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The initiative builds on a broader shift by companies exploring stablecoins to move money between international operations more efficiently.

If you’re starting from scratch, saving for a down payment on your first home could take half your life, depending on where you’re buying.
In two major U.S. metro areas, the typical first-time homebuyer would need to save for at least 50 years, according to a new report by real estate lender Rocket Mortgage. Those cities are New York and San Francisco, where it requires an estimated 65 years and 57 years of saving to afford the standard down payment, respectively.

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“Saving for a down payment takes years of discipline, which is why receiving the keys is such a meaningful milestone,” Bill Banfield, Rocket’s chief business officer, said in the report.
“For anyone hoping to own a home someday, it’s never too early to understand what buyers are putting down in your market and start building a plan.”
In 13 cities, first-time buyers would need to save for at least 20 years, the analysis found. But in several popular metros in the South and Midwest — including Detroit and Fort Worth, Texas — buyers could reach a down payment in five years or less.
Rocket’s analysis looked at nearly 50 major metros across the U.S. It calculated the required years of savings based on local home prices and down payment trends for first-time buyers and assumed they were saving 5% of their annual income.

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Saving for a down payment, by city
Down payments are consistently a major barrier for homebuyers, according to the Harvard University Joint Center for Housing Studies. For first-time buyers, down payments can be a downright nightmare. That’s because these buyers are typically younger, have less money saved and can’t tap equity from the sale of an existing home to help fund the purchase.
The prospect of saving for decades to afford a down payment can be demoralizing. But there are ways to sidestep that roadblock. And they largely boil down to location.

How Long It Takes First-Time Buyers to Save a Down Payment, by Metro

Metro

Years to Save for a Down Payment

Median Down Payment for First-Time Buyers ($)

Median Down Payment (%)

Median Home Price for First-Time Buyers

New York, NY
65.2
$265,000
30%
$883,333

San Francisco, CA
57.2
$400,000
26.60%
$1,501,466

Los Angeles, CA
41.5
$170,500
20%
$852,500

Boston, MA
37.8
$185,000
22.60%
$819,573

Anaheim, CA
33.6
$170,000
20%
$850,000

San Jose, CA
33.6
$249,000
22%
$1,113,595

San Diego, CA
25.8
$143,500
20%
$717,500

Oakland, CA
25.4
$130,000
20%
$650,000

Washington, D.C.
23.6
$129,500
20%
$647,500

Austin, TX
21.2
$95,700
20%
$478,500

Seattle, WA
21.2
$125,700
20%
$628,500

Nassau County, NY
21
$150,000
20%
$750,000

Providence, RI
20.6
$63,750
15%
$425,000

Nashville, TN
15
$60,200
15.80%
$379,954

Portland, OR
13
$59,670
12.80%
$467,928

Miami, FL
12.9
$42,745
7.70%
$556,155

Denver, CO
12.8
$59,000
12.70%
$466,100

New Brunswick, NJ
10.2
$58,500
15%
$390,000

Montgomery County, PA
10
$56,000
18.60%
$301,399

Sacramento, CA
9.9
$45,407
10%
$454,065

Charlotte, NC
9.3
$40,000
10%
$400,000

Philadelphia, PA
9.1
$27,275
10%
$274,750

Chicago, IL
8.7
$35,125
10%
$351,250

Newark, NJ
8.6
$25,102
6.20%
$404,574

Tampa, FL
8.5
$35,625
10%
$356,250

Dallas, TX
8.4
$31,225
9.40%
$333,002

Cincinnati, OH
8.3
$23,500
8.10%
$289,917

Riverside, CA
8.1
$36,600
5.40%
$681,232

Saint Louis, MO
8.1
$21,675
9.90%
$218,189

Orlando, FL
7.9
$30,750
8.80%
$350,813

Minneapolis, MN
7.1
$27,500
7.90%
$347,806

Atlanta, GA
6.8
$29,970
9.60%
$310,817

Phoenix, AZ
6.6
$28,000
6.20%
$449,198

Houston, TX
6.4
$20,512
7.30%
$282,105

Baltimore, MD
6.2
$20,000
7.70%
$260,917

Kansas City, MO
6.2
$21,750
7.30%
$298,399

Pittsburgh, PA
6.2
$20,715
9.10%
$226,817

Las Vegas, NV
6.1
$23,886
5.30%
$447,471

San Antonio, TX
5.6
$18,628
7.20%
$258,948

West Palm Beach, FL
5.3
$19,569
5.70%
$344,521

Cleveland, OH
5.1
$11,148
5.30%
$209,758

Columbus, OH
5.1
$17,001
6.50%
$262,121

Jacksonville, FL
4.7
$17,121
6.20%
$277,717

Indianapolis, IN
4.4
$14,600
5.60%
$261,273

Milwaukee, WI
4.4
$12,375
5%
$247,500

Fort Worth, TX
4.3
$17,867
5.80%
$306,589

Virginia Beach, VA
4.3
$20,450
6.30%
$323,993

Detroit, MI
3.9
$7,600
5%
$152,000

Warren, MI
3.1
$8,797
5%
$175,940

Adam Hardy for Money.com; Rocket Morgage

Nationally, the median down payment is now $64,000, according to Redfin. As a percentage, that translates to 15% of the median sale price. But that picture can vary drastically from city to city.
In Rocket’s analysis, the time it takes to save for a down payment is not just a reflection of home prices of the area. The estimate also accounts for local incomes and the typical down payment percentage in each market.
In New York, for instance, not only are home prices high, but the typical down payment is a whopping 30%. Rocket noted that the percentage is so high because condo associations in the city tend to have strict rules that require between 20% and 30% down payments.
Large down payments can also be a reflection of the competitiveness of the housing market. Increasingly, first-time buyers have to compete with all-cash buyers, which can result in higher down payments across the board.
A massive down payment isn’t always necessary, however. In fact, single-digit-percentage down payments are customary in about two dozen major cities, including Miami, Atlanta and Pittsburgh.
Down payment assistance programs are another key method to help first-time buyers afford a home. The Urban Institute identified more than 1,600 such programs across the country. Some programs provide publicly funded grant money with no strings attached, while others extend interest-free loans that are essentially second mortgages.
One notable option available nationwide is through the Neighborhood Assistance Corporation of America, a nonprofit housing group that runs a homebuying program boasting zero down payment and a discounted mortgage rate.

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From Walmart to the Nobel Prize, Federal Reserve Chairman Kevin Warsh went long and deep in his quest to kick off massive reforms of the U.S. central bank’s operations — changes he and others, including Treasury Secretary Scott Bessent, have argued are long overdue.Fifteen outside experts including former central bankers, academics, business leaders and even a college pal of Warsh who made it big in tech venture capital are co-leading five Fed task forces. The focus: improvements and upgrades to support the Fed’s dual mandate of labor and inflation plus its balance sheet, communications and data with a spotlight on the role of artificial intelligence.“Each task force will carefully consider whether policymakers’ means and methods, analytical tools and policy approaches can be improved upon. I am honored that the best minds from a range of disciplines have agreed to work with us to sharpen our performance as an institution,’’ Warsh said in a July 9 statement.He announced the formation of the task forces following the July 17 Federal Open Market Committee meeting, his first as chairman of the world’s largest central bank. He then set about personally recruiting the co-leads, who are not being compensated for their efforts. They will work with Fed staff to deliver recommendations by the end of the year to the FOMC.“The goal is straightforward: to ensure the Fed is best positioned to achieve our objectives in this consequential time,’’ Warsh said.Will AI lead the Fed to lower interest rates?Warsh, who served as a Fed governor from 2006 to 2011, made AI a key speaking point of his campaign to take over as chair of the central bank.His arguments have been that use of AI will lead to massive improvements in the productivity of people and businesses that will allow for lower interest rates without higher inflation.“If we do our jobs, we’ll be here a year from now and we’ll say we’ve discovered data that helps us make better decisions,” Warsh said at a conference I covered on July 1.  The Fed’s dual mandate from Congress requires maximum employment and stable prices.Lower interest rates support hiring but can fuel inflation. This risks fueling further inflation, potentially leading to an inflationary spiral.Higher rates cool prices but can weaken the job market. This increases the cost of borrowing and further stifles economic activity.

Federal Reserve Chairman Kevin Warsh, left, is congratulated by President Donald Trump at his June swearing-in ceremony. Warsh named 15 global experts on July 9 to lead five task forces studying improvements and upgrades across the entire central bank. (Getty Images)Aaron Schwartz / Getty Images

Fed Communications Task Force Will review how the Fed conveyspolicy deliberations and decisions amid uncertainty.Peter R. Fisher, professor of practice, Foster School of Business, University of Washington. He is a former Treasury and New York Fed official. Arminio Fraga, founder and chairman of Gávea Investimentos and former president of the Central Bank of Brazil.Mervyn King, former governor, Bank of England. King ran the BOE for over a decade and led the U.K. central bank through the 2008-09 financial crisis.Fed Balance Sheet Policy Task ForceWill examine the costs, benefits, and institutional implications of the Fed’s $6.7 trillion balance sheet.Karen Dynan, Harvard University economics professor who held top roles at the Treasury Department during the Obama administration. Raghuram Rajan, University of Chicago finance professor. He is a former governor of the Reserve Bank of India and is known for his early warnings ahead of the global financial crisis.Jeremy Stein, Harvard University economics professor and a former Fed governor.Fed Data Task ForceWill examine how to improve the quality and timeliness of real economic signals that inform the Fed’s policy judgments.Raj Chetty, Harvard University economics professor and a pioneer in the use of alternate and real-time data to analyze households and neighborhoods economic behavior. Doug McMillon, former Walmart Inc. president and CEO.Kevin Murphy, University of Chicago economics professor.Fed Productivity and Jobs Task ForceWill assess the economic impact of new general-purpose technologies, including artificial intelligence, to inform the Fed’s policy judgments.Famed tech investor Marc Andreessen, cofounder and general partner at Andreessen Horowitz. Andreessen is an outspoken supporter of the Trump 2.0 administration as well as a close friend of Warsh from their Stanford undergraduate days.Related: Fed Warsh era kicks off with big surprise no one saw comingCharles I. Jones, Stanford University economics professor currently on leave from Anthropic, the AI research and product company founded by former OpenAI executives.Asha Sharma, executive vice president and XBOX CEO at Microsoft Corp.Fed Inflation Frameworks Task ForceWill revisit how the Fed understands and responds to the drivers of inflation.Greg Mankiw, Harvard University economics professor and former chair of the Council of Economic Advisers in the George W. Bush administration.William White, senior fellow, C.D. Howe Institute. He is a former economic adviser to the Bank for International Settlements.Thomas Sargent, New York University economics professor. He shared the Nobel Prize in Economics in 2011 for his work in macroeconomics and government policy.Fed task forces to face multiple challenges RSM US Chief Economist Joe Brusuelas said the list of global economic and business experts will add experience to the task forces, though he cautioned they will face challenges, too. “It’s a very impressive list that’s been put forward by Chair Warsh that I’m confident will inform the discussion around the substantive topics,” Brusuelas told Bloomberg. “However, the Fed already has an army of Ph.D.s that had investigated these areas, so I’m not exactly convinced that this is going to shed much light into how we understand productivity and AI,” Brusuelas said. Related: Warsh’s AI task force could reshape Fed economic models

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Ask two chatbots the same personal finance question using identical financial details, and you may get dramatically different advice. One might recommend an emergency fund that is nearly twice as large as what the other advises.A study published in the Journal of Financial Planning in June 2026 found that recommendations varied significantly across AI platforms and, in several cases, also by the hypothetical user’s race or gender. Researchers tested seven widely used generative AI platforms on identical personal finance prompts and found significant inconsistencies, demographic bias in certain recommendations, and a gap most users never consider.Two out of three Americans who have used generative AI said they have tapped it for financial guidance, with that share climbing to 82% among both Millennials and Gen Z, CNBC reported.Study exposes wide gaps in AI financial recommendationsResearchers Gianni Nicolini of the University of Rome Tor Vergata; Brenda Cude, a professor emerita at the University of Georgia (UGA); and Swarn Chatterjee, Bluerock professor of financial planning at UGA, tested free versions of ChatGPT, Claude, Copilot, DeepSeek, Gemini, Meta AI, and Perplexity. All prompts were submitted in August 2025, covering emergency savings, retirement withdrawal rates, and investment portfolio allocation.Emergency savings recommendations ranged from $19,500 to $37,500 across platforms, a spread the researchers found to be statistically significant. Portfolio allocation guidance also diverged widely in equity, cash, and alternative asset categories, the study confirmed.The platforms generally aligned with broad principles such as the conventional 4% retirement withdrawal guideline, the authors noted.Yet the scale of variation in savings and allocation recommendations raised direct concerns about the reliability of chatbot-generated money advice.Demographic bias surfaces in chatbot financial guidanceThe researchers then resubmitted the same prompts with only the race and gender of the hypothetical individual changed. ChatGPT, Copilot, and DeepSeek all recommended higher emergency savings for women and African American users than for their white male counterparts, the study found.More AI:Goldman Sachs has blunt message for AI stock investorsMicrosoft CEO sends a blunt warning on AI and the tech ecosystemThe next AI infrastructure race has nothing to do with chips”If I’m a consumer, the recommendation I receive can vary simply based on which AI platform I’m using,” Chatterjee said in a University of Georgia press release.Chatterjee explained that AI models combine vast datasets on human behavior and finance to make assumptions about individual users. Those assumptions, such as expecting minority male users to face longer job searches, can lead the tools to recommend larger emergency funds.The tools may prescribe larger financial cushions for certain demographic groups without explaining the reasoning behind those adjustments.

Researchers found that AI financial advice can change based on a user’s race and gender, raising concerns about bias and transparency.d3sign/Getty Images

AI chatbots have no fiduciary obligation to usersA licensed financial advisor operating under a fiduciary standard faces legal consequences for putting personal interests ahead of a client’s needs, including regulatory penalties, civil liability, and criminal charges.”What they don’t have is that fiduciary duty,” Andrew Lo, director of the Laboratory for Financial Engineering at the MIT Sloan School of Management, told CNBC. “They don’t have the ability to suffer consequences if they make a mistake to the same degree that a human advisor does.”Lo also warned that large language models consistently generate responses that sound authoritative, regardless of whether the underlying information is accurate. That risk is sharper when users press chatbots for personalized answers on topics such as taxes, retirement timing, or portfolio allocations, where accuracy depends on details the model may lack.The Consumer Financial Protection Bureau’s 2023 report found that chatbots deployed by financial institutions for customer service sometimes provide inaccurate information. The CFPB warned that such errors can violate federal consumer financial protection laws. Data privacy adds another layer of risk for AI money adviceAsking a chatbot for money guidance often requires sharing sensitive financial details, including income, account balances, and spending patterns. That data can be stored indefinitely by the company providing the AI, and human reviewers may access those conversations, Empower noted in its analysis of chatbot privacy risks.Certified financial planner Brenton Harrison told CNBC that relying on AI for financial guidance creates an inherent tension.Looking to [AI] for advice implies you are giving it enough information to form an opinion and make a recommendation, and that’s a step further than I’d go with AI.Research from cybersecurity firm Harmonic Security found that 4.37% of workplace AI prompts and 22% of file uploads to GenAI tools in the second quarter of 2025 contained sensitive company information.Empower has advised users to leave out personal identifiers such as Social Security numbers, exact income, and account numbers when sharing information with AI chatbots and to frame questions in general terms.What experts say about relying on AI outputs for money decisions”Trust but verify,” Chatterjee said in the University of Georgia release. “AI gives people a starting point, not an ending point. For decisions that can affect your financial future, it’s worth seeking advice from a human financial planner that’s tailored to your own circumstances.”A “Return on Advice” survey of 2,202 Americans in October 2025 found 76% say technology can provide financial information but not judgment or trust, Empower reported. The Journal of Financial Planning study does not argue that AI has no place in personal finance. Its authors concluded that generative AI holds potential to expand access to financial guidance. Still, they said, safeguards are essential, and users who treat chatbot outputs as authoritative advice are taking on risks they may not fully understand.Related: Morningstar drops bombshell warning on AI stocks

The best product doesn’t always win. In most markets, the winner is the product that’s good enough at a price nobody can ignore. Toyota understood that. Southwest Airlines built an empire on it. Now the same playbook is being tested in the most expensive technology race in history.Artificial intelligence labs have spent the past year one-upping each other on capability, and businesses have paid for it. Every automated coding agent and research assistant runs on tokens, the units of text AI models read and write, and monthly bills have grown so fast that some companies now treat AI spending like a second cloud budget.That tension is the backdrop for the newest move from Elon Musk, whose rocket company became a publicly traded AI bet with its June 12 initial public offering (IPO) and has spent the weeks since trying to convince Wall Street that the second half of that description is real.On Wednesday, July 8, SpaceXAI, the artificial intelligence unit of SpaceX (SPCX), launched Grok 4.5, and the sales pitch is unlike anything Musk has tried before. He isn’t claiming he built the best model in the world. He’s claiming he built the one you can actually afford to run all day.What Grok 4.5 brings to the AI fightThe model was “trained alongside Cursor” and built for coding, agentic tasks, and everyday knowledge work, according to SpaceXAI. Agentic tasks are jobs an AI finishes on its own across multiple steps, such as finding a bug, fixing it, and testing the result.That Cursor reference matters. SpaceX agreed in June to buy Anysphere, the startup behind the Cursor coding tool, in a $60 billion all-stock deal, as TheStreet covered, and Grok 4.5 is the first model to emerge from that pairing.Related: Elon Musk wants you to file taxes with GrokThe training run used tens of thousands of Nvidia (NVDA) GB300 graphics processing units, according to SpaceXAI. Grok 4.5 runs on the company’s new V9 foundation model with roughly 1.5 trillion parameters, about three times the size of its predecessor, Musk said on X.Grok 4.5 arrived roughly three months after Grok 4.3 shipped in April, following weeks of private beta testing inside SpaceX and Tesla, Musk wrote earlier this month.Developers got access on July 8 through Cursor, the Grok Build coding agent and the SpaceXAI developer console, with the public rollout following on Thursday, July 9. European availability is expected in mid-July, and usage is free for a limited time in Grok Build and Cursor, the company confirmed.The company’s own benchmark charts tell an honest story. Grok 4.5 topped rivals on the SWE Marathon software engineering test at 29%, but trailed Anthropic’s Claude Opus 4.8 and Claude Fable 5 on SWE Bench Pro, scoring 64.7% against their 69.2% and 80.4%, according to SpaceXAI’s published figures.

SpaceXAI launched Grok 4.5 on July 8, priced at $2 per million input tokens and $6 per million output tokens.SOPA Images / Getty Images

Musk’s pricing play targets OpenAI and AnthropicThe launch was never really about benchmarks. It was about the invoice, and Musk made sure everyone knew it.Here is how the launch pricing compares per million tokens:Grok 4.5 costs $2 for input and $6 for output, SpaceXAI noted.Anthropic’s Claude Opus 4.8 costs $5 for input and $25 for output, according to Reuters.OpenAI’s GPT-5.6 Luna costs $1 for input and $6 for output, Reuters added.Musk framed the tradeoff himself. “It is an Opus-class model, but faster, more token-efficient and lower cost,” the SpaceX CEO said in a post on X, according to Reuters.Then he went further than most executives would. “In fairness, Fable is definitely better than Grok 4.5,” Musk wrote of Anthropic’s flagship model, adding that most tasks don’t require that level of capability, as reported by Stocktwits.I ran the numbers on what that gap means in practice. A company generating one billion output tokens a month, a realistic volume for a mid-sized engineering team running coding agents, would pay about $6,000 on Grok 4.5 versus roughly $25,000 on Opus 4.8. Across a year, the difference approaches a junior developer’s salary.That is the emotional core of this launch for anyone who signs an AI invoice. Musk isn’t selling brilliance. He’s selling relief.What the Grok 4.5 gamble means for SPCX investorsThe market’s first reaction was a shrug. SpaceX shares fell nearly 1% on Wednesday, July 8, to $148.30, a third straight decline that left the stock down about 8% for the week, Stocktwits reported. Shares edged up 0.88% to $149.60 in the premarket of July 9, according to Benzinga.More Artificial Intelligence:Cathie Wood buys $2.1M of tumbling Al stockElon Musk’s Al jobs prediction gets harsh reality checkElon Musk sends wakeup call on runaway Al spendingSome context helps here. SpaceX priced its IPO at $135 a share, raised a record $75 billion in the June 12 debut, and briefly traded above $176 before giving nearly all of those gains back.The muted response fits a stock that has spent its first month erasing its post-IPO pop. Jim Cramer has already warned buyers about the one-way momentum, as seen in my TheStreet coverage. Morningstar said before the debut that it doesn’t count Grok among the leading AI labs, TheStreet highlighted.Pricing is the variable that could change that conversation. Enterprises running autonomous agents are facing “token bill shock,” Counterpoint Research analyst Neil Shah said July 9, Benzinga confirmed. If Grok holds its cost advantage while narrowing the accuracy gap, it could squeeze pricing at OpenAI and Anthropic, Shah added.My analysis is that Musk picked the one fight he can win right now. He can’t out-benchmark Anthropic this quarter, and he admitted as much in public. He can out-price it, however. Musk also told users to expect noticeable gains in the Grok Build coding harness every week, a promise that shifts the story to shipping cadence rather than one launch-day scoreboard.The timing adds pressure, too. OpenAI’s GPT-5.6 arrives Thursday, July 9, which means the cost-versus-capability debate will sit at the center of the AI trade for the rest of the summer.Wall Street remains constructive, despite the wobbly chart. Analysts hold a strong buy consensus on SpaceX with an average price target of $212.08, implying roughly 40% upside, according to TipRanks.For investors, the test is no longer whether Musk can build a frontier model. It’s whether “good enough” at a quarter of the output price shows up in SpaceXAI revenue before the lockups expire and Wall Street’s patience runs out.Related: Elon Musk and Tesla announce serious AI changes for workers

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