Best stocks by sector

Best AI stocks: how to choose them with a real method

Updated June 17, 2026 · DeepTicker

Searching for the best artificial intelligence stocks has become one of the questions most repeated by individual investors, and also one of the most dangerous if approached without a method. AI moves headlines, promises and sky-high valuations, but behind the noise there are very different businesses: some really make money and others only promise to make it someday. This guide is educational, not a shopping list: it teaches you how to identify, analyze and choose AI companies with solid fundamentals, which metrics truly matter, and how to separate the quality from the story. The idea is simple: invest with serious fundamental analysis, but made easy so that you decide for yourself.

The artificial intelligence sector is not a homogeneous industry, but a value chain with very different links. At the base are the semiconductors (the GPUs and specialized chips that train the models), followed by the cloud infrastructure and data centers (the computing power that is rented out), the platforms and foundation models (the large language models and their APIs), and finally the application software that incorporates AI to solve concrete problems for companies and consumers. Each link has different margins, risks and competitive dynamics, so talking about "AI stocks" as a block is a mistake from the start. To invest in artificial intelligence with a method, the first thing is to know which part of the chain the company you are looking at is really in.

The appeal of the sector is real: demand for computing is growing explosively, many of these companies bill billions and some have competitive advantages (moats) that are hard to replicate, such as software ecosystems, proprietary data or high switching costs. But the same enthusiasm that makes them attractive inflates their prices. A company can be excellent and, at the same time, be wildly expensive: these are two distinct questions. Confusing them is the most expensive mistake the novice AI investor makes. The perfect company at an absurd price can deliver mediocre returns for years while the business "catches up" to the valuation.

That is why it pays to separate three questions that are rarely asked together. Is the company good? (business quality: profitability, growth, balance-sheet strength). Is it expensive or cheap today? (what expectations the price already has baked in). And is there a sustainable franchise? (a real advantage that protects those profits from the attack of competition). At DeepTicker these three questions are answered with widely recognized fundamental-analysis methods, translated into clear numbers: the quality DeepTicker Score, the valuation Reverse DCF and a franchise test (EPV, the value of current profits with no growth). And because every figure comes explained, the more you analyze, the more you learn to tell a good AI stock apart from a mere fad.

Throughout the guide we will mention some well-known companies in the sector only as illustrative examples to study the method, never as a recommendation. The goal is not to give you a shortcut, but to teach you to think like an analyst: look at the business, look at the price and judge for yourself whether the expectations the market discounts are credible.

What to look at to choose the best AI stocks

Before you fix on the price, evaluate the quality of the business. In AI, the key questions are: does the company really make money today or live on promises and funding rounds? Does it have a high and sustained ROIC (return on invested capital), a sign that it reinvests well? Is revenue growing profitably or just "growing while burning cash"? Is there a competitive advantage (moat) that prevents a rival from copying its product in six months? This is the question that the analysis of quality and competitive advantage places at the center of everything: what matters is the moat and a high, durable ROIC, not growth at any cost.

DeepTicker sums up that quality in the DeepTicker Score, a 0-to-100 grade that combines 5 dimensions (Valuation, Growth, Track record, Profitability and Solvency) comparing them with the rest of the sector, because a 30% margin or a P/E of 25 do not mean the same in semiconductors as in software. The grade translates into a clear label: Elite (>=80), Solid (65-79), Acceptable (45-64), Fragile (30-44) or Critical (<30). So, at a glance, you know whether an AI company has quality fundamentals or whether its appeal is only the story. You can see the detail in the stock fact sheets and learn what pushes each dimension up or down.

The metrics that matter most in AI stocks

Not all metrics are worth the same depending on the link. In semiconductors and infrastructure, look at gross and operating margins, the ROIC, the capital intensity (how much you have to invest to grow) and the dependence on a few customers. In software with AI, what is relevant is recurring revenue, customer retention, the gross margin (ideally high) and whether growth translates into free cash flow or only into billing. In foundation models, watch the cash burn: training models is wildly expensive and many projects are not yet profitable. The golden rule is to be wary of growth that does not generate cash.

Here comes the second question: is it expensive or cheap TODAY? Instead of inventing a "target price", DeepTicker applies the discounted cash flow valuation (Reverse DCF): it turns the calculation around and tells you what growth and what margin the current price is discounting, so that you judge whether you believe it. A real example from the system itself: a company trades at $372 and today grows ~12% a year; the price is only justified if it grows ~18% a year for 10 years and lifts its cash margin from 20% to 32%. Growth is not projected flat, but rather moderates year by year down to ~2.5% (multi-phase model). The verdict is summed up in an honest scale: Bargain, Reasonable, Demanding, Expensive, Priced-in bubble. In AI, a great many stocks fall into "Demanding" or worse.

Risks of the AI sector you should not ignore

The first risk is valuation: in such a hot sector, prices incorporate heroic expectations. If a company already discounts growing at 30% for a decade, it is enough for it to grow "only" at 20% for the stock to fall hard even if the business is doing well. The second is technological obsolescence: what is a competitive advantage today can be a commodity tomorrow, and a new model or a competitor with more data can erode an apparently solid moat. The third is concentration: many companies depend on few customers or suppliers (for example, a single chipmaker), which makes them fragile.

There are also regulatory risks (privacy, copyright, restrictions on chip exports) and an obvious psychological risk: FOMO, the fear of missing out, which pushes people to buy high and late. That is why a mathematical brake helps: a useful rule from value analysis is that if the growth implied in the price equals or exceeds the cost of capital (G >= R), the price is not rational, it is a "discounted miracle". DeepTicker issues that warning automatically, and it also reminds you when the classic Reverse DCF does not apply well (for example, an AI startup with no stable revenue, where you have to look at cash, pipeline and dilution instead of a misleading number).

How to find the best AI stocks with a screener

Looking at company after company by hand is unfeasible. The professional way to find AI stocks with good fundamentals is to filter thousands of companies at once with a screener and then study in depth the ones that make the cut. With the DeepTicker stock screener you have more than 140 filters and ready-made strategy presets (including classic strategies such as Graham or Magic Formula) to narrow the universe: you can ask, for example, for technology companies with ROIC > 15%, high gross margin, sustained revenue growth, low debt and a high DeepTicker Score, discarding those that only live on the story.

The screener is not for finding "the winning stock", but for building a short list of quality candidates that you then analyze one by one: their DeepTicker Score, their valuation verdict with the Reverse DCF and whether they have a franchise. That way you go from a universe of thousands to a handful of companies you understand and whose price you know how to judge. It is exactly what a fund does, only made simple and with every number explained, so that you learn while you filter.

Artificial intelligence: quality versus price

The great lesson of the sector is that quality and price are distinct things, and you need both. An AI company can be a spectacular franchise (Elite DeepTicker Score, clear moat, very high ROIC) and still be a bad investment at a price that discounts perfection. And the other way around: a mediocre company never becomes good by being cheap. The investor with a method crosses both dimensions: looks for quality businesses at reasonable prices, or at least knows exactly what expectations is paying for.

Value analysis brings a valuable tool here: the EPV (value of current profits with no growth). Comparing the value of the current business with what it would cost to replicate it tells you whether there is a real franchise. If you pay well above the EPV, you are paying pure future growth, and that growth has to materialize for you to make money. Putting the three views together, quality (DeepTicker Score), price (Reverse DCF) and franchise (EPV), you get a complete picture of any AI stock. This is not financial advice: it is information so that the decision, with everything in view, is taken by you.

Choosing the best artificial intelligence stocks is not about guessing the next star, but about applying a method: separating the quality of the business from the price you pay and judging with data whether the expectations are credible. DeepTicker gives you that rigor, DeepTicker Score, Reverse DCF and franchise test, in a simple and transparent way, with every number explained so that you learn by using it. Filter the sector with the stock screener, study the fact sheets and decide for yourself with a method. The Contest and My Portfolio are free, and you get a 14-day trial with no card. This is educational information, not financial advice.

Open the stock screener →

Frequently asked questions

What are the best artificial intelligence stocks to invest in?

There is no universal list of "best stocks", because it depends on your profile, the price they trade at and the moment. Instead of looking for names, look for a method: identify companies with good fundamentals (high profitability, competitive advantage and strength) and check whether their price discounts reasonable expectations. This is educational information, not financial advice.

How do I choose AI stocks with good fundamentals?

Look at the quality of the business (high and sustained ROIC, margins, profitable growth and little debt) before the price. The DeepTicker Score sums up those five dimensions in a 0-100 grade compared with the sector, so that you see at a glance whether a company is Elite, Solid or Fragile.

How do I know if an AI stock is expensive or cheap?

With a Reverse DCF: instead of giving a target price, it calculates what growth and what margin the current price is already discounting. If the price is only justified by growing at 18% or 30% for ten years, you decide whether you believe it. DeepTicker sums up the verdict in Bargain, Reasonable, Demanding, Expensive or Priced-in bubble.

Are there undervalued AI stocks?

There may be, but they are rare in such a popular sector, where optimism tends to inflate prices. To detect a possible undervaluation you need to compare the quality of the company with what its price discounts; a solid business whose Reverse DCF comes out "Reasonable" or "Bargain" is more interesting than an expensive promise.

What should I look at in AI stocks?

At whether the company makes money today or only promises it, at its ROIC and margins, at the strength of its competitive advantage (moat), at its dependence on few customers or suppliers and, above all, at what expectations the price already has baked in. Quality without a reasonable price yields little.

Is it a good idea to invest only in AI companies?

Concentrating your whole portfolio in a single hot sector greatly increases risk, especially if valuations are demanding. Diversification and a cool analysis of quality and price reduce the danger of buying high out of FOMO. Remember that this is educational information, not a recommendation.

How can I find quality AI companies with a screener?

By filtering thousands of companies by objective criteria: high ROIC, solid margins, profitable growth, low debt and a high DeepTicker Score. The DeepTicker screener offers more than 140 filters and ready-made strategy presets to create a short list of candidates that you then analyze in depth one by one.

Why can an excellent AI stock be a bad investment?

Because quality and price are distinct things. A magnificent company at a price that discounts perfection can deliver mediocre returns for years while the business "catches up" to its valuation. That is why it pays to cross the DeepTicker Score (quality) with the Reverse DCF (price) before deciding.