Karsten Wenzlaff, Advisor
August 26th, 2025
August 31, 2026 | NCFA Insight | Artificial Intelligence And Data, Competition And Market Structure, Data Privacy And Governance

On August 31, 2026, OpenAI said ChatGPT Ads reached a US$1 billion annualized revenue run rate in less than 200 days. Tens of thousands of advertisers are using the platform, OpenAI reports more than one billion weekly active ChatGPT users, and ads are available across more than 40 countries through direct sales, agencies and technology partners.
US$1 billion is a run rate, not revenue already collected. At that pace, ChatGPT Ads would produce roughly US$250 million per quarter. That's small beside the companies OpenAI now competes with for advertising budgets, but getting there in less than seven months gives advertisers, competitors and regulators something tangible to watch.
Notably, on the same day OpenAI announced the milestone, European regulators designated ChatGPT a Very Large Online Search Engine under the Digital Services Act. In the United States, the FTC and 22 states sued Amazon over alleged practices in its advertising auctions. Google is already operating under court ordered search remedies after losing a U.S. monopolization case.
OpenAI is building its advertising business in a market where regulators already know how digital distribution, auction rules, data and default positions can concentrate power.
ChatGPT Ads started as a U.S. test on February 9 for adult Free and Go users. Canada, Australia and New Zealand followed in the spring. OpenAI added self service Ads Manager and cost per click buying in May, expanded into the U.K., Mexico, Brazil, Japan and South Korea in August, and is now adding more markets across Europe, India, the Middle East and North Africa.
The product itself has grown just as quickly. CPC and outcome optimized bidding now account for most campaigns, according to OpenAI. Advertisers can use Pixel and Conversions API measurement, product feeds, geographic targeting and custom audiences, while more than 50 technology and measurement partners connect businesses to the platform.
OpenAI says one ecommerce advertiser produced a 3x return on ad spend over 28 days, while one technology partner reported that more than 80% of traffic generated by ChatGPT ads came from new customers. Those are select examples supplied by OpenAI, so they shouldn't be treated as typical campaign performance. OpenAI also says it doesn't yet have reliable performance benchmarks across industries and campaign types.
Back in February, NCFA asked whether conversational advertising could develop into a new route for product discovery. Six months later, OpenAI has a US$1 billion annualized ad run rate, self service buying, conversion tools and tens of thousands of advertisers. Businesses will pay to advertise inside AI conversations, and enough of them are doing so to create a sizable new revenue stream.
Alec Shao, Newegg performance marketing manager, an advertiser on OpenAI's own ads site explains the attraction directly:
“We want Newegg to be part of the conversation when someone is researching what to buy.”
OpenAI isn't close to Google in advertising revenue. Alphabet reported US$81.63 billion of Google advertising revenue in Q2 2026. Search and other advertising alone generated US$63.27 billion, up from US$54.19 billion a year earlier.
Meta generated US$59.36 billion of Q2 advertising revenue, while Amazon generated US$19.81 billion from advertising services. OpenAI's roughly US$250 million quarterly equivalent makes clear how much ground separates a fast growing new entrant from established digital advertising businesses.
However, ChatGPT offers advertisers something those numbers don't capture. Search advertising usually begins with a query. Social advertising often begins with interests, behaviour and attention. Amazon can catch a shopper close to purchase. A ChatGPT conversation can contain a goal, budget, constraints, preferences and several rounds of comparison before an ad is selected.
OpenAI says its ad system considers the context and intent of the current conversation, the ad and landing page, advertiser supplied context hints and, when personalization is enabled, selected information from a user's wider ChatGPT experience. Advertiser hints describe relevant conversations, topics or keywords, but they aren't exact match search keywords.
The auction is already becoming familiar territory for performance marketers. OpenAI supports CPM and CPC buying and recommends starting CPC bids around US$3 to US$5. It says eligible ads compete through a relevance weighted second price auction.
Whether a detailed conversation produces a better customer than a search keyword hasn't been established. Advertisers now have enough buying and measurement tools to start finding out.
Regulators may reach AI advertising earlier than they reached search and social platforms. Google is already operating under court ordered search remedies, while Amazon now faces allegations over how its ad auctions were priced. OpenAI is entering the same commercial territory with regulators already alert to distribution control, auction transparency, data use and platform power.
ChatGPT Ads is still small beside Google, Meta and Amazon, but it reached a US$1 billion annualized run rate in less than 200 days. Regulators may not need to wait for comparable scale before asking how the market works, especially if distribution, data or auction rules begin limiting competition.
Canadian advertisers can already create and manage ChatGPT Ads through OpenAI's self service Ads Manager, with a minimum daily budget of C$25. Canadian financial firms face an important limit, though. OpenAI currently generally prohibits financial services ads outside the United States, where approved advertisers can be accepted case by case for products such as credit cards, mortgages, brokerages, insurance, loans and payments.
OpenAI treats financial services as a restricted category requiring extra safeguards, including enhanced advertiser verification or manual review, and says advertisers may have to prove they are properly licensed.
Financial software, budgeting tools and educational products may have more room to experiment. For firms that can participate, can ChatGPT produce customers at a better cost and with stronger intent than search, social media, affiliates or other channels?
Will regulators act on AI advertising before market power becomes difficult to unwind?
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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August 28, 2026 | NCFA Market Activity + Insight | Capital Markets And Market Infrastructure, Risk Compliance And Regtech, Regulation And Policy, Wealthtech Investing And Trading

On August 28, 2026, the Commodity Futures Trading Commission penalized Gabriel Perez for prediction market insider trading after finding that he used confidential presidential speeches to trade event contracts for his own benefit. Perez worked as a White House technical adviser and teleprompter operator, which gave him access to prepared remarks before President Donald Trump delivered them.
Perez generated US$107,539.02 in profits by trading contracts on words and phrases the President would mention. He must return those profits, pay a US$65,000 civil penalty, stop violating the Commodity Exchange Act and stay out of CFTC regulated trading for three years. Perez consented to the settlement without admitting the CFTC's findings or legal conclusions, and the Commission says his cooperation justified a substantial reduction in the civil penalty. The CFTC also thanked KalshiEX for assisting the investigation.
As event contracts attract more volume, products and mainstream distribution, exchanges need to do more than price outcomes and settle trades. They need credible ways to identify when someone may know the answer before everyone else.
A mention market lets traders take a Yes or No position on whether a word, phrase or term will appear during a defined event. Perez opened his Kalshi account on December 8, 2025 and traded markets tied to presidential speeches, including addresses, rallies, policy remarks and the State of the Union.
The CFTC order says Perez generally saw prepared remarks about an hour before the President spoke. He bought Yes contracts when the target word appeared in the speech and No contracts when it did not. On one occasion, he changed his position after watching the President skip part of the prepared text.
Perez traded across 14 presidential mention markets and made money on 39 of 43 contracts. He was not making a better forecast than other traders. He had already seen the prepared remarks and knew whether many of the words being traded were present.
Traditional financial markets already deal with executives, advisers and employees who may hold valuable information before investors receive it. Event contracts can create a much wider group of people with direct knowledge of an outcome. A political speech can involve writers, production staff, government employees and technical crews, while sports, entertainment and corporate events can involve players, coaches, producers, employees, advisers or others close to the result.
That risk was visible before federal enforcement arrived. Kalshi's earlier insider trading cases included a MrBeast editor and a California political candidate, and the exchange said it had opened roughly 200 investigations or probes. Those cases showed that integrity work was already becoming part of running an event market. The Perez action is more consequential because the CFTC is now applying federal commodities law directly to misuse of confidential information in prediction market contracts.
The integrity problem can also extend beyond advance knowledge. Some traders may know an outcome early, others may be able to influence it, and some may hold information through a public duty or private relationship. That makes the source of the information as important as the trade itself.
The CFTC order and what the public record shows is that investigators could connect Perez's account, government role, speech access, trading times and profits. For prediction markets, knowing who is behind an account matters as much as spotting an unusual trade.
Traditional surveillance remains important. Exchanges can look for unusual profits, concentrated positions, repeated success, trading immediately before an event and activity that doesn't fit a customer's normal behaviour. Prediction markets add another requirement because suspicious trading may only make sense once the account is connected to a job, relationship or source of access outside financial markets.
A trader repeatedly winning presidential speech contracts becomes far more interesting if the exchange or regulator also knows that person works on presidential events. The same logic applies to sports personnel trading injury or lineup contracts, employees trading corporate outcomes or production staff trading entertainment events.
Exchanges need to know who is trading, what access they may have to the event and whether their trading pattern fits that access. Reliable customer identity, account history and information about relevant jobs or relationships can help investigators decide whether an unusual trade deserves a closer look. Surveillance teams need tools that can connect trading patterns with occupations, relationships and event access. Case management, alert review and auditable investigation records become more important as the number of contracts and traders grows.
This boosts the commercial case for regulated event contract infrastructure. Market surveillance, identity controls, outcome verification, compliance workflows, investigation tools and regulator reporting are becoming part of what platforms need to operate credible markets, alongside matching, pricing and settlement.
Different contracts also require different surveillance assumptions. An inflation contract settles on a formal public release. A presidential mention contract may depend on a speech seen by staff shortly before delivery. A sports contract can depend on injury or lineup information known to a relatively large group before the public learns it. Exchanges need to understand how each event is produced, who may know the answer early and who can influence the result before they can decide what suspicious trading looks like.
Prediction markets are reaching customers through larger financial platforms, which brings more liquidity but also more accounts and more activity to monitor. Recent CSA and CIRO guidance on prediction markets keeps sports and entertainment event contracts outside Canada's securities dealer channel, while Wealthsimple Investments and Interactive Brokers Canada can offer a narrower set of economic, environmental and financial contracts under CIRO conditions.
The CFTC case also shows that regulators are prepared to use existing commodities rules when confidential information is abused. Exchanges and distributors therefore need to spot suspicious activity early, connect it to useful account information, investigate it and keep records that can support enforcement.
That creates a practical market for surveillance, identity, behavioural analytics and case management tools. Prediction markets have already proved they can attract products, liquidity and mainstream distribution, but can market integrity keep up.
Can prediction markets scale faster than their ability to detect who knows the outcome before everyone else?
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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Aug 31, 2026

Anyone who moves money online eventually thinks about the network they are moving it over. Public Wi-Fi at a co-working space, a hotel connection during a conference, a café network shared with fifty strangers: all of these sit between your device and whatever platform holds your funds. Lets VPN, listed on app stores as "Lets VPN - The VPN that Always Connects" and published under the name LetsGo Network, is one of the tools people reach for in those moments.
It is a Tools-category app with a narrow job. It routes your traffic through a server you pick, encrypts it in transit, and does so without asking who you are.
The pitch from the developers is short. Unlimited data on every monthly plan, no registration and no personal information required, a free download, and a list of servers you choose from yourself rather than being auto-assigned to whatever the app thinks is closest.
That second point matters more than it looks in a fintech context. Most VPN services want an account, which means an email address, sometimes a phone number, and a billing record that ties your identity to your connection history. Lets VPN's stated approach skips the registration step entirely, which shrinks the amount of data any single party holds about you.
The manual server picker deserves a second look too. If you are connecting to a banking portal or an exchange that flags logins from unexpected regions, being able to pick a consistent server yourself avoids the whack-a-mole of geographic fraud alerts.
The obvious audience is the traveller. Founders on the road, contractors billing across borders, anyone checking a payments dashboard from an airport lounge.
Then there is the small business owner running operations from a laptop and a phone with no IT department behind them. A one-person consultancy handling client invoices does not have a corporate VPN concentrator to dial into. A consumer-grade tool that connects reliably and does not require ongoing administration fills that gap reasonably well.
Crypto and web3 users form a third group, often for reasons that have less to do with secrecy than with network hygiene. Wallet interfaces, node dashboards and DeFi front-ends are frequent phishing targets, and reducing exposure on untrusted networks is basic practice.
A caveat before going further: a VPN encrypts transport. It does not authenticate you, it does not protect a compromised device, and it will not save you from approving a malicious transaction. Treat it as one layer, not a security posture.
Financial work happens on big screens. Spreadsheets, treasury dashboards, tax software, three browser tabs of reconciliation. If your VPN only runs on a phone, your protected session and your actual work session are on different devices.
There is also a workflow problem. Toggling a connection on a phone while reading a compliance document on a monitor is friction, and friction is why people skip the security step. Having the connection control in the same place as the work makes it more likely to be used.
Lets VPN is distributed as an Android app, so running it on Windows means running Android on Windows.
An Android emulator creates a virtual Android environment on your PC, and Lets VPN behaves inside it much as it would on a handset. BlueStacks is the usual starting point for people who have never done this before. Users who want something lighter on system resources sometimes prefer LDPlayer instead.
Two things specific to this app are worth getting right.
First, the VPN tunnel inside an emulator generally protects traffic from apps running inside that emulator, not your host Windows browser. If your goal is protecting your everyday desktop browsing, verify what is being routed before you assume you are covered. Some emulator configurations share the host network stack in ways that make this behave differently than you expect.
Second, when the app asks for VPN permission on first launch, that dialog comes from Android's own permission layer, not from the app. Granting it inside the emulator is required for the tunnel to establish at all, and denying it by reflex is the most common reason people report the connection failing on desktop.
That is largely why people look up Lets VPN download for PC instead of just running the phone version: checking two or three servers for latency before committing is a task that takes five minutes on a monitor with a speed test tab open, and considerably longer squinting at a phone screen.
Multitasking is the real difference. Switching servers mid-session on mobile means leaving whatever you were doing; on desktop it is a window you alt-tab to.
For a business, personal VPN tools sit in an awkward spot. They are useful and they are also invisible to whoever is responsible for security oversight.
If your team handles customer financial data, a consumer app installed on individual laptops through an emulator is not an audit-friendly answer. It works for a solo operator or a founder in transit. It does not substitute for a managed solution with logging, device policy and revocation when someone leaves.
Ask yourself which of those two situations you are in before standardising on anything.
Free tiers and paid plans on VPN apps change, and the store description's mention of unlimited data applies to monthly plans specifically, so check current terms rather than assuming.
The more durable caveat is trust. Routing traffic through a VPN moves your visibility from your internet provider to the VPN operator, and a no-registration policy tells you about data collected at signup, not about what happens at the server. That trade is often worth making on hotel Wi-Fi. It is a different calculation for something you leave running all day on a machine that touches client money.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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Aug 28, 2026

Image: Unsplash/Jakub Żerdzicki
Artificial intelligence is rapidly changing the way individual investors interact with financial markets. Tasks that once required hours of reading reports, comparing charts, or building spreadsheets can now be compressed into a few prompts or clicks.
Modern AI investing tools can summarize market news, screen thousands of securities, identify unusual price movements, compare financial metrics, analyze sentiment, and even help investors test trading ideas.
That convenience is valuable. But it also creates a new problem: when sophisticated analysis appears instantly on a screen, it is easy to confuse speed with reliability.
For investors, the real opportunity is therefore not simply finding the most advanced AI. It is learning where AI adds value, where it can fail, and which decisions should always remain subject to independent due diligence.
The term covers a surprisingly broad range of financial technologies.
Some tools use machine learning to identify patterns in historical market data. Others apply natural language processing to earnings reports, central-bank announcements, news stories, or social-media discussions. Generative AI assistants can explain financial concepts, summarize research, compare investment ideas, or help users create screening criteria.

Image: Pexels/Matheus Bertelli
In practice, retail investors are likely to encounter AI across several areas:
The important distinction is that these tools do not all perform the same job.
An AI assistant that summarizes an earnings call should be evaluated differently from an algorithm that generates trading signals. Likewise, a portfolio risk analyzer presents a very different level of financial consequence from software capable of automatically executing trades.
One of the easiest mistakes is choosing an impressive AI application before deciding what problem actually needs to be solved.
A better approach is to begin with a specific investment task.
| Investment task | How AI may help | What still needs human verification |
|---|---|---|
| Researching a company | Summarize filings, news and earnings calls | Financial statements and original disclosures |
| Finding opportunities | Screen large datasets quickly | Whether the screening logic makes economic sense |
| Monitoring markets | Detect unusual changes or sentiment shifts | Why the change occurred and whether it matters |
| Managing risk | Analyze correlations and portfolio exposure | Personal risk tolerance and liquidity needs |
| Comparing trading platforms | Organize fees, features and trading conditions | Regulation, execution quality and actual costs |
| Generating trade ideas | Identify historical patterns | Whether the pattern remains relevant today |
This simple framework changes the role of AI.
Instead of asking, “What should I invest in?”, an investor might ask, “Which companies in this sector have improving margins, declining debt and positive free cash flow?”
The second question gives AI a defined analytical task rather than handing it an open-ended financial decision.
AI is particularly useful when the bottleneck is information volume.
Imagine following several currencies, commodities, central-bank decisions and economic indicators. Reading every announcement manually can quickly become impractical. An AI system can summarize information and highlight developments that may deserve closer attention.
But summarization is not the same as verification.
A model may misunderstand context, rely on incomplete information, overlook a change that occurred after its underlying dataset was created, or confidently present an incorrect conclusion.
That leads to a useful rule:
AI should reduce the amount of information you need to inspect, not remove the need to inspect important information altogether.
For consequential decisions, investors should return to primary sources whenever possible.
If an AI summary says a company changed its guidance, verify the announcement. If it claims a central bank changed policy, read the official statement. If it identifies an unusual fee or condition at a financial platform, confirm it directly with the provider.

Image: Pexels/Tiger Lily
This distinction becomes especially important in actively traded markets such as forex.
AI can help an investor analyze inflation data, interest-rate expectations, currency correlations, technical indicators or market sentiment. None of those capabilities, however, answer another fundamental question:
Where will the trade actually be executed?
Broker selection involves a different set of variables:
These factors should be researched independently from any AI-generated market signal.
For example, an investor researching the trading infrastructure available in the forex market can use an independent comparison resource such as iamforextrader.com/en/forex-brokers/best/ as one starting point, and then verify relevant regulatory and account information directly with the broker and applicable regulator.
The principle applies beyond forex as well. A good investment idea and a trustworthy platform are two separate questions.
Every financial model has boundaries.
Traditional quantitative models depend on the variables selected by their designers. AI systems may work with much larger datasets, but they still operate within informational limits.
Before trusting an AI-generated conclusion, consider five questions:
The last question is especially important.
The more authority an AI system receives, the higher the standard of oversight should become.
A useful financial tool should help users understand why a result appeared.
Suppose two platforms both generate a “buy” signal.
Tool A explains that the signal resulted from improving earnings expectations, falling valuation multiples and increasing free cash flow.
Tool B simply displays:
AI Confidence Score: 94% — Strong Buy
The second interface may look more sophisticated, but it actually gives the investor less useful information.
An unexplained confidence percentage can create false precision. Without knowing the inputs, methodology, testing conditions or assumptions behind a signal, users cannot properly evaluate its reliability.
This is why explainability matters in financial technology.
Investors do not necessarily need access to every line of code, but they should be able to understand the basic logic behind a recommendation.
AI trading products often attract attention with historical performance.
Backtesting can certainly be useful. It allows investors and developers to see how a strategy would have behaved under previous market conditions.
But historical success can become misleading when a model has effectively been optimized to explain the past.
This is known as overfitting.
A strategy may perform exceptionally well because it has learned patterns that happened to exist in a particular dataset rather than patterns likely to persist in future markets.
When evaluating a data-driven strategy, look beyond headline returns and ask about:
A backtest that ignores realistic trading costs is particularly questionable for strategies that trade frequently.

Image: Pexels/cottonbro studio
AI investing tools can also create privacy and cybersecurity considerations.
Investors should be cautious about entering sensitive information into general-purpose AI systems, particularly:
Before connecting any application directly to an investment account, understand exactly what permissions it receives.
Read-only portfolio access is very different from permission to execute trades or transfer assets.
The principle of least privilege works well here: give a financial application only the access it genuinely needs.
Investors do not need to choose between artificial intelligence and traditional research. The two can complement each other.
A practical workflow might look like this:
Step 1: Use AI for discovery.
Screen markets, identify unusual developments or generate research questions.
Step 2: Ask for reasoning.
Request the factors behind the conclusion rather than accepting a score or recommendation.
Step 3: Verify important facts.
Check financial statements, regulator databases, company announcements and original economic data.
Step 4: Challenge the thesis.
Ask what could make the investment idea wrong.
Step 5: Evaluate execution conditions.
Understand fees, liquidity, spreads, platform risks and regulatory protections.
Step 6: Size the risk independently.
A high-confidence AI prediction should never automatically determine how much capital is placed at risk.
This workflow turns AI into an analytical assistant rather than an autonomous decision-maker.
The evolution of AI investing tools is part of a broader transformation in fintech.
Retail investors increasingly have access to analytical capabilities that were once expensive, technically difficult or available mainly to professional institutions. That democratization of financial technology can improve access to information and make research considerably more efficient.
But better technology does not eliminate uncertainty.
Markets still respond to changing expectations, unexpected events, human behaviour and information that models cannot perfectly anticipate.
The investors who benefit most from AI may therefore not be those who automate the greatest number of decisions. They may be those who learn to divide the investment process intelligently between machines and humans.
Let AI search faster, process more information and challenge assumptions.
Keep verification, risk tolerance and final accountability human.
The National Crowdfunding & Fintech Association (NCFA Canada) is a financial innovation ecosystem that provides education, market intelligence, industry stewardship, networking and funding opportunities and services to thousands of community members and works closely with industry, government, partners and affiliates to create a vibrant and innovative fintech and funding industry in Canada. Decentralized and distributed, NCFA is engaged with global stakeholders and helps incubate projects and investment in fintech, alternative finance, crowdfunding, peer-to-peer finance, payments, digital assets and tokens, artificial intelligence, blockchain, cryptocurrency, regtech, and insurtech sectors. Join Canada's Fintech & Funding Community today FREE! Or become a contributing member and get perks. For more information, please visit: www.ncfacanada.org
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