Infrastructure AI Stocks
Infrastructure AI stocks build the foundation that modern AI runs on. They supply the data centre hardware and systems needed for large scale training and real time inference, including GPUs, server CPUs, and the networking that keeps AI clusters running efficiently, instead of selling AI apps. This category benefits early in the AI cycle, but it can be more sensitive to capex swings, large customer concentration, and chip export restrictions.
NVIDIA (NVDA)
NVIDIA (NVDA) is widely viewed as an infrastructure stock because it supplies the core computing layer that makes large scale AI possible inside modern data centres. It is also a mega cap company, with a market capitalisation around 4.514 trillion USD, which reflects how central its technology has become in the global AI buildout.
Its AI exposure is strongest in its data centre compute platform, led by GPUs and the CUDA software ecosystem that developers and enterprises use to build, train, and run AI models. NVIDIA earns revenue primarily from semiconductor hardware, while also extending its platform through software and networking that support full AI cluster deployment. A key metric investors track is data centre revenue growth, since NVIDIA has reported record data centre revenue in recent quarters as AI infrastructure demand scaled. The main risks to watch are export limits that can restrict sales into certain markets, and hyperscaler spend cycles, where a small number of very large cloud buyers can accelerate or pause infrastructure spending depending on their budgets and internal chip strategies.
BROADCOM (AVGO)
BROADCOM (AVGO) is an infrastructure AI stock because it sits in the data center plumbing that enables AI at scale. It is firmly in the mega cap tier, with a market cap of about 1.576T USD, which reflects its role as a key supplier to the largest cloud and enterprise buildouts.
BROADCOM’s AI exposure comes from AI networking and custom silicon used in AI clusters, where fast data movement and purpose-built accelerators matter as much as raw compute. It generates revenue through semiconductor sales plus software, giving it leverage to both AI hardware demand and longer cycle software cash flows. A practical metric investors and traders watch is the AI semiconductor revenue trend, which Broadcom has highlighted directly in its results, including AI semiconductor revenue growth in recent reporting. The main risks are customer concentration because hyperscaler demand can be a large driver, and capex timing, because AI infrastructure orders can speed up or slow down depending on cloud spending cycles.
ADVANCED MICRO DEVICES (AMD)
ADVANCED MICRO DEVICES (AMD) is categorized as an infrastructure AI stock because it supplies the core compute components used in data centers to build and run AI workloads. It sits in the large cap tier, with a market capitalization of about 339.84B USD, reflecting its scale as a major semiconductor provider to cloud and enterprise customers.
AMD’s AI exposure comes from AI compute, led by server CPUs (EPYC) and AI GPUs (Instinct) that are used for training and inference in data centers. Its revenue model is primarily from semiconductor sales, which gives it direct leverage to AI infrastructure buildouts. A key metric investors watch is data center segment growth, since AMD reported its data center segment revenue reached a record 5.4B USD in the quarter, up 39% year over year, driven by EPYC demand and continued Instinct GPU shipment ramp. The main risks are GPU competition in an intensely contested market and platform execution, especially the need to keep improving full stack adoption around its AI accelerators and systems so demand scales beyond a small set of very large buyers.
Software AI Stocks
Software AI stocks deliver AI through cloud platforms and enterprise apps, embedding AI features that help businesses analyse data, automate work, and improve customer support. Investors often favour this category because revenue is typically recurring through subscriptions or usage-based pricing, and strong AI features can lift retention and expansion.
MICROSOFT (MSFT)
MICROSOFT (MSFT) is a software AI stock because it delivers AI through platforms businesses already use at scale, rather than relying on chip sales or hardware cycles. It sits in the mega cap tier, with a market capitalization around 2.981T USD, which reflects its position as one of the largest enterprise software and cloud platforms in the world.
MICROSOFT’s AI exposure is anchored in enterprise distribution via Azure AI and Copilot, meaning it can push AI into cloud workloads and everyday productivity tools inside existing customer environments. Its revenue model is primarily subscriptions plus cloud usage, so AI monetization can show up through Microsoft 365 adoption and higher Azure consumption as customers scale AI workloads. A key metric investors watch is the Azure growth rate, which MICROSOFT reported at 39% year over year for Azure and other cloud services in its FY26 Q2 update. The main risks are high AI capex, since MICROSOFT has been increasing capital spending to support AI infrastructure, plus regulation and competition as cloud and AI markets remain heavily contested and scrutinized.
SALESFORCE (CRM)
SALESFORCE (CRM) is categorized as a software stock because its core business is delivering enterprise applications through the cloud, with CRM products that sit directly inside sales, service, and marketing workflows. It is considered a large cap company, with a market capitalization of about 182.16B USD, reflecting its scale as one of the largest global enterprise software platforms.
SALESFORCE’s AI exposure comes from AI agents embedded in CRM and customer workflows, where automation can sit on top of customer data and daily enterprise processes. Its revenue model is subscription SaaS, with subscription and support revenue forming the core of reported results. The key metrics investors watch are RPO and subscription growth, since current remaining performance obligation and remaining performance obligation are often used as forward demand indicators, alongside subscription revenue growth. The main risks are monetisation pace and competition, because AI driven products need to convert into durable recurring revenue while competing against other enterprise platforms pushing similar automation and agent capabilities.
SERVICENOW (NOW)
SERVICENOW (NOW) is a software stock because it sells a cloud platform that enterprises use to run and automate critical workflows across IT and operations. It sits in the large cap tier, with a market cap of about 106.29B USD, which reflects its scale as a global enterprise software standard for workflow management.
SERVICENOW’s AI exposure is centered on workflow automation across IT and operations, where AI features can help teams resolve issues faster, route work automatically, and standardise processes across departments. Its revenue model is subscription SaaS, so performance is closely tied to recurring subscription growth and renewals. A key metric investors watch is subscription revenue growth, which SERVICENOW reported at 3,299 million USD in Q3 2025, up 21.5% year over year. The main risk is enterprise deal cycles, because large software contracts can be delayed or resized when budgets tighten, and timing can impact near term growth even if long term demand remains intact.
Energy AI Stocks
Energy AI stocks use AI to optimize how energy is produced and managed across power grids, oil and gas operations, and home energy systems. Their focus is on real world improvements such as demand forecasting, supply balancing, storage optimisation, and predictive maintenance. Investors and traders watch this category because energy infrastructure is in a long upgrade cycle and software driven optimisation can improve efficiency while adding recurring revenue.
GE VERNOVA (GEV)
GE VERNOVA (GEV) sits in the energy sector and is often grouped with energy focused AI stocks because it helps utilities and power operators modernise how electricity is generated, transmitted, and managed. It is a large cap company with a market cap of about 211.45B USD, giving it the scale and customer access that many smaller grid software players do not have.
GE VERNOVA’s AI exposure is strongest in grid software and optimization for power networks, led by its GridOS portfolio that is built for grid orchestration and utility operations. Its revenue model combines equipment plus software and services, so investors typically watch orders and backlog as a forward indicator of demand visibility. The company highlighted an expanded backlog of 150B USD, which supports the case that grid and power investment cycles are extending into 2026 in its 2025 results. The main risks are slow utility procurement and project timing, since large grid upgrades can move slowly through budgets, approvals, and deployment schedules even when long term demand remains strong.
SLB (SLB)
SLB (SLB) is an energy sector stock that investors often include in energy focused AI lists because it operates at the intersection of oilfield services and digital transformation. It is a large cap company with a market cap of about 75.81B USD, giving it the scale and customer footprint to deploy AI driven tools across major energy operators globally.
SLB’s AI exposure comes through its data and AI platform for energy operations, including the Lumi data and AI platform that is designed to turn energy data into usable insights across workflows. Its revenue model is services plus digital platforms, and the company now reports digital performance in a way investors can track, with its latest full year update highlighting digital revenue and its momentum. A practical metric to watch is the digital mix and margin trend, since rising digital contribution can support stronger margins and improve resilience through the cycle. The main risks are energy cycle sensitivity because customer budgets still follow commodity and capex cycles, and adoption pace because scaling AI from pilots to broad production deployments can take time in operational environments.
ENPHASE ENERGY (ENPH)
ENPHASE ENERGY (ENPH) is a stock categorized under the energy sector that investors often include in the energy focused AI lists because it sits at the intersection of residential solar, storage, and software driven energy management. It is a mid cap company with a market cap of about 6.51B USD, which makes it smaller and typically more volatile than mega cap infrastructure and software names, but also more directly tied to consumer and distributed energy trends.
ENPHASE’s AI exposure is best described as a home energy management and optimisation layer, led by IQ Energy Management, which uses AI to forecast production, consumption, and energy rates to optimise how an ENPHASE ENERGY system runs. Its revenue model is hardware plus software and services, with hardware shipments remaining central while software and services support the ecosystem and attach opportunities. The key metrics investors tend to watch are shipments and margin trends because they show real demand and pricing power as ENPHASE has disclosed shipment volumes and non-GAAP gross margin in its latest quarterly reporting. The main risks are the solar demand cycle and channel volatility, where changes in installer activity, financing conditions, and inventory levels can quickly affect shipments and profitability even if the long term energy transition remains intact.
Penny AI Stocks
Penny AI stocks are smaller AI companies that often trade under 5 USD per share. They can deliver outsized upside if the business gains traction, but risks are higher due to limited cash, volatile revenue, and frequent fundraising. Investors typically watch cash runway, dilution risk, and whether AI is truly driving revenue rather than marketing.
VERITONE (VERI)
VERITONE (VERI) falls under the penny category because it is a smaller public company whose stock trades at a lower price level and carries the higher volatility that typically comes with that segment. It is also a small cap company, with a market cap of about 0.30B USD, which is why it is often discussed as a higher risk, higher potential AI stock compared with large cap software leaders.
VERITONE’s AI exposure is best described as an enterprise AI workflow platform for audio and video, built around its aiWARE platform that helps organisations analyse and operationalise unstructured media data at scale. Its revenue model is software plus services, which gives it both platform upside and project-driven volatility. Two practical metrics that investors tend to watch are gross margin and cash runway, since penny stocks can swing quickly based on operating leverage and liquidity, and VERITONE reports cash and cash equivalents in its financial disclosures. The main risks are dilution risk and the profitability timeline because smaller AI firms may need additional capital to fund growth before they reach consistent profitability, which can impact shareholders even if the product story improves.
REKOR SYSTEM (REKR)
REKOR SYSTEM (REKR) is a penny AI stock because it trades in the lower priced, higher volatility end of the market. It is also a micro cap company as its market capitalization is about 0.12B USD, which is why it is typically viewed as a higher risk, higher potential AI stock compared with large cap AI infrastructure and software leaders.
REKOR’s AI exposure is best described as roadway intelligence and mobility analytics, using artificial intelligence to collect and organize mobility data and turn it into actionable insights for transportation and public safety use cases. Its revenue model combines software plus deployments and services, with management emphasizing a shift toward more recurring software and data delivered as a service alongside implementations. The most practical metrics to track for investors are contract wins and cash burn, because contract timing can drive quarter to quarter volatility while cash usage determines how long the company can fund growth without raising capital. The main risks are contract lumpiness and funding risk, since public sector style procurement can be uneven and micro cap companies may need external financing if cash burn does not improve fast enough.
FISCALNOTE (NOTE)
FISCALNOTE (NOTE) is categorized as a penny AI stock and sits in the nano cap tier, with a market cap of about 18.23M USD. This small public company is often grouped into penny AI lists because its valuation is more sensitive to quarterly execution, balance sheet updates, and contract momentum than larger software platforms.
FISCALNOTE’s AI exposure comes from policy and regulatory intelligence with AI summaries inside its PolicyNote platform, which includes AI assisted capabilities to help users interpret and act on legislative and regulatory information. Its revenue model is subscription SaaS, with subscription revenue representing the large majority of total revenue in its latest reported quarter. The key metrics to watch are ARR and retention, since the company reports Annual Recurring Revenue and Net Revenue Retention, with Q3 2025 ARR at 84.8M and NRR at 98%. The main risks are balance sheet risk and growth execution, because a nano cap profile can magnify the impact of financing decisions and the business needs to sustain ARR stability and retention while reaccelerating growth.
How Do I Choose Which AI Stocks To Buy?
Choosing which AI stocks to buy starts with understanding where each company sits in the AI value chain and what actually drives its revenue. Some businesses benefit from data centre buildouts, others monetise AI through recurring software subscriptions, and smaller names rely on contract wins and funding. A practical approach is to compare business models, track a few key metrics consistently, and focus on companies where AI demand shows clearly in financial results rather than market hype.
After identifying suitable AI stocks, the next step is deciding how you want to trade them. The 4 common trading styles include Conservative Traders, Momentum and Trend Traders, Swing Traders, and Aggressive Traders and High Risk Seekers.
Conservative Traders
Conservative traders focus on preserving capital by prioritising high liquidity, lower relative volatility, and diversified businesses that can hold up better across market cycles. MICROSOFT (MSFT) and BROADCOM (AVGO) are typically the most suitable fits. These 2 AI stocks are suitable for this trading style because their diversified, established businesses spread demand across multiple revenue streams, helping reduce reliance on any single AI cycle and smooth performance when market conditions shift.
Momentum And Trend Traders
Momentum and trend traders focus on capturing strong price moves by following stocks with clear direction, a strong narrative, and sustained market participation from buyers. NVIDIA (NVDA), SERVICENOW (NOW), and GE VERNOVA (GEV) are typically the most suitable fits. These 3 AI stocks are suitable for this trading style because they are highly headline sensitive and often sit at the center of major themes investors rotate into, which can trigger sustained moves and cleaner trend behaviour as attention and capital concentrate into the market leaders.
Swing Traders
Swing traders focus on capturing medium term price moves over days to weeks by trading around catalysts such as earnings and guidance updates. ADVANCED MICRO DEVICES (AMD), SALESFORCE (CRM), SLB (SLB), and ENPHASE ENERGY (ENPH) are typically the most suitable fits. These 4 AI stocks are suitable for this trading style because they often trade in wider ranges, react strongly to earnings, and see frequent catalysts that cause investors to reprice growth expectations, margins, and demand outlooks, creating repeatable swing setups as sentiment shifts.
Aggressive Traders And High Risk Seekers
Aggressive traders and high risk seekers focus on speculative opportunities with the potential for outsized moves, while accepting higher volatility and high drawdown risk. VERITONE (VERI), REKOR SYSTEMS (REKR), and FISCALNOTE (NOTE) are typically the most suitable fits. These 3 AI stocks are suitable for this trading style because smaller penny companies can swing sharply on catalysts such as contract wins, earnings surprises, funding announcements, and listing or dilution updates, creating explosive upside potential but also significant downside if liquidity tightens or execution disappoints.
How Do I Buy AI Stocks?
You can buy AI stocks through three common methods, depending on whether you want ownership, diversification, or trading flexibility. The three common methods are trading shares CFDs, owning Stocks, and buying ETFs.
Trading Shares CFDs
Trading shares CFDs can give you exposure to AI stocks by letting you trade the price movement of an underlying share without owning the share itself. That means to take a long position if you expect an AI stock to rise, or a short position if you expect it to fall. You can also control a larger position with a smaller initial deposit, which can increase potential returns but also increases potential losses because shares CFDs are also margined products. Traders should also factor in costs such as spreads and overnight financing for positions held beyond the trading day, and use risk controls like stop loss and position sizing because shares CFDs are traded rather than held as ownership.
You can trade AI related stock CFDs, manage exposure with built-in order types and risk tools, and practise using a demo account on TMGM’s platforms. It is important to understand leverage and financing costs before trading CFDs live, as they are complex and high risk.
Owning Stocks
Owning stocks gives you exposure to AI by letting you buy shares in companies that build or benefit from AI, such as chipmakers, cloud platforms, or enterprise software firms. The share price rises when the company grows revenue, expands margins, or gains market share. Owning stocks also means your returns are tied to company fundamentals and broader market sentiment, so it helps to compare business models, track key metrics like revenue growth and profitability, and manage risk through diversification rather than relying on a single AI stock.
Buying ETFs
Buying ETFs provide exposure to AI stocks by bundling multiple AI related companies into one tradable instrument. You gain diversified coverage across parts of the AI ecosystem, such as infrastructure, software, and sometimes robotics or data platforms, depending on the fund’s mandate, instead of picking individual AI companies. This can reduce single stock risk and smooth volatility, but it also means performance is tied to the overall basket and the ETF’s methodology, including fees and how concentrated the top holdings are.
