Mortgage delinquencies remain relatively contained nationally, but Ontario and B.C. continue to show more pronounced stress as first-time buyers increasingly rely on joint mortgages.
Ontario, B.C. mortgage stress rises as joint borrowing grows: Equifax
Why the Latest AI Model Isn’t Always the Best Business Decision
Opinions expressed by Entrepreneur contributors are their own.
Key Takeaways
- Implementing a new AI tool every time you make a slight update can actually yield a worse result, because after all the work that goes into the new model, the customer may not even see an improvement on their end.
- A technically better model does not automatically make it a better business decision.
Founders often assume that every improvement in AI model accuracy deserves a production release. But when testing, deployment, monitoring and engineering labor are factored in, deploying a slightly better model can actually produce a worse business outcome.
Imagine your AI team has trained a new model that performs 0.2% better than the version currently serving customers. Naturally, the data scientists are pleased and the automated pipeline marks the candidate as superior, leading everyone to assume it should immediately replace the existing model. But that is when the real production work begins.
The candidate must pass rigorous security and integration tests before engineers can package it, deploy it into a test environment and validate its behavior. Furthermore, the team might need to run a shadow or canary release, update monitoring rules, document the changes and prepare a comprehensive rollback plan. By the time this new model finally reaches production, the company has spent significantly more than the original training cost, yet customers may never even notice the improvement.
This highlights one of the most expensive misunderstandings in applied artificial intelligence: A technically better model is not automatically a better business decision.
Accuracy and business value are not the same thing
Accuracy measures technical performance, whereas business value measures whether that performance actually improves an outcome your company cares about.
Consider two different AI systems. The first detects potentially fraudulent financial transactions, where a small increase in recall could help identify additional fraud, prevent losses and protect customers. In this high-stakes scenario, even a fraction of a percentage point can produce substantial value when the system processes millions of transactions.
Conversely, imagine a second system that summarizes internal help-desk tickets. A similar improvement in an offline metric here might be statistically valid, but it remains practically invisible in daily operations. Employees likely won’t finish their work noticeably faster, meaning the company won’t see a reduction in support costs. Although the technical improvements in both scenarios are similar, the economic value is vastly different.
Before approving a new model, you must determine what one unit of improvement is actually worth. That value might be expressed as:
- Fraud losses avoided
- Additional purchases converted
- Employee hours saved
- Customer complaints prevented
- Forecasting errors reduced
- Manual reviews eliminated
If your team cannot connect the model’s improved accuracy to one of these tangible outcomes, the company does not yet have enough information to justify the release.
Count the complete cost of a model update
Many companies miscalculate the cost of an AI update by looking solely at training compute, which is like estimating the cost of opening a restaurant by counting only the price of the oven. Training is just one small part of a much larger system.
As highlighted in Google’s research on hidden technical debt in machine learning systems, model code is only a fraction of a production AI system. Data dependencies, testing, monitoring and supporting infrastructure create substantial long-term complexity. Furthermore, Google’s ML Test Score framework demonstrates that production readiness depends on far more than a model’s offline quality score.
A realistic cost calculation should include:
- Data preparation and validation
- Model training and experimentation
- Security and privacy testing
- Fairness or robustness evaluation
- Container or package creation
- Dependency and vulnerability scanning
- Integration testing
- Infrastructure provisioning
- Shadow or canary testing
- Monitoring changes
- Documentation and approval
- Engineering review
- Incident and rollback risk
- Potential customer disruption
This distinction matters immensely because an automated training pipeline can make experimentation appear artificially inexpensive. The truly costly work often begins only after training, right when a candidate enters the production-release process.
In my peer-reviewed IEEE Access research on the Retraining-Efficiency Score, I studied a highly relevant question: When should an organization promote a newly trained forecasting model instead of retaining its existing one?
After evaluating 2,320 controlled runs across four public time-series datasets and four forecasting architectures, the results were clear: Organizations do not have to choose between continuously releasing new models and leaving an old model untouched indefinitely. Instead, a selective promotion policy allows you to retain the current model when the expected improvement is too small and approve a new one only when the benefits justify the operational costs.
Founders can apply this principle without implementing a complicated mathematical framework by simply requiring their team to answer four critical questions before releasing any model:
1. Did the model improve a business-relevant outcome? Do not accept “the score increased” as a complete answer. Demand to know which metric improved, why that metric matters and whether it directly correlates with a customer or operational outcome. An improvement in a laboratory benchmark often fails to translate into a real-world production benefit.
2. Will customers or operations notice the difference? A technically measurable change can still be commercially irrelevant. Estimate how many decisions, users or transactions the change will affect, and then calculate whether it will materially improve revenue, risk, cost, speed or the overall customer experience.
3. What is the complete cost of releasing it? This must include training, testing, security review, deployment, monitoring and engineering labor. Crucially, you must also account for opportunity cost; every hour spent releasing a marginally better model is an hour that cannot be used to improve the core product, repair a reliability problem or build a highly requested feature.
4. Does the improvement justify the cost and additional risk? Compare the expected value of the improvement against the complete release cost. A company should promote the candidate only when the answer is a definitive yes. If the business case is uncertain, the disciplined choice is to retain the current model, collect more evidence and reevaluate later.
Keeping the current model can be the disciplined decision
Because AI teams are often rewarded for releasing new models, retaining an existing one can falsely appear as stagnation. In reality, keeping a model that already meets customer expectations, has predictable costs and possesses a known risk profile is often the smarter engineering choice.
A new model, despite a superior offline score, introduces uncertainty. It might fail on uncommon inputs, disrupt downstream systems or generate novel errors. This means model development and model promotion must be treated as entirely separate decisions. Your team should continue experimenting and training candidates without feeling obligated to push every “winner” into production.
Founders apply rigorous financial discipline to hiring and product development; AI releases deserve that exact same scrutiny. Because every new model consumes capital, operational attention and engineering bandwidth, it must offer a tangible return.
To enforce this, require a simple record for every proposed release detailing the technical improvement, its expected business value, the complete deployment costs and any new risks. Over time, this documentation will reveal which upgrades create genuine value versus those that merely make internal dashboards look better.
Ultimately, the goal is not to stifle innovation, but to direct it toward outcomes your customers and business can actually feel. The next time your AI team presents a more accurate model, do not simply ask whether it is better. Ask whether it is better enough.
Key Takeaways
- Implementing a new AI tool every time you make a slight update can actually yield a worse result, because after all the work that goes into the new model, the customer may not even see an improvement on their end.
- A technically better model does not automatically make it a better business decision.
Founders often assume that every improvement in AI model accuracy deserves a production release. But when testing, deployment, monitoring and engineering labor are factored in, deploying a slightly better model can actually produce a worse business outcome.
Imagine your AI team has trained a new model that performs 0.2% better than the version currently serving customers. Naturally, the data scientists are pleased and the automated pipeline marks the candidate as superior, leading everyone to assume it should immediately replace the existing model. But that is when the real production work begins.
The candidate must pass rigorous security and integration tests before engineers can package it, deploy it into a test environment and validate its behavior. Furthermore, the team might need to run a shadow or canary release, update monitoring rules, document the changes and prepare a comprehensive rollback plan. By the time this new model finally reaches production, the company has spent significantly more than the original training cost, yet customers may never even notice the improvement.
What Higher Interest Rates Mean for Caterpillar, GE Vernova, and Vertiv
The recent hike in interest rates, plus hawkish language from Federal Reserve Chair Kevin Warsh, led to an immediate sell-off in industrial stocks such as Caterpillar (CAT +1.30%), GE Vernova (GEV +1.67%), and Vertiv (VRT +3.27%). Now that the knee-jerk action is over, it’s time to look in more detail at the potential impact of further rate increases on these stocks.
Image source: Getty Images.
Caterpillar carries the most risk
The industrial company’s stock has surged this year as investors have priced in a return to profit growth in its construction industries and resource industries segments following the impact of tariff costs on both last year. The stock was also helped by ongoing strength in its power & energy segment, driven by booming demand for off-grid power coming from artificial intelligence (AI) data centers. As such, Caterpillar has become one of investors’ favorite “hidden” ways to play the AI infrastructure boom.

Today’s Change
(1.30%) $10.42
Current Price
$808.99
Key Data Points
Market Cap
Day’s Range
$799.57 – $809.49
52wk Range
$459.57 – $1073.46
Volume
2.7K
Avg Vol
3.1M
Gross Margin
32.83%
Dividend Yield
0.76%
These trends were confirmed by the company’s recent second-quarter earnings. However, I would argue that its construction industries, financial products, and to a lesser extent, its resource industries segments are negatively exposed to higher rates.
|
Segment Profit |
Six Months 2025 |
Six Months 2026 |
Change |
|---|---|---|---|
|
Power & Energy |
$2.84 billion |
$3.48 billion |
$635 million |
|
Construction Industries |
$2.27 billion |
$3.48 billion |
$1.21 billion |
|
Resource Industries |
$1.19 billion |
$1.07 billion |
($115 million) |
|
Financial Products |
$463 million |
$573 million |
$110 million |
Data source: Caterpillar presentations. Table by the author.
For example, higher rates tend to make large infrastructure and construction projects more expensive because they rely on financing. It’s a similar story with resource industries (mining and aggregates), where higher rates can negatively affect decision-making on expansion activity, let alone commodity pricing. Meanwhile, credit quality (financial products) can deteriorate if higher rates pressure borrowers or make it difficult to finance equipment purchases.
However, the power & energy segment is probably the least exposed, at least for now, because the AI infrastructure-building boom is part of a structural trend and is mainly financed from cash reserves of well-funded companies like Alphabet, Amazon, and Microsoft. Caterpillar CEO Joe Creed said on the last earnings call that “Power & Energy customers continue planning with us by sharing their long-term forecasts, and some are placing orders as far out as 2030.”
All told, it’s far too soon to panic over a 25-basis-point hike in interest rates; a sustained increase in rates, however, is likely to hurt Caterpillar’s construction segment in particular.
GE Vernova has mixed exposure
Building on the argument presented above, it appears unlikely that structural demand for the gas turbine and electrification equipment that’s driving revenue and backlog growth for GE Vernova will be significantly affected by anything other than a significant increase in interest rates.

Today’s Change
(1.67%) $15.40
Current Price
$940.33
Key Data Points
Market Cap
Day’s Range
$926.20 – $949.51
52wk Range
$530.16 – $1195.94
Volume
2.9K
Avg Vol
2.6M
Gross Margin
20.16%
Dividend Yield
0.19%
Moreover, the company’s backlog, or remaining performance obligation (RPO), is so strong that it can ride out temporary weakness driven by interest rate concerns. Here’s a look at GE Vernova’s RPO growth in recent years. To put the current figure of $176 billion in context, Wall Street expects the company to hit $46.2 billion in revenue in 2026.
Moreover, according to the company’s Securities and Exchange Commission filings, its RPO has a long duration. For example, management expects 97% of the equipment RPO to be recognized as revenue over five years, and 92% of services RPO over 15 years.
Data source: GE Vernova presentations. Chart by the author.
And CEO Scott Strazik, speaking at a recent Morgan Stanley conference, told investors: “We’ve talked about getting to a $200 billion backlog in 2027. I would say on the strength of the orders we expect to see in the third quarter, that $200 billion milestone we should hit very early in 2027.”
That said, GE Vernova still sells gas turbines and electrification equipment to meet traditional demand from interest-rate-sensitive power utilities, and higher rates would hurt its wind power segment, since investment decisions could be curtailed due to increased borrowing costs.
Vertiv has the least exposure
Building on the themes discussed above, and recognizing Vertiv’s exposure to the AI data center infrastructure spending, it seems unlikely that its segment for data center infrastructure equipment (cooling and power management technology) will suffer unless rates rise significantly.

Today’s Change
(3.27%) $7.90
Current Price
$249.39
Key Data Points
Market Cap
Day’s Range
$242.43 – $250.15
52wk Range
$133.85 – $379.94
Volume
20.8K
Avg Vol
6M
Gross Margin
35.73%
Dividend Yield
0.10%
As noted above, the larger hyperscalers, which account for the overwhelming bulk of spending, are largely funding AI investment from their own resources. And these are multiyear structural investments designed to generate a huge return on investment coming from long-cycle secular growth in AI adoption. It’s not the same as the kind of cyclical investment that gets curtailed when rates rise — as when, say, an airline cuts capacity as the economy slows.
AmEx Platinum 175k Offer No Lifetime Language (NLL) Link (2026.9 Update)
AmEx Summaries
There’s a new No Lifetime Language (NLL) link for AmEx Platinum with 175k welcome offer!
This link was originally posted on USCardForum in a hidden category by qazmlp and cowboy. Later on it was reposted everywhere, so now we also publicly post it here.
Enjoy!

If you like this post, don’t forget to give it a 5 star rating!
Disclaimer: The responses below are not provided or commissioned by the bank advertiser. Responses have not been reviewed, approved, or otherwise endorsed by the bank advertiser. It is not the bank advertiser’s responsibility to ensure all posts and/or questions are answered.
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Fitch Downgrades Xavier University to BBB+ With a Negative Outlook
Fitch Ratings downgraded Xavier University to ‘BBB+’ from ‘A-‘ last week, and assigned a Negative Outlook. The action covers the Cincinnati Jesuit university’s issuer rating and $275.6 million in Ohio Higher Educational Facility Commission revenue bonds.
Operating deficits sit at the top of the warning signs families should watch at any college, and that’s what drove this decision.
Fitch cited “significantly weaker-than-expected” preliminary fiscal 2026 results and a budgeted deficit for fiscal 2027. The agency cut its Operating Risk assessment to ‘bb’ from ‘bbb’ and its Financial Profile assessment to ‘bbb’ from ‘a’.
Xavier joins a growing list of private schools under budget strain, including the University of Denver, which cut five departments to close a $30 million gap.
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Why It Matters
A ‘BBB+’ rating is still investment grade on Fitch’s rating scale. Fitch did not say Xavier is at risk of closing, and the school does not belong on any list of colleges shutting down in 2026. The downgrade does signal that the university is spending more than it earns, and a lower rating raises the cost of any future borrowing.
For students and parents, persistent deficits tend to show up as program cuts, staff reductions, and tighter aid budgets. Fitch noted that Xavier has already sunset some programs while launching others in health and technology.
Families comparing offers should weigh that against the record 56% average tuition discount at private colleges, which shows how much schools like Xavier already give up in sticker-price revenue to fill seats.
The Numbers Behind the Downgrade
Fitch’s report lays out a school with real strengths and a weak undergraduate pipeline. The details below come from the rating report, and they mirror the pattern behind the record 10.8 million applications that still left 4 in 10 colleges with fewer applicants.
- Enrollment: Total enrollment fell to 4,358 full-time equivalent students from 4,580 a year earlier, a drop of about 4.8%.
- Freshman class: Fall 2026 matriculation rebounded nearly 33% to about 920 students after a sharp drop in fall 2025. The smaller 2025 cohort will weigh on total enrollment for three more years.
- Retention: 86%, which Fitch called solid.
- Cash flow: Fitch-calculated cash flow margins are projected to stay below 5%, even with expected revenue growth.
- Revenue mix: Fundraising, investment income, and other non-student sources made up more than 20% of unrestricted revenue, supported by a $500 million campaign and record fundraising.
- One-time hits: An unplanned switch in enterprise software and a consultant-led “operational transformation” completed in early 2026 added costs.
Xavier also replaced its enrollment management vendor and hired a new enrollment chief in April 2026. Fitch flagged “execution risk” from running that many initiatives at once. Students evaluating aid packages from schools in this position can review how private colleges award merit grants to understand where negotiating room exists.
The Medical School Bet
Xavier’s largest project is its new College of Osteopathic Medicine, which received pre-accreditation from the Commission on Osteopathic College Accreditation in June 2026. Fitch said the build is on time and on budget, with a first class of 90 students planned for fall 2027. Those students will enroll under the new $50,000 annual and $200,000 lifetime federal loan caps for professional programs, with no Grad PLUS loans to cover the rest.
Fitch’s base case assumes “significant tuition revenue growth starting in fiscal 2028” from the medical school. The university must also move $56 million of unrestricted cash into a temporary escrow required for the program. Failing to enroll that first cohort of 90 is one of four triggers Fitch listed for another downgrade, which ties Xavier’s credit directly to how the graduate loan limits affect college finances.
How This Connects
Xavier is a Division I school with a national brand, a solid donor base, and an 86% retention rate, and its rating still fell. The pressure on tuition-dependent private colleges now reaches well past the small rural campuses that fill our tracker of 9 closures and 6 mergers in 2026.
Universities are also absorbing layoffs and program cuts tied to falling international graduate enrollment, which removes another revenue cushion.
What’s Next
Fitch named the signals to watch: fall 2027 new student enrollment, cash flow margins reaching at least 5%, debt service coverage of at least 1x, and available funds staying above 100% of adjusted debt. A planned energy-as-a-service deal will bring in cash, but Fitch will count the related service agreement as a debt-equivalent obligation.
If Xavier balances its budget and stress conditions don’t materialize, Fitch said the Outlook could return to Stable. Families with a student headed to any private college can run the same check using these five financial red flags.
Editor: Colin Graves
The post Fitch Downgrades Xavier University to BBB+ With a Negative Outlook appeared first on The College Investor.
A Mission-Driven Coffee Chain Grew From 4 Employees to Hundreds. Now It Faces a Federal Labor Lawsuit
Not Your Average Joe is accused of labor violations. Its founder says growth outpaced the organization’s internal systems.
Jeff Taylor, mortgage tech OG, leaving Digital Risk
Jeffrey C. Taylor, long-time mortgage industry veteran who founded industry service provider Digital Risk, is stepping away from the company, a LinkedIn post said.
Processing Content
Digital Risk was created in October 2004 and
“After 22 extraordinary years, I have stepped away from Digital Risk,” Taylor’s LinkedIn post began. “I have often joked that Digital Risk was my firstborn. Saying goodbye to something I poured so much of my life into is far more emotional than I ever imagined.”
National Mortgage News reached out to the company for a comment.
He is currently a member of the MBA board and served as
His LinkedIn bio said he is a member of Sagent’s board.
From 1999 through 2004, Taylor was the founding partner of
“To my founding partners — and every teammate, client, partner, and friend who believed in us —thank you for helping turn an idea into something truly special,” Taylor wrote in the LinkedIn post.
He said teaming with Mphasis was the start of “a remarkable second chapter” for Digital Risk.
“To everyone at Mphasis, it has been a true privilege,” he said. “Together, we accomplished more than I ever thought possible.” The post indicated Taylor was stepping back from the business but did not specify his next steps. Although he did say it was now time to be fully present for his wife Jaclyn, whom he thanked, and their sons Landon and Preston.
“I leave with tremendous pride, a full heart, and a lifetime of memories,” the post concluded. “I look forward to seeing many of you on a golf course or youth baseball field somewhere.”
Dollar General CEO says consumers making $100,000 a year don’t feel high income anymore
A six-figure annual salary used to be a milestone for career success and financial security, but it isn’t what it used to be.
The era of elevated inflation that began during the COVID pandemic has eroded the value of $100,000 to the point that Americans earning that much are hunting for bargains like everyone else.
Walmart has previously noted that more affluent customers are shopping at the discount chain. And now they are patronizing deep discounters like dollar stores.
At the Goldman Sachs Global Consumer and Retail Conference on Tuesday, Dollar General CEO Todd Vasos said core customers, whom he defined as those making less than $45,000 a year, change their shopping behavior when gas prices hit $4 a gallon.
For example, they tend to buy closer to home, shop more often, and buy less on each trip. The frequency of shopping goes up because customers don’t know what the next week will hold and will buy what they can, when they can.
“But the interesting thing with this economy, because of the other sustained headwinds of inflation over the years that have passed, even that middle to upper middle is acting more like a lower-income shopper these days,” he said, according to a Seeking Alpha transcript.
The national average for a price of gasoline is now $4.476 a gallon, up from $3.189 a year ago, according to AAA, as President Donald Trump’s war on Iran disrupts global oil markets. The price of diesel has soared to $6.50, making items shipped by trucks more expensive.
But it’s not just energy costs. Utility bills, new and used cars, insurance, food, and caregiving costs have all jumped. Vasos said even those making $100,000 a year or more are feeling the squeeze.
“I would tell you, what we’re hearing more and more from them is ‘I don’t feel like I’m higher income at $100,000 any longer,’ because of all of the headwinds that I just mentioned,” he added. “So we believe at Dollar General, we’re in a really good position to service all of the different demographics of what we have.”
Still, consumers remain very resilient, and the biggest reason is that they have stayed employed, Vasos explained.
Indeed, the ability of consumers to adapt to higher prices has been a hallmark of the U.S. economy lately. The latest retail sales report showed a better-than-expected 1.2% increase in August and a 1.1% gain after excluding gasoline.
As long employment holds, Dollar General’s costumers will find a way to navigate the inflation landscape, he predicted.
“Having 2,000 items at or below $1 is very meaningful for the consumer, always has, but especially in this environment,” Vasos added.
Other signs have emerged that making $100,000 isn’t enough to avoid economic anxiety. A survey from the Harris Poll last year found that 64% of six-figure earners said their income isn’t a milestone for success but merely the bare minimum for staying afloat.
In fact, even those making $200,000 or more have resorted to financial tactics that are often associated with less wealthy consumers. For example, 64% said they’ve used rewards points to pay for essentials, 50% have used “buy now, pay later” plans for purchases under $100, and 46% rely on credit cards to make ends meet.
And Michael Green, chief strategist and portfolio manager for Simplify Asset Management, wrote a viral Substack post last year arguing the real poverty line should be $140,000.
Conventional gauges don’t capture how much Americans are struggling with the cost of living, even households earning six figures, he said.
“If the crisis threshold—the floor below which families cannot function—is honestly updated to current spending patterns, it lands at $140,000,” Green added.
Citi Strata Cards Add Lifetime Language for Welcome Bonuses
Citi Strata Cards Add Lifetime Language for Welcome Bonuses
Citi appears to have made an important change to the bonus rules on its Strata cards. The current application pages for the Citi Strata Elite, Citi Strata Premier, and Citi Strata now show lifetime language instead of the old 48-month rule that we are used to seeing on Citi ThankYou products.
That means the welcome bonus language is now much more restrictive. Rather than becoming eligible again after a set period of time, the terms now say that you are not eligible if you currently have or previously had that specific card.
Citi Strata Elite® Card
New account bonus offer is not available if you currently have or previously had a Citi Strata Elite® account. You also may not be eligible for the new account bonus offer based on a number of factors, such as your history of opening, closing and using credit cards.
Citi Strata Premier® Card
New account bonus offer is not available if you currently have or previously had a Citi Strata Premier® account. You also may not be eligible for the new account bonus offer based on a number of factors, such as your history of opening, closing and using credit cards.
Citi Strata℠ Card
New account bonus offer is not available if you currently have or previously had a Citi Strata® or a Citi Strata Student account. You also may not be eligible for the new account bonus offer based on a number of factors, such as your history of opening, closing and using credit cards.
Why This Matters
This is a pretty significant change. Under the old setup, many applicants mainly had to worry about Citi’s 48-month rule, which at least left the door open to earning another bonus down the road. With this updated wording, it now looks like these bonuses are once per product, for life.
That doesn’t necessarily mean you can’t earn a bonus on more than one Strata-branded card. Based on the current wording, the restriction is tied to the specific card, not the entire Strata family.
We also do not know how strictly Citi will enforce this new language. We know that Amex for example considers lifetime to be 5-7 years, but they will even approve you for some cards (co-branded business usually) much sooner than that in some cases. We’ll have to wait and see what Citi does going forward.
HT: DoC
