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Could Investing $300 a Month in VOO Make You a Millionaire? Here’s the Math.


One of the most frequently recommended ways for people to invest in the stock market is to buy the S&P 500 index. With a low-cost S&P 500 index fund, you can own all 500 of the largest publicly traded companies in America. Just buying these 500 major names could be enough to make you a millionaire. That’s because the S&P 500 has delivered strong long-term returns.

In the 98 years since 1928, the S&P 500 has delivered annualized returns of about 10%. Keep in mind, that number includes some massive economic downturns during worldwide catastrophes like the Great Depression and World War II. Even with the dot-com bubble of 1999-2000, the global financial crisis of 2008, the pandemic of 2020, and other serious crises and short-term sell-offs, the S&P 500 has been one of the best places for people to put their money for almost 100 years.

One of the best ways to buy the S&P 500 is via a popular Vanguard exchange-traded fund (ETF). The Vanguard S&P 500 ETF (VOO +0.58%) is so well-known that it’s often referred to by its ticker (VOO). This fund has a shorthand investment strategy named after it called “VOO and chill.”

Let’s look at why “VOO and chill” could be a simple strategy to make you a millionaire with long-term investing.

Image source: Getty Images.

Vanguard S&P 500 ETF (VOO): 14.94% annualized returns since September 2010

The Vanguard S&P 500 ETF holds a total of 505 stocks and tracks the performance of the S&P 500 index. Just like the broad index it tracks, this ETF has delivered stellar returns in recent years. In the past (nearly) 16 years since VOO was established in September 2010, this S&P 500 ETF has delivered average annual returns of 14.94%. In the past five years, it’s delivered 12.82% annualized returns (by net asset value).

Both of those average returns are higher than the long-term stock market average return of 10% per year. This strong performance might not continue. The stock market could go into a bear market or fail to deliver such high growth in the future.

But let’s look at how VOO could make you a millionaire with a few different possible rates of return, based on its real-life past performance.

Vanguard S&P 500 ETF Stock Quote

Today’s Change

(0.58%) $4.05

Current Price

$705.06

How VOO can make you a millionaire

Let’s say you can invest $300 per month, and you keep using that same $300 amount to keep buying shares of the Vanguard S&P 500 ETF (VOO) month after month. Let’s also assume that the fund delivers the same 98-year long-term average annual return of 10% per year.

At that 10% annualized rate of return, your money would grow to $57,375 after 10 years. After 20 years, you’d have $206,190. After 30 years, you’d have $592,178, and after 36 years, you’d have more than $1 million.

What if VOO could perform even better than that? Let’s assume that VOO can keep delivering the same return it did in the past five years: 12.82%. $300 per month invested at that rate of return would grow to $65,735 after 10 years. After 20 years, you’d have $285,346. After 30 years, you’d have more than $1 million.

What if VOO keeps up the same strong performance of the past 15 (almost 16) years? Let’s assume that the fund delivers the same 14.94% average annual return that it’s delivered for the past 15 (almost 16) years.

At that rate of return, $300 invested per month would grow to $72,880 after 10 years. In 20 years, you’d have $366,183, and after 27 years, you’d pass the $1 million mark.

Why invest in VOO?

There is no guarantee that any stock ETF or investment will deliver 10% or higher annual returns forever. But these are real numbers based on historic returns. The ultra-low 0.03% expense ratio, broad diversification, and simplicity show why the Vanguard S&P 500 ETF ranks among the best low-cost index funds.

Boston Beer stock falls as CFO Reynoso to depart




Boston Beer stock falls as CFO Reynoso to depart

8,000 Professor Accounts Hacked: What Every .edu Email Holder Should Do


The Justice Department unsealed a 14-count indictment on August 18 charging 17 Iranian nationals with running a decade-long hacking campaign against 144 U.S. universities, 178 foreign universities, at least 42 U.S. companies, five federal and state agencies, and two non-governmental organizations.

Prosecutors say the defendants (leaders, contractors, and hackers-for-hire tied to the Tehran-based Mabna Institute) stole at least 31.5 terabytes of academic data and intellectual property, much of it at the direction of Iran’s Islamic Revolutionary Guard Corps. The targets were research operations at institutions already cutting back, including schools like MIT.

Nine of the 17 defendants were charged in the original indictment announced in March 2018 while eight are new. The State Department’s Rewards for Justice program is offering up to $10 million for information leading to the location of five defendants — an indicator that none are in U.S. custody.

Why It Matters

The conspiracy targeted more than 100,000 professor accounts worldwide, roughly half of them at U.S. schools, and successfully compromised about 8,000, including 3,768 belonging to U.S. professors. One reused password gave outsiders the same library access a tenured faculty member has, which is the same basic failure behind most student loan and financial aid scams that hit borrowers.

The number that should catch a reader’s eye is $3.4 billion. That’s what U.S. universities spent to license and access the academic material prosecutors say was stolen — journals, dissertations, e-books, and database subscriptions. Library and research licensing is a fixed line item that never shrinks, and it feeds directly into why college costs keep climbing even at schools with flat enrollment.

The Details

  • Spearphishing. According to the indictment, conspirators researched professors’ published work, then emailed them posing as faculty at another university, referencing a recent article and linking to “related” papers. The links led to a look-alike domain with a fake university login page that captured credentials.
  • Password spraying at companies and agencies. For corporate and government targets, the group collected employee email addresses from public sources and tried commonly used and default passwords across them, then exfiltrated entire mailboxes and set up automatic forwarding rules to keep receiving mail.
  • The stolen data was resold. Megapaper sold pilfered academic resources to Iranian universities and institutions. Gigapaper sold customers direct access to compromised professor accounts so they could use U.S. and foreign university library systems themselves.
  • Named victims. The Department of Labor, the Federal Energy Regulatory Commission, the State of Hawaii, the State of Indiana, the Indiana Department of Education, the United Nations, and UNICEF. Private-sector victims included three academic publishers, two defense contractors, and HBO.
  • The HBO connection. Behzad Mesri was charged separately in 2017 with hacking HBO and demanding roughly $6 million in bitcoin. Five additional defendants are now alleged to have taken part in that intrusion.
  • Remediation costs. Private and government victims spent more than $20 million investigating and cleaning up the intrusions.

What This Means For Your Own Accounts

Nothing in the alleged playbook required advanced skill, only a convincing email, a domain that looked almost right, and passwords people reuse. Anyone with a .edu account should assume they are a target, since institutional library credentials are worth real money on a resale market.

Use a unique password everywhere, turn on multi-factor authentication or passkeys, and check your email for forwarding rules you didn’t create, which is the step almost nobody takes.

If credentials tied to your identity are exposed, the follow-on risk is financial rather than academic. Knowing how to freeze your credit and what to do if someone takes out loans in your name matters more than the specific breach that caused it.

How This Connects

Research access is one of the least visible expenses in higher education, and its a driver of the same cost increases that families are already facing. Sallie Mae reported families spent $34,019 on college last year, up 10%. Graduate students and postdocs feel it more directly, since library and database access is part of what makes a funded assistantship or fellowship usable at all.

What’s Next

The defendants are believed to be in Iran, which has no extradition treaty with the United States, so the practical outcome is likely travel restriction and sanctions exposure rather than a trial.

Watch for two things: whether the Rewards for Justice offer produces a location on any of the five named individuals, and whether universities respond with hard multi-factor authentication mandates for faculty accounts, a cost that, like everything else in higher education, eventually shows up in the price students pay.

Editor: Colin Graves

The post 8,000 Professor Accounts Hacked: What Every .edu Email Holder Should Do appeared first on The College Investor.

ADU Income Now Allowed On Purchases And Refinances


Accessory Dwelling Units (ADUs) have become one of the most sought-after features of property. Whether it’s a detached guest house, a converted garage, a basement apartment, or another legally permitted living space, ADUs allow homeowners to generate rental income while increasing the overall value. Now, borrowers have even more reason to consider properties with ADUs. Updated mortgage guidelines allow rental income from up to two Accessory Dwelling Units to be used to help qualify for eligible purchase and refinance transactions, including certain DSCR loan programs.

More Qualifying Income

For many borrowers, qualifying for a mortgage can come down to income. Including projected or existing ADU rental income can significantly improve a borrower’s ability to qualify, increase purchasing power, or meet refinance requirements. If you’re purchasing a property with income-producing potential or refinancing an existing property with ADUs already in place, these updated guidelines create new opportunities for homeowners and real estate investors alike.

Eligible Property Types

  • Single-Family Residences
  • Two-Unit Properties
  • Three-Unit Properties
  • One or Two ADUs Allowed
  • Total Property Unit Count Cannot Exceed Four Units

Documentation Requirements

The documentation process remains straightforward.

Purchase Transactions

For purchase loans, borrowers simply provide a signed letter of intent stating their intention to rent the ADU or ADUs.

Refinance Transactions

For refinance loans, borrowers provide:

  • Current lease agreement
  • One month of rent receipts

ADU Requirements

To utilize ADU income for qualification purposes, the following requirements apply:

  • Each ADU must be legally permitted
  • Each ADU must contain at least 500 square feet
  • The appraiser’s market rent survey must support market rent

ADU Income and DSCR Loans

Real estate investors may also benefit from these expanded guidelines. Eligible DSCR transactions can utilize rental income generated from ADUs, creating additional opportunities for investors seeking to maximize cash flow and financing options.

Contact us to learn how rental income from an ADU could help you qualify for your next mortgage.

Amazon: Reorder 5 Items & Get 10% Off


The Offer

Direct Link to offer (affiliate link)

  • Amazon is offering 10% discount when you reorder 5 items which you’ve purchased in the past.

Note: not all reorder items will be found on the page. It seems to be for any item which has a Subscribe&Save option – you don’t need to S&S for this offer, but the offer is available for those types of items. 

Our Verdict

10% is decent. What’s especially nice about the promo is how practically usable it is since on items that you’re already interested in and are likely to need again. 

I checked on a few accounts and the promo seems to be available across the board, though perhaps not for everyone. 

The AI ‘death zone’ is here and most corporate AI strategies are standing in it



In July, for the first time, Chinese developed models took all five top positions on OpenRouter, the neutral routing platform that has become the closest thing the AI industry has to a Nielsen rating. Xiaomi’s MiMo V2.5 ranked first by token volume, followed by models from DeepSeek, MiniMax, Alibaba’s Qwen family and Moonshot’s Kimi. Chinese models now carry more than 60% of the platform’s traffic, which exceeds 20 trillion tokens a week.

That is not a benchmark result but a usage curve.

A year ago, US models carried roughly 70% of OpenRouter’s traffic. Today they carry about 30%. Even more striking is that by mid-July, Chinese models accounted for a record 58% of tokens processed by American firms on the platform. US companies are not being forced into Chinese AI. They are choosing it, workload by workload, because the price/performance math is impossible to ignore.

The race split in two

Here is the paradox that should be on every board agenda this fall. American labs still hold the absolute frontier. GPT 5.5, Claude Fable 5, and Gemini 3.x lead on the hardest reasoning, long-horizon agents, and the most demanding enterprise work. The frontier gap is real and measured in months.

But the race split into two contests: capability and distribution. America is winning the first and losing the second. DeepSeek’s V4-Pro is priced at roughly one-twelfth the cost of GPT-5.5 at comparable benchmark performance. DeepSeek V4 Flash costs $0.14 per million input tokens, compared with $5.00 for GPT-5.5. OpenRouter’s own analysts report that Chinese open models run 60% to 90% cheaper than the leading American offerings. For high-volume production workloads, coding agents, document processing and customer operations that differential decides the purchase order.

Distribution is where ecosystems lock in. Alibaba’s Qwen family has passed one billion cumulative downloads and replaced Meta’s Llama as the most downloaded open model family in the world. Llama, which defined open weight AI in 2023 and 2024 has fallen below 1% of routed volume. Developers optimize what they can download. They build tooling around what they deploy. This is how Linux won servers and Android won phones, and it is happening again in plain sight.

Welcome to the death zone

Between the frontier and the commodity floor sits a death zone: any model, product or corporate AI strategy that is neither clearly the best nor clearly the cheapest. It is being crushed from both directions at once.

The market data shows exactly how this bifurcation works. According to analysis of OpenRouter’s usage data, Anthropic holds only about 12% of the platform’s token share yet captures roughly half of total spending. That is the premium lane with fewer tokens, priced for the work that justifies them. The commodity lane belongs to efficient open models moving trillions of cheap tokens. The middle, closed models without a decisive capability edge and enterprise deployments paying frontier prices for commodity work has no lane at all.

Most Fortune 500 AI strategic plans are standing in that middle right now. The typical enterprise signed one frontier API contract in 2024  routed everything through it, and never looked back. In 2026, that is the equivalent of running your entire logistics operation by overnight air freight.

China built this on purpose

None of this happened by accident. Export controls denied Chinese labs the largest GPU clusters, so they engineered around scarcity with token efficiency, novel attention mechanisms, efficient mixture of expert designs, higher quality data over raw volume and inference-aware architecture from day one. State support lowered the effective cost base further. Xiaomi cut MiMo API prices by as much as 99% in May.

Constraint now became strategy. American labs that prioritize efficiency as a secondary concern risk maintaining their technological edge while losing market volume, developer interest, and ultimately the whole AI ecosystem.

The builder’s playbook for 2026

For the executives and founders actually building on AI, four moves matter now more than anything else.

1. Make hybrid routing your default architecture.

Route the hardest, most regulated, highest stakes work to frontier models. Route high volume, cost sensitive tasks to efficient open models. Companies doing this are cutting inference costs 60% to 90% on the majority of their workloads without touching quality where it counts. If your AI budget runs through a single closed API, you are overpaying for most of what you do.

2. Treat efficiency as a first-class weapon.

Inference optimization, quantization, speculative decoding, and model hardware co-design are now standard practices rather than mere research curiosities. Study how the constrained labs built, and then apply those lessons with American compute behind them.

3. Differentiate above the model layer.

Proprietary data, application layer, domain fine tuning, agent frameworks and rigorous evaluation harnesses outlast any base model advantage. Base models are converging into infrastructure. Your moat was never going to be someone else’s model.

4. Get out of the middle.

If your product depends on a model that is neither the best nor the cheapest then pick a direction this year. Move up the capability curve with real differentiation, or compete hard on cost and openness. The middle does not survive 2027.

America needs an open weight answer now

My point of view is that Washington is preparing to fight the wrong battle. The instinct in Congress is to restrict Chinese models on security grounds, and for sensitive government and defense workloads, that caution is warranted. Data sovereignty concerns already limit Chinese hosted adoption across Western regulated sectors, though self-hosted open weights blunt much of that argument.

A ban is not a strategy, it’s a tariff on your own developers. Chinese open weights succeed not due to deception, but because they are high-quality, affordable, accessible, and no American lab currently releases frontier-class open-weight models on a regular schedule. Meta’s retreat left the field open and China took over quickly.

The answer is to compete with credible US and allied open weight models, released regularly and backed by procurement incentives or direct lab commitments. Open weights are how you export your ecosystem, your safety norms and your standards to the rest of the world. America understood this with the internet stack. America needs to remember it now.

The frontier still matters and the US should defend it. But the practical race in 2026 is won by mastering both contests at once with absolute capability and radical efficiency, closed excellence and open diffusion, the biggest reliable compute and the smartest use of it. Innovation under constraint should no longer be a consolation prize.

The question for the American C-suite, boardrooms, and Washington is the same one. When the next generation of global software is built, whose models will it be built on? Right now, the download numbers are answering. It is not the one America wants to hear.

The opinions expressed in Fortune.com commentary pieces are solely the views of their authors and do not necessarily reflect the opinions and beliefs of Fortune.

This story was originally featured on Fortune.com

Spotify expands its share buyback program by $1.5B, raising total authorization to around $2.2B


Spotify has increased the size of its share repurchase program by an additional USD $1.5 billion.

The company’s Board of Directors approved the increase, which Spotify confirmed in a press release on Thursday (August 20).

With $723 million left under the existing program, the increase raises Spotify‘s total authorization to approximately $2.223 billion.

The program “will run for as long as the shareholders’ authorization to the Board of Directors to repurchase ordinary shares remains in force (including by renewal),” Spotify said.

Spotify said the timing and number of shares it buys back would depend on factors including “the renewal of repurchase authorization by shareholders, price, general business and market conditions, and alternative investment opportunities.”

Repurchases can be made “from time to time using a variety of methods, including open market purchases,” in line with US Securities and Exchange Commission rules, the company said.

The program does not commit Spotify to buying any set number of shares and “may be suspended or discontinued at any time at the Company’s discretion.”

Spotify first launched its buyback program in 2021, when its board approved repurchases of up to $1.0 billion of ordinary shares, following approval from shareholders at a general meeting.

The company added a further $1.0 billion to that authorization in July 2025.

Spotify isn’t the only large-scale music industry player to be buying back its shares.

Universal Music Group launched its first-ever share buyback program, worth €500 million ($575m), in March, and doubled that authorization to €1 billion the following month.

It used €250 million of this expanded authorization in June to buy back shares directly from Bill Ackman‘s Pershing Square, as the fund exited the company after its $64 billion takeover bid was rejected.

UMG completed the original €500 million program in July, having spent €499.2 million buying back its own stock.

Then, in August, it kicked off an additional €250 million ($288m) tranche of the program.

UMG confirmed in April that it would sell half of its Spotify stake, a move expected to generate around $1.4 billion, to help fund its own share buyback program.

According to UMG‘s 2025 annual report, the company held 6,487,000 Spotify shares at the end of that year, equivalent to a 3.10% stake.

Spotify grew its Premium subscriber base by 7 million to 300 million paying users in Q2 2026, and now counts 777 million Monthly Active Users across 184 markets.

Spotify generated total revenue of EUR €4.777 billion ($5.56bn) in the quarter up 14% year-over-year, and posted quarterly operating income of €655 million ($762m).

The firm’s Premium monthly average revenue per user stood at €4.89 ($5.69), up 7.4% year-over-year at constant currency.

Spotify ended Q2 with €9.4 billion in cash, restricted cash, and short-term investments.Music Business Worldwide

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Question Reveals if Quant Can Explain Trade


The architecture is moving toward opacity. The shift to large foundation models, reinforcement learning policies, and agentic systems in investment management has outpaced the vocabulary allocators use to evaluate managers. As models grow more capable, the link between input and decision grows more obscure, and the temptation to accept a confident narrative in place of a genuine explanation grows with it.

Post-hoc explanation tools have created a false sense of resolution. SHAP values, attention maps, and saliency methods produce outputs that look like explanations and are increasingly offered as such. Rudin’s warning applies directly: an explanation that is not faithful to the model is worse than no explanation, because it manufactures confidence the evidence does not support.

Allocators then need to dissect attribution from explanation. The question is not whether every sophisticated model must be simple, but whether the manager can provide a defensible account of how its decisions relate to the economic reasoning behind the strategy.

The most rigorous institutions already treat explanation as a standard rather than a courtesy. ADIA Lab’s investment in causal inference, including a $100,000 research award and a global challenge that drew nearly 2,000 researchers, reflects a view that understanding why a model decides is now part of the work.

CFA Institute’s Standard V(A) requires members to have a reasonable and adequate basis for investment recommendations, including an understanding of the assumptions and limitations of quantitative models. The ability to reconstruct and justify individual decisions can provide allocators with another way to assess that understanding.