Business

The Death the Mutual Fund: Matthew Murawski Explains Why ETFs May Be a Fit as Part of Your Investment Strategy

Published

3 years ago

November 9, 2022

Since the Great Depression, mutual funds have presented a great opportunity for everyday people to invest in the stock market. Rather than risking their fortune on individual winners and losers, investors selected groups of stocks, making them not only a more diversified investment but also more attainable to people who could not afford the high commission fees, in Murawski’s opinion.

And for decades, mutual fund investing has been touted as a smart, principled financial planning strategy. However, those days may soon be coming to an end. As Goodstein Wealth Management financial planner Matthew Murawski explains, a new generation of investors may usher in a new investment strategy.

“We have a big shift in demographics,” Murawski says. “The Baby Boomer advisor has almost all classic mutual funds. But now, an exchange-traded fund does the same basic principle, but they are typically a lot less expensive and are more transparent and tax efficient.”

One of the most important distinctions between mutual funds and ETFs are the costs associated with each. Although Murawski still uses a few mutual funds, most of his portfolio contains ETFs – for the simple reason that they are generally less expensive and more efficient in his opinion.

“There are zero trading costs for an ETF,” Murawski says. “I can buy the S&P 500 index ETF for about a .03 expense ratio and not pay a commission. I can buy it or sell it whenever I want. But if I buy the same thing in a mutual fund, I’m going to pay a $12, $14, $16 commission every time through our custodian, TD Ameritrade.”

With many Baby Boomer investors and advisors retiring, the guidance is beginning to shift toward a younger generation. And according to Murawski, new advisors and this new investing class are overwhelmingly choosing ETFs.

“I don’t know anybody under 40 buying mutual funds,” Murawski says. “If I said to a client under 40, we’re buying mutual funds in an account, a majority of them will ask, why aren’t we buying ETFs?”

This gradual transition from mutual funds to ETFs is being seen throughout the investment world. ETF.com has projected that in the near future, ETF assets will exceed mutual fund assets. And traditional mutual fund advisors are beginning to take notice. They are trying to adapt to the changes in the market, as well as changes in investment strategy, to maintain relevance with a new generation of investors.

“In my opinion, investors under 30 will never own mutual funds,” Murawski says. “It would be like selling them a Discman. It is almost out of style. So mutual fund companies are being forced to change and come out with ETF versions of the same mutual funds.”

Another way that mutual fund companies are able to adjust is by offering what they call clean shares – dramatically reducing the cost of buying mutual funds. These represent important changes in the way mutual fund companies compete with the emergence of ETFs.

“In my opinion, In the end, those that are not innovating are losing massive amounts of assets,” Murawski says. “The pandemic alone brought millions of new investors into the market. And I do not feel those investors are not going to buy mutual funds.”

In the end, it comes down to cost and performance – and many actively managed mutual funds are not outperforming their benchmarks enough to justify their cost. Instead, investors are choosing ETFs, which can give them nearly the exact same thing at a lower price.

“When you don’t outperform and you charge more, it’s problematic,” Murawski says. “In my opinion, mutual fund companies are either dying or they’re innovating and moving toward a different structure.”

Matthew Murawski is a financial planner with Goodstein Wealth Management. He provides personalized wealth management advice to the firm’s 401(k) clients as well as his own individual clients. Murawski educates investors to help them work towards being positioned for long-term financial growth.

To learn more about Murawski and Goodstein Wealth Management, visit www.goodsteinwm.com or connect on Facebook, Instagram, and Twitter.

Business

MetaWorx: Building Full-Stack AI Teams, Not Just Automation

Published

6 days ago

June 6, 2025

Matthew Wiles

Automation still dominates most headlines, yet the returns often fail to meet expectations. A sprawling chatbot rollout might shave a few support tickets, but it rarely shifts the profit-and-loss statement in a lasting way.

McKinsey’s 2025 workplace survey pegs AI’s long-term productivity upside at $4.4 trillion, but only one percent of enterprises say they’ve reached true “AI maturity.” MetaWorx, a Dallas, Texas-based AI employee agency founded by Rachel Kite, argues that the shortfall has nothing to do with models and everything to do with people.

“Treat AI like a point solution and you’ll get point-solution results,” shares Kite. “You need a roster that can carry the ball from raw data to governance, or the whole thing stalls at the proof-of-concept phase.”

The pod blueprint

When a plug-and-play automation script collapsed under real-world data drift, costing Kite a lucrative contract, she sketched the six-person “pod” that now anchors every MetaWorx engagement:

An infrastructure architect to tame compute costs.
A data engineer to secure and shape pipelines.
An applied scientist to prototype models against live feedback loops.
An MLOps engineer to automate rollback and retraining.
A domain product lead translates forecasts into features users actually notice.
Ethics and compliance analysts to stress test outputs for bias and keep the audit.

The team’s first sprint still delivers a quick-win bot — “small enough to calm the CFO,” jokes Kite — but the roadmap quickly pivots to reliability, explainability, and eventually optimization. By tying every algorithmic decision to a quantifiable business metric, the pods turn AI from a science project into a growth lever.

Recruiting for curiosity, not credentials

With Bain & Company predicting a global AI-skills crunch through 2027, MetaWorx has stopped chasing unicorn résumés. Instead, it hires “adjacent athletes”: a computer-vision PhD who hops from medical imaging to warehouse surveillance, or a former journalist who recasts her nose for story into prompt-engineering finesse.

“Domain expertise expires fast,” Kite says. “What doesn’t expire is the instinct to ask better questions.” The result is a lattice of overlapping skills that stays flexible when models wander into the long tail of edge-case data.

A culture of rapid experiments

Inside MetaWorx, every idea faces the same litmus test: ship something — anything — into a user’s hands within 21 days. The “three-week rule” forces prototypes into the wild early, where failure is cheap and feedback is swift. Post-mortems, including cost overruns, are circulated company-wide, erasing any stigma associated with missteps.

That laboratory mindset powers velocity. “Our first model is almost always wrong,” Kite admits, “but version 1.0 is the tuition we pay for version 2.0.” The philosophy echoes her TEDx talk on resilience: progress is iterative, not heroic.

How leaders can steal the playbook

Executives itching to replicate MetaWorx’s results don’t need a blank check. Kite offers a five-step sequence:

Inventory pain points, not tools: Walk the P&L line by line and tag the friction you can measure.

Map the stack to the problem: A recommendation engine, for instance, requires behavior data, retraining triggers, and feedback capture — automation alone won’t suffice.

Stand up a pod: Reassign existing talent into a cross-functional tiger team before hiring externally; the chemistry test is free.

Measure the story, not just the statistic: Pair model accuracy with human-scale metrics like ticket backlog or employee churn.

Budget for the boring: Reserve at least 30 percent of spend for MLOps and governance; Stanford’s HAI review links most AI failures to neglected upkeep.

Taken together, those steps shift AI from a pilot novelty to an operational habit that compounds value rather than topping out after an initial PR splash.

Character still scales faster than code

MetaWorx plans to double its headcount this year, yet Kite insists the secret isn’t a proprietary framework or a monster war chest. It’s credibility. Clients see a founder who has wrestled with the same outages and surprise bills they face. That authenticity converts skeptics faster than any algorithmic novelty.

“Tools level out,” Kite says. “Culture compounds.”

The insight lands in a marketplace still dazzled by generative fireworks. Yes, MetaWorx ships models and dashboards, but its true product is a mindset: resilience over rigidity, questions over credentials, experiments over edicts. In Kite’s world, automation is merely the appetizer. The main course is a full-stack team that knows why the model matters to the business and who owns its success after launch day.

And that, Kite argues, is how AI finally graduates from cost-cutter to growth engine, one curious pod at a time.