You may have seen the term artificial intelligence (AI) so many times that, by now, your eyes glaze over at the mention of it!
It reminds me of a few years ago when the Internet of Things (IoT)—the ability to connect various devices to perform tasks more efficiently (think smart watches and phones, energy grids, and transportation networks)—emerged as the new buzzword. The hoopla then was amazing; IoT was in the news every day … for years, and the technology radically changed many tasks and services that we now consider commonplace.
And now something similar is happening with AI. But while IoT created an incredible number of opportunities, including new products, improved operations, and huge cost-savings, I’m just going to say it—with AI, you ain’t seen nothing yet!
What Is Artificial Intelligence?
Let’s start with an example. The best one I can conjure up is Hal, the computer in the movie 2001: A Space Odyssey. Remember, Hal, who had been programmed to “think like humans,” killed his space flight crew, except for Dr. David Bowman, who eventually was transformed from human to pure energy.
And while I hope that AI doesn’t actually move in that dire direction, the concept is essentially the same. AI is basically teaching machines to think and learn like humans, so the machines can perform tasks that would normally require human intelligence. Some of the programmable functions of AI include visual perception, reasoning, decision-making, speech recognition, problem-solving, and translation between languages.
Identifying patterns is at the root of AI systems, which allow machines to learn from gathered data, with the goal of improving their performance over time.
No doubt, you have already been using or subject to AI and don’t even realize it. If you’ve recently called any 800 number or consulted a chatbot or voice assistant, you’ve no doubt been communicating with a machine that has AI functions. Those uses are still very much in their infancy, as you can tell if—like me—you have been totally frustrated with the answers you received and started yelling at the chatbot that “I need to speak with a real person!”
Here are some other everyday applications of AI that you are probably using:
- Siri or Alexa answering your questions, dialing a phone number, setting your alarm clock, or playing your music
- Social media feeding you content (and ads) based on your previous scrolling
- Those annoying ads that pop up when you are shopping online
- Email spam filters.
In my other profession, as a real estate company owner, I’m beginning to see AI used quite frequently as Realtors consult ChatGPT or some other AI product to write descriptions of homes they have listed. Oh my, I’ve never seen such beautifully written opuses about the features of homes—and especially from agents who I know do not possess such writing skills!
Honestly, many of the recommended phrases are outstanding. Unfortunately, a lot of them do not match up with a home’s actual features. The lesson here is, if you’re going to use AI to write such narratives, you might want to proofread them!
The Four Types of AI
There are four main types of AI:
Reactive Machines respond directly to their immediate environment without any memory or understanding of past experiences. They can only react to the current input and are the most basic type of AI, such as IBM’s Deep Blue, the chess-playing supercomputer, which reacts only to current moves and doesn’t store memory of previous games or Netflix’s recommendation engine based on your viewing habits—not on how you liked the movies.
Limited Memory Machines can store past experiences and use them to make future predictions or decisions. They can learn from data and adapt their behavior based on their memory. Examples include self-driving cars, chatbots or virtual assistants.
Theory of Mind would be capable of understanding and predicting the thoughts, feelings, and intentions of others, similar to how humans do. It’s a more advanced concept that is still theoretical. Uses included education and healthcare, in which customized learning and treatment plans could be formulated, based on the student’s/patient’s needs.
Self-Aware AI is the most advanced and theoretical type of AI. Self-aware AI would possess consciousness, self-awareness, and the ability to understand its own existence and emotions. This highly evolved AI would be “a machine that not only understands human emotions but also experiences its own emotions and has a sense of self.” Oh, that sounds like Hal, doesn’t it?
AI Applications
I mentioned some of the applications that AI is currently supporting. But those are just the tip of the iceberg. The near-term future of AI is pretty remarkable, and includes:
Healthcare: Early diagnosis of diseases, drug discovery, personal treatment plans
Manufacturing: quality control, waste reduction, optimization of production
Transportation: Autonomous vehicles, traffic flow, and improved navigation systems
Education: Tutoring systems, lesson planning, adaptive learning tools, identifying at-risk students, safety issues, and automated grading systems
E-commerce: Inventory management, assessing customer demand
Marketing: Targeted advertising, customer segmentation, and campaign optimization
Financial institutions: Risk management, algorithmic trading, fraud detection
Security: Facial recognition, surveillance, threat detection, and cybersecurity
Agriculture: Crop monitoring, yield prediction, and irrigation optimization
Law: Legal research, due diligence, document preparation and review and litigation analysis.
Of course, in the long term, the uses and applications for AI will probably read like science fiction, but hopefully, they will include safeguards that will protect consumers, buyers, corporations, workers, and investors.
The Pros and Cons of Artificial Intelligence
When the news media discusses AI—along with its predicted uses—they almost always introduce the fear factor—machines taking over the human race. I can offer no educated opinion on that dire consequence, but I can offer you the current take on the advantages and disadvantages of AI—from industry experts.
The Pros
- Increased Efficiency and productivity
- Automation of repetitive tasks, allowing humans to focus on more creative and strategic work
- Improved accuracy and reduced errors while processing information quickly and consistently
- Cost savings through automation
- Creation of predictive analysis to enable better planning and mistake avoidance by businesses
- Personalization and customization of individual needs and wants
- New insights and discoveries, as a result of the analysis of huge data to identify patterns and insights that humans might miss
- Enhanced accessibility for technologies that can help the disabled
- Real-time feedback
- Improved healthcare via more accurate medical diagnoses, treatment planning, and drug discovery.
The Cons
- Job displacement from automation, which can lead to economic inequality and instability; according to a report by PwC, AI and related technologies could lead to the displacement of up to 30% of the jobs in the financial services industry by 2025
- Ethical concerns about bias, fairness, and accountability, particularly in decision-making and hiring processes
- Fake news and polarization (deepfakes), which we are already seeing, using the images and/or voices of celebrities to falsely peddle anything from cryptocurrency to medications to weight-loss gummy bears
- Data dependency on data, which can be incomplete or biased, leading to inaccurate or unfair outcomes
- Lack of human intuition and emotional intelligence, in tasks requiring intuition, creativity, or emotional understanding
- Privacy and security risks, from the large amounts of personal data parsed through AI, which could tempt the unscrupulous to steal identities, create disinformation, or even stalk a person who doesn’t want to be found
- Potential for misuse for malicious purposes, such as creating misinformation or creating cyberattacks
- Increased inequality, as the benefits of AI may not be evenly distributed, potentially exacerbating existing inequalities, such as the bias against women hires that was discovered at Amazon
- Expanded energy consumption from data mining
- Creation of autonomous weapons
- Loss of authenticity (makes cheating easier) and boundaries more difficult to define.
If you haven’t actually tried out any of the AI tools, you may want to consider these that are ranked as the Top 23 by Synthesia:
The Best AI Tools by Category
AI Assistants (Chatbots): ChatGPT, Claude, Gemini, DeepSeek, Grok
Video Generation and Editing: Synthesia, Runway, Filmora, OpusClip
Image Generation: GPT-4o, Midjourney
Notetakers and Meeting Assistants: Fathom, Nyota
Research: Deep Research
Grammar and Writing Improvement: Grammarly, Wordtune
Search Engines: Perplexity, ChatGPT search
Social Media Management: Vista Social, FeedHive
Graphic Design: Canva Magic Studio, Looka
App Builders & Coding: Bubble, Bolt, Lovable, Cursor, v0
Project Management: Asana, ClickUp
Scheduling: Reclaim, Clockwise
Customer Service: Tidio AI, Hiver
Knowledge Management: Notion AI Q&A, Guru
Email: Hubspot Email Writer, SaneBox, Shortwave
Presentations: Gamma, Presentations.ai
Resume Builders: Teal, Kickresume
Voice Generation: ElevenLabs, Murf
Marketing: AdCreative
Sales: Clay
AI in Finance and Investing
While I find the entire subject of AI intriguing and exciting, there is one area where AI is expanding that worries me. And that’s in finance and investing.
Of course, as an investment newsletter writer, I’m understandably concerned about the proliferation of articles, press releases, and investing columns that I read and that I know are not written by humans.
Certainly, I am all for increasing efficiency and productivity, and I wholeheartedly agree that the AI tools that can handle the following tasks just make sense:
- Financial reporting and analysis: Improved accuracy and efficiency by minimizing errors and streamlining processes
- Enhanced decision-making by providing valuable insights to support informed decision-making
- Reducing costs by employing automation to reduce manual work and improve efficiency
- Increasing productivity by automating repetitive tasks, thereby freeing up human resources for more strategic and analytical work.
But I also have a real problem with articles that are patched together from numerous sources (often unidentified) that are essentially—at best—untested theories and—at worse—harmful advice.
The uses for AI in finance and investing are rapidly increasing. And many of the tools will be very valuable to consumers and investors. But many others will have the potential for leaving folks vulnerable to fraud, identity theft, and highly unprofitable investing strategies and decisions.
So, let’s take a look at the current available tools, decipher where and when (if at all) to use them, and which ones you need to avoid at all costs.
First, let’s look at some of the tools that financial institutions are employing and that are radically changing how they do business:
Financial Institution AI Tools
According to PYMTS.com, 72% of finance leaders say they are actively using AI in their operations for a variety of tasks, including fraud detection (64%) and customer onboarding automation (42%). Other applications include:
Algorithmic trading can be used for analyzing market trends and historical data to make decisions and execute trades faster than humans. AI-enabled trades can be made at high speed based on real-time data and market trends.
Automation of repetitive and time-consuming tasks will enable institutions to process large amounts of data faster and more accurately.
Fostering innovation to make institutions more competitive
Compliance tools can automate monitoring and reporting requirements to ensure regulatory compliance.
Credit scoring takes into account many data points, including social media activity and other online behavior, to more accurately assess customers’ creditworthiness.
Automation of certain tasks can reduce manual labor, streamline workflows and improve operational efficiency, resulting in cost reduction.
Customer service using AI-powered personal assistants and chatbots can be provided 24/7, and can provide more personalized services such as real-time credit approvals, extra fraud protection and cybersecurity measures. Additionally, voice assistants and chatbots can easily help with account inquiries, proactive reminders for bill payments, savings goals, and insights into spending habits.
According to the CFPB, more than 110.9 million users are expected to interact with a bank’s chatbot by 2026.
Increasing cross-selling of products as AI algorithms analyze customer behavior to recommend relevant financial products, services, and investment opportunities.
Data analysis AI can analyze massive amounts of data, providing insights and trends that would be very difficult and time-consuming for humans, leading to better decision-making.
Fraud detection algorithms can prevent financial crime, such as fraud and cyberattacks, by identifying unusual patterns in financial transactions, helping to improve security in activities such as online banking and credit card transactions.
Loan processing is used to better predict and assess loan risks, and streamline the process and approvals for borrowers by automating tasks such as risk assessment, credit scoring and document verification.
Predictive analytics is used to enable predictive modeling, forecasting market trends, potential risks and customer behavior.
Risk management tools are utilized to assess and manage risk.
Compliance monitoring to make sure regulations and rules are being followed.
Sentiment analysis monitors news sources, social media and other information to gauge market sentiment, to aid in predicting market trends.
Most customers are in favor of using artificial intelligence to improve their satisfaction with their financial institutions. That’s because 47% of them are tired of being transferred from one representative to another while repeating the same information. Honestly, when the voice system asks for your account number, your social security number, and your password, why do you have to repeat the same thing to the next three real people to whom you are inevitably transferred?
Additionally, 56% of consumers say their institutions don’t proactively anticipate their financial needs.
Maybe that’s why 76% of consumers believe AI will be a “standard part of their financial services relationships” in the future, according to Salesforce.com.
Another study by MX.com indicated that consumers are not quite yet ready to hand over all their finances to AI:
- 59% trust AI to deliver proactive reminders to pay bills, save money, etc.
- 57% trust AI to provide a comprehensive breakdown of how they spend money
- 51% trust AI to personalize recommendations on where they can make changes to improve their finances
- 51% trust AI to provide customer support when they need assistance
- 50% trust AI to automatic savings options, such as rounding up to the nearest dollar on purchases and depositing in savings
- 46% trust AI to provide basic financial advice, such as how much to put in savings or pay towards debt
- 42% trust AI to categorize transactions to help understand where money is going
- 42% trust AI to process a loan or credit application based on their financial data, including setting credit limits or approving/denying a loan
- 39% trust AI to provide investment-related financial advice, such as stocks to purchase, retirement planning, etc.
How Best to Use Investment AI
Artificial intelligence is being widely integrated into the investing scene for the following tasks:
- Research and analysis
- Investor sentiment
- Investment recommendations
- Portfolio management
- Personalized financial advice from AI-powered chatbots and advisors
- Investment recommendations from robo-advisors using algorithms to tailor investment profiles
- Asset allocation and investment strategies
- Portfolio rebalancing
- Online trading platforms and advice.
While consumers may be willing to accept AI to more efficiently manage their banking, budgeting, and credit tools, they are more reticent in handing over their investing lives to AI tools.
The CFP Board reports that 31% of investors “feel comfortable actually implementing generative AI advice (a type of artificial intelligence that creates new content like text, images, and code, based on existing data) without first verifying it elsewhere.” I personally think that verification is a really good idea!
Language AI (such as ChatGPT) is already being used extensively by financial advisors in certain client communications, by searching through lots and lots of data to answer customer questions. One of the biggest problems with generative AI is “garbage in, garbage out,” meaning you have to ask the correct question in order to receive the answer you are looking for.
And you must remember, the answer depends on the data the AI tool is parsing. Sometimes, the publicly available data is not the highest quality (including all those blogs and social media posts by folks who may not be qualified in the finance/investment fields). And the source may be missing or untested.
The primary weakness of generative AI is that it tries to give you a response that you want to see; it even goes so far as to make up sources—what experts call “hallucinations”—in order to give users what it expects they want.
I often use very general questions when I begin my research on investments, and I usually get a lot of interesting answers from artificial intelligence tools, accompanied by various statistics. However, when I try to trace the sources, I’m often stymied and can’t figure out where the information actually originated.
That’s why you must formulate the right question to ask—and always verify the sources.
Most generative AI models want to be “all things to all people,” so they should not be relied upon to give investors specific, tailored advice.
Numbers AI, alternatively, is the technology that looks for patterns and executes trades based on who’s buying and selling, according to Daniel Satchkov, co-founder and president at RiXtrema, a software platform for financial advisors and brokers. Industry experts say that it works extremely well in quantitative and technical trading—big-money deals, to be precise.
High-net-worth investors have been using these AI tools for years, but now some ETFs are employing them, making them available to all types of investors. One such example is the BTD Capital Fund (DIP), an AI-powered fund that uses AI to identify and trade short-term moves in the market using a “buy the dip” strategy. Additionally, Amplify AI-Powered Equity ETF (AIEQ) uses IBM’s Watson to analyze millions of data points and select stocks.
And while these tools may work well in high-volume trading for big institutional and well-heeled clients, the average investor will find them lacking, as these models cannot understand context or nuance. Consequently, it’s essential that a human oversees the process. Otherwise, investors are bound to make a lot of mistakes, lose money, and risk their overall financial security.
There are certainly advantages to using an investment AI tool, including:
Lack of emotional bias. One of the greatest investing strategies is discipline—remaining emotionally absent from buying and selling decisions. That’s not so easy to do, and using an AI tool removes that challenge.
Accuracy. Technology, including quantum machine learning (QML) and computer vision, is being deployed for complex probability calculations and analyzing visual data (from technical charts to satellite imagery). The goal is to find subtle correlations that traditional statistics may not, making calculations and recommendations more accurate. One example I found that I thought was very interesting was that there are systems that review earnings call transcripts using natural language processing (NLP) or leverage large language models (LLMs) to scan and analyze reams of social media posts in order to track the emotional content of the calls in real-time. I’d like to see those results!
Pattern identification, especially beneficial to traders, as I said earlier.
Quick and timely real-time portfolio adjustments and rebalances.
Researching and gathering news that may affect your investments. Using LLM-enhanced research and analysis in large language models like ChatGPT, Gemini, Grok, and Claude can be helpful in your analysis.
Tailored advice for specific strategies such as investing in environmental, social and governance stocks.
How AI Investing Works
In addition to rapidly evolving technology, one of the reasons that financial institutions are so anxious to bring artificial intelligence to investing is very simple: We are on the threshold of one of the largest transfers of wealth in the history of this country, and they want to rake in as much money as possible.
The amount? Some $84 trillion, according to The New York Times. That’s the inheritance expected by Millennial and Gen X heirs through 2045. And about $16 trillion of that will flow through the generations in just the next ten years.
Predictive risk analysis. With the ability to review and assess mass quantities of data, it makes sense that AI could be used to understand how a decline in one sector may affect others, bleeding through to your individual investments. For example, when COVID struck, remote workers emptied out office space, which led to a decline in commercial real estate. But I don’t think you needed a robo-advisor to tell you that. I mentioned this many times to my subscribers and didn’t make any recommendations in that sector.
Generate backtesting insights. Okay, I agree that backtesting is invaluable. And it is probably much easier using automated technology to review past market cycles. But an experienced advisor already takes that into account when making investment recommendations.
Robo-Advisors Are Appealing to the Younger Generations
Technology is very important for the younger generations, as a recent survey from Experian revealed that “67% of polled Gen Zers and 62% of surveyed Millennials are using artificial intelligence to help with their personal finances.” They are mostly using generative AI tools like ChatGPT for saving and budgeting (60%) and credit score improvement (48%).
But 48% of them are also employing AI for investment planning.
I’m thrilled that the younger generations are paying attention to their finances, but it also worries me, for two reasons:
1. Many are extremely comfortable with technology (worship, perhaps?) and may rely too heavily on automated systems that—while seemingly customized to their risk tolerance, age, income, etc.—exclude the human element that may ultimately determine whether or not their portfolios and strategies are successful over the long term.
2. The majority of Millennials (also known as Gen Y), born between 1981 and 1996 and Gen Zers (born between 1996 and 2010) have little investing experience and may be easily persuaded into an investing strategy that hasn’t been fully practiced and measured.
What concerns me most is the results of a study that reported that, “Some 31% of Gen Zs and 20% of Millennials are using robo-advisers.”
Robo-advisors collate data regarding your personal finances, risk tolerance, goals, etc., and use algorithms on digital platforms to devise a financial plan and investment strategy—with little human interaction or supervision.
The robo-advisor then recommends a (hopefully, diversified) portfolio, automatically manages it (buying, selling, and rebalancing) with little input from a real person, or even you.
And like any investing strategy, there are advantages and disadvantages to robo-advisors.
The Pros
The goal of robo-advising is to make investing automatic, efficient, and virtually thought-free for the investor.
Oh, if only investing really worked that way!
Believe me, I am all for automation, especially in routine tasks. It’s absolutely wonderful to set up a portfolio online and let the computer check the daily prices, automate your stop-losses, and alert you when there’s news or the stock has moved radically in either direction.
Easy to set up a portfolio. And I think that investors who just don’t want to take any time to learn about investing may actually benefit from a robo-advisor that selects an initial portfolio of exchange-traded funds (ETFs) for him or her. I’m also all for automatic monitoring of prices and news that can affect your portfolio.
But I’m very opposed to letting a software program decide how and when to buy and sell or rebalance my portfolio.
Cost savings and speed. The pro-robo-advisor community touts the cost savings and speed of using such a program. Robo-advisors charge between 0.25% and 0.50% as an annual management fee, and some do require a minimum initial investment of $5,000; others have smaller or no account minimums.
But, honestly, with the average fee to trade a stock between $3 and $7, cost really isn’t a factor. Of course, if you want a personal broker or financial advisor, you will have to pay 1% to 2% of your assets annually, or you may be like our subscribers, who find an investment newsletter(s) that meshes with their personal investing goals and strategies, which does come with a fee, although a pretty minimal fee.
And as for speed, sure, automation would certainly be faster than studying and researching investments, but does the robo-advisor really make the right decision for you?
24-hour availability. Another advantage to robo-advisers is that they are available 24/7, which also appeals to the younger generation.
The Cons
Over-dependence on technology can lead to some risky decisions and a losing portfolio. With such easy access to automation, newbie investors may become overconfident in their abilities and take on too much risk (remember when day trading was the rage and cost novice investors billions of dollars?)—gambles that an experienced advisor would protect against. Since you can’t reason with a computer and have no human to consult, you may not adequately understand the risks associated with an investment.
The regulatory environment for robo-investing is not mature. Right now, regulatory bodies, including the SEC, NASAA, and FINRA, have issued warnings to investors about fraudulent schemes that claim to use “proprietary AI trading systems” promising outsized returns. Many of these platforms are unregistered, so are actually subject to no regulation. Make sure to check with FINRA to see if they are registered and if any complaints have been filed: https://brokercheck.finra.org/
Just remember, if those promises of outsized investing returns look “too good to be true,” they are.
Performance may not be all it’s cracked up to be. For instance, the Amplify AI Powered Equity ETF (AIEQ) returned some 12% last year, while the S&P 500 Index posted a 22% gain.
Using AI Effectively
Look, I am not opposed to using AI in investing. I’m just saying, be careful. It’s a great tool for research and education, and helping you devise an initial ETF portfolio. But—while I know many folks don’t want to or have any interest in learning about investing—it would be foolhardy, in my opinion, to entrust that very important endeavor to just a machine.
Find a decent advisor—maybe more than one—and take steps to learn, at least a little, about investing. You need to have enough knowledge to know when the advice you are receiving is good, bad, or downright fraudulent.
My advice—stay vigilant; use the tools that are available; be responsible for knowing your own investment profile so that you can personally devise a strategy and goal that best suits your and your family’s needs.
Where to Invest
As I mentioned, there are AI-powered ETFs available, and tons of start-ups and cutting-edge AI companies, but I think you are better off looking at the technology companies that have a long history and a successful track record and that have expanded into artificial intelligence sectors.
Here are three names that you will be very familiar with—companies that are making huge strides in artificial intelligence and that look attractive to me. They have all declined during the recent market volatility and are now trading at discounted prices:
International Business Machines Corporation (IBM)
Amazon.com Inc (AMZN)
Nvidia Corp (NVDA)
