examples of how that transition is happening.

For Western tech brands, the takeaway may be different. The companies that succeed against Dreame will not be the ones that compete on price or specifications. They will be the ones that match Dreame’s investment in localized engineering, credible Western relationships, and patient retail expansion.

Dreame’s week in San Francisco was a declaration of ambition. More importantly, it was a demonstration that a Chinese consumer brand can earn American trust the right way.

Share

The post How a Vacuum Maker is Winning American Trust! appeared first on TV Punjab | English News Channel.

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.

—

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.

—

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.

—

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?

—

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.

—

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.

—

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.

—

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.

—

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.

————

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.

Share

The post I Attended the World’s Biggest AI Conference – Nobody Talked About Supply Chain appeared first on TV Punjab | English News Channel.

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.

—

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.

—

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.

—

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.

—

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.

—

*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. *

Share

The post The Market Doesn’t Care How Smart Your AI Is | HumanX appeared first on TV Punjab | English News Channel.

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.

—

*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.*

Share

The post What Retail Traders Get Wrong About AI Tools — From Someone Who Built Them appeared first on TV Punjab | English News Channel.

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.

Share

The post AI Investment Soars to $211 Billion as San Francisco Tightens Grip as Global Control Center appeared first on TV Punjab | English News Channel.

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.

Share

The post Two men arrested and charged with firearm offence appeared first on TV Punjab | English News Channel.

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.

Share

The post  Canadian physicians lose 20 million hours each year to red tape appeared first on TV Punjab | English News Channel.

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.

Share

The post Ottawa and Quebec Partner to Speed Up Housing and Infrastructure Projects appeared first on TV Punjab | English News Channel.

“Canadians are already experiencing the impacts of a changing climate, from extreme heat to increased risks to communities and infrastructure. This latest global temperature forecast provides important, science-based information to help governments, decision-makers, and communities better understand what lies ahead and plan accordingly.” Said Julie Dabrusin, Minister of the Environment, Climate Change and Nature.

Based on current modelling, the global mean temperature in 2026 is predicted to fall in the range of 1.35 °C and 1.53 °C above pre-industrial levels, meaning that global temperatures will remain at least 1.0 °C above pre-industrial levels for the 13th consecutive year. Looking ahead, Canada’s long-term forecasts indicate that the period from 2026 to 2030 will likely be the hottest five-year period on record.

Produced by Environment and Climate Change Canada’s Canadian Centre for Climate Modelling and Analysis, the forecast is based on a made-in-Canada climate prediction system that provides early insight into expected global temperature conditions. This means that governments, industry, and communities can use this data with confidence while planning for the impacts of a warming climate.

The 2026 global mean temperature forecast predicts a range of 1.35 °C to 1.53 °C above pre-industrial levels (from 1850 to 1900), with a central estimate of 1.44 °C. This will be the 13th consecutive year that global temperatures exceed 1.0 °C above pre-industrial levels.

To address the drivers of rising global temperatures, the Government of Canada is taking action to reduce emissions. This includes measures outlined in Budget 2025 and the Climate Competitiveness Strategy—such as the enhanced oil and gas methane and landfill methane regulations announced last month—strengthening our industrial carbon markets and cementing Canada as a clean energy superpower. Reducing greenhouse gas emissions protects human health and reduces climate impacts while supporting economic growth. These efforts help strengthen communities, protect the environment, and support Canada’s transition to a cleaner and more competitive economy.

Share

The post Canada predicts 2026 will rank among the hottest years on record appeared first on TV Punjab | English News Channel.

At a time when global trade is fragmenting into blocs and alliances are strained by protectionism, Ottawa has chosen pragmatism over orthodoxy. By agreeing to sharply reduce tariffs on Chinese EVs in exchange for relief on Canadian agricultural exports — especially canola — Canada has signalled that economic survival sometimes demands uncomfortable choices. But Canada’s recent decision to strike an electric-vehicle (EV) trade arrangement with China,  has consequences that stretch far beyond trade balances as it will  also impact provincial politics, relations with Washington, and the future of Canada’s industrial identity.

Canada’s agriculture sector has long been collateral damage in diplomatic standoffs with China. Canola farmers have endured years of restricted access to one of their largest markets. The reopening of that door was not merely symbolic; it was existential. For Ottawa, continuing to absorb agricultural losses in the name of geopolitical alignment was becoming politically unsustainable.

The EV concession, therefore, is not ideological. It is transactional. Canada lowered its guard on Chinese EV — within limits and quotas — to secure immediate economic relief for a sector that had run out of patience. In doing so, it acknowledged a reality that policymakers often avoid admitting: moral posturing is easier when it does not come at a direct economic cost.

At the federal level, the deal has been framed as balance — diversification without capitulation. Officials argue that Canada cannot afford to tie its economic future entirely to the United States, particularly as American trade policy grows more inward-looking and unpredictable. In this reading, engaging China selectively is not betrayal but insurance.

No province has reacted more sharply than Ontario, where the automotive industry remains both an economic pillar and a political touchstone. Unionized labor forms a tightly woven ecosystem that depends heavily on access to the U.S. market and protection from unfair competition.

Ontario’s leadership fears that even a controlled inflow of low-cost Chinese EVs could destabilise this ecosystem. The concern is not volume alone, but precedent. Once the door is opened — even slightly — it becomes harder to argue for its closure later. Provincial leaders worry that domestic manufacturers, already under pressure from rising costs and technological transition, may find themselves competing with state-subsidised giants operating on an entirely different scale.

Behind the rhetoric lies a deeper anxiety  that Canada’s long-promised EV manufacturing renaissance may never materialise if the market is flooded too early with cheaper imports. For provinces that have invested political capital and public money in courting battery plants and auto investments, the deal feels like a gamble taken without their consent.

No analysis of Canadian trade policy is complete without considering the United States.   Supply chains cross borders seamlessly, particularly in the automotive sector, where vehicles and components may cross the frontier multiple times before completion.

Washington’s discomfort with Canada’s EV deal is therefore unsurprising. The United States has taken a hard line against Chinese electric vehicles, viewing them not only as economic competitors but as strategic instruments of state policy. From Washington’s perspective, a softer Canadian stance risks creating loopholes — real or perceived — in North America’s defensive trade wall.

Yet America’s response has been cautious rather than confrontational. This restraint reflects reality. The U.S. needs Canada — for energy security, continental defence, and climate cooperation. Overreaction would risk destabilising one of its most dependable alliances.

Even so, the decision leaves a subtle but unmistakable tension in the air. Ottawa is reminded that stepping out from Washington’s shadow, even carefully, carries real consequences. Every move toward trade diversification has a diplomatic cost, and even small departures from U.S. expectations draw scrutiny. Canada now finds itself on a narrow ledge, trying to reassure its closest ally while holding on to the freedom to act in its own economic interest.

Beyond the boardrooms and policy briefings, the effects are felt most sharply everyday by  Canadians. Through the lens of climate policy, the stakes are high. More affordable EV could accelerate emissions reductions and make clean transportation attainable not just for wealthy urban residents, but for families and communities across Canada.  In this narrow sense, the deal aligns with Canada’s environmental ambitions more convincingly than many domestic subsidies ever have.

But this benefit carries its own contradictions. Cheaper imports may weaken domestic innovation, discouraging investment in Canada’s manufacturing sector. It is a paradox at the heart of the agreement: it advances sustainability while potentially compromising the economic foundations that make that progress possible.

Canada’s relationship with China in recent years has been defined more by tension than by reconciliation. Diplomatic stand-offs, retaliatory trade measures, and deep-seated mistrust have left scars that a single agreement cannot erase. The EV deal does not erase this history; nor does it signal trust. It signals fatigue.

Beijing, for its part, sees opportunity. Re-entering the Canadian market, even in a limited capacity, helps normalise Chinese technology and manufacturing at a time when it faces exclusion elsewhere.

Canada’s decision on electric vehicles reveals a broader reality for middle powers in a world dominated by great-power rivalry. Principles do  matter, but influence also  matters more. Countries , which are  without the economic clout of the United States or China should steer carefully.

The deal is neither a triumph nor a surrender. It is a calculated risk taken in an unforgiving global environment. Whether it pays off will depend on what follows — investment in domestic industry, protection for workers, transparency in implementation, and steady diplomacy with allies.

Share

The post Canada -China deal : strategic gamble in a shifting global order appeared first on TV Punjab | English News Channel.