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7 Artificial Intelligence Stocks Worth Watching Right Now (February 8, 2026)

ByInnoVirtuoso

If you feel like every AI headline spawns three more, you’re not alone. But amid the hype, one data point cuts through the noise: where the money is moving. According to a recent screen by MarketBeat, seven AI-focused companies drew the highest dollar trading volume among AI names over the last several days—an objective signal that big market participants are paying attention.

Below, we unpack what that momentum could be signaling, what each company actually does in the AI stack, the catalysts and caveats to watch, and how to think about building an AI-focused watchlist in 2026 without getting swept up in the next hype cycle.

Note: This article is for informational and educational purposes only and is not financial advice. Always do your own research.

Why these seven stood out this week

  • High dollar trading volume often reflects a tug-of-war between large buyers and sellers. Whether the net move is up or down, it usually corresponds to a period of price discovery.
  • In AI, elevated volume can be sparked by contracts, product updates, regulatory news, broader sector rotation, or even a ripple effect from megacap AI leaders.
  • The seven MarketBeat flagged span very different use cases—from healthcare to energy to lending to voice interfaces. That diversity underscores a crucial point: “AI” is not a single industry; it’s a capability being woven into dozens.

Here are the seven MarketBeat highlighted: – Tempus AI: precision medicine, healthcare data and clinical AI – Hut 8: digital asset mining, managed services, and high-performance computing colocation – SoundHound AI: independent voice AI platform across auto, TV, IoT, and customer service – BigBear.ai: decision intelligence and analytics for commercial and government customers – Fluence Energy: energy storage systems with AI-driven optimization software – OneStream: corporate performance management with AI-enhanced planning and forecasting – Upstart: AI-driven lending and credit decisioning

Let’s break down what to know about each.

Company deep dives: what to watch, why it matters

Tempus AI: Turning clinical and genomic data into decisions

Tempus AI sits at the intersection of healthcare, data, and machine learning. Its mission is to help clinicians and researchers make more informed decisions using massive multi-modal datasets—think genomic sequencing, pathology, imaging, and electronic health records.

  • What it does: Builds AI models and decision-support tools for oncology and other disease areas, offers laboratory testing and companion diagnostics, and supports biopharma with trial matching and real-world evidence.
  • Why the market cares: Healthcare is a high-stakes AI proving ground. If AI can reduce time-to-diagnosis, improve treatment matching, and accelerate drug development, the value creation is enormous.
  • Potential moat: Proprietary, de-identified healthcare data flywheels; clinical workflows embedded with provider networks; regulatory and lab accreditations that are hard to replicate.

Key things to watch: – Growth in clinical test volumes and provider partnerships – Biopharma contracts and trial-matching adoption – Regulatory clarity on data usage and reimbursement

Potential pitfalls: – Data governance and privacy scrutiny – Reimbursement risk for diagnostics – Competition from hospital systems and tech incumbents building in-house AI

Learn more: Tempus

Hut 8: Bridging digital asset mining and AI/HPC infrastructure

Hut 8 made its name in Bitcoin mining, but its strategy also includes managed services and high-performance computing (HPC) colocation—capabilities relevant to AI compute demand.

  • What it does: Operates digital asset mining facilities; runs managed services and colocation centers that can support compute-intensive workloads; explores power/real estate strategies to monetize infrastructure.
  • Why the market cares: Two secular narratives converge here—crypto cycles and AI infrastructure demand. If Hut 8 can flex facilities toward AI/HPC workloads where economics warrant, it diversifies its revenue.
  • Potential moat: Access to competitively priced power, existing data center footprints, and experience operating at scale.

Key things to watch: – Energy contracts, uptime, and cost per MWh – Mix shift between pure mining versus colocation/HPC revenue – Balance sheet flexibility through crypto price cycles

Potential pitfalls: – Bitcoin price volatility and halving cycles – Regulatory overhang for crypto operations – Capex intensity and execution risk entering new workload markets

Learn more: Hut 8

SoundHound AI: An independent voice stack in a world of assistants

SoundHound AI builds an end-to-end voice AI platform (wake words, ASR, NLU, TTS) focused on speed, accuracy, and brand ownership for clients who don’t want to hand customer interactions to Big Tech assistants.

  • What it does: Provides embedded and cloud voice AI for automotive, smart devices, restaurants, call centers, and media. Offers custom integrations, wake words, and monetization options.
  • Why the market cares: As cars, TVs, and appliances “talk,” brands want control over the experience and the data—not just a generic assistant. Independent providers can be more flexible on models, privacy, and on-device inference.
  • Potential moat: A unified stack optimized for latency, embedded/offline modes, and domain-specific intents built over years.

Key things to watch: – Auto OEM design wins and production launches – Recurring revenue growth from usage-based models – Expansion into call centers and quick-service restaurants

Potential pitfalls: – Competitive pressure from platform giants bundling assistants – Customer concentration risk with large OEMs – Long sales cycles tied to hardware refreshes

Learn more: SoundHound Investor Relations

BigBear.ai: Decision intelligence for complex operations

BigBear.ai focuses on turning messy, high-velocity data into actionable decisions for customers in defense, logistics, and industrial markets.

  • What it does: Provides AI/ML analytics, forecasting, and mission planning software and services. Helps organizations optimize supply chains, detect anomalies, and simulate outcomes.
  • Why the market cares: Governments and enterprises are accelerating spend on AI that tangibly improves readiness, safety, and cost efficiency. Decision intelligence sits close to mission-critical workflows.
  • Potential moat: Deep domain expertise in defense/critical infrastructure, integration with existing systems, and accreditations that support sensitive deployments.

Key things to watch: – New contract awards, renewals, and funded backlog – Margin profile on software vs. services mix – U.S. and allied defense AI budget trends

Potential pitfalls: – Timing lumpiness of government contracts – Talent competition in cleared AI/ML roles – Dilution risk if capital is raised to fund growth

Learn more: BigBear.ai Investor Relations

Fluence Energy: Where batteries meet AI optimization

Fluence Energy is best known for utility-scale energy storage systems. Its AI relevance comes from software that forecasts, dispatches, and trades energy to squeeze more value out of every megawatt-hour.

  • What it does: Designs, deploys, and services battery storage systems; offers optimization and trading software (e.g., day-ahead and real-time market participation, grid services, and degradation-aware dispatch).
  • Why the market cares: As renewables scale, storage plus AI-enabled optimization becomes the linchpin to grid reliability and economics. Software attach rates and recurring revenue are key.
  • Potential moat: Installed base, utility relationships, market integrations, and domain models trained on real operational data.

Key things to watch: – Growth in software subscriptions and attach rate to new systems – Supply chain stability and gross margin trajectory – Policy tailwinds from clean energy incentives and market reforms

Potential pitfalls: – Commodity and component cost volatility (cells, inverters) – Project execution risk on large deployments – Grid policy changes that affect ancillary revenue streams

Learn more: Fluence Energy | Fluence IR

OneStream: AI-enhanced corporate performance management

OneStream provides finance teams with a unified platform for planning, consolidation, reporting, and analytics—now increasingly supercharged by AI for forecasting and scenario modeling.

  • What it does: Replaces fragmented spreadsheets and point tools with a single CPM platform; layers in predictive analytics and ML-driven insights to improve planning accuracy and agility.
  • Why the market cares: CFO suites are prioritizing tools that turn financial data into decisions faster. AI-native forecasting and driver-based planning translate directly to better capital allocation.
  • Potential moat: Deep finance workflows, extensibility (“marketplace” solutions), and high switching costs once embedded across FP&A and accounting processes.

Key things to watch: – Net revenue retention and enterprise win rates – Adoption of AI-driven planning modules – Competitive dynamics with legacy suites and cloud-native peers

Potential pitfalls: – Long enterprise sales cycles in tight IT budgets – Need for quantifiable ROI to justify platform consolidations – Competition from hyperscaler-native analytics stacks

Learn more: OneStream

Upstart: AI underwriting through credit cycles

Upstart applies AI to credit underwriting, aiming to price risk more precisely than traditional FICO-driven models and to expand access to affordable credit via partner banks and credit unions.

  • What it does: Provides lending models and a marketplace connecting banks with consumers across personal, auto, and other loan types; some loans are held or securitized by partners.
  • Why the market cares: If models can maintain or improve loss-adjusted returns versus benchmarks across cycles, Upstart’s approach could shift industry paradigms in credit decisioning.
  • Potential moat: Proprietary borrower data, model features, and continuous-learning infrastructure; distribution via bank partners.

Key things to watch: – Conversion rates, approval rates, and contribution margins – Credit performance of recent vintages versus targets – Funding stability from bank partners and capital markets

Potential pitfalls: – Sensitivity to interest rates and macro credit conditions – Regulatory scrutiny of AI-driven underwriting and fairness – Potential model drift if data shifts rapidly

Learn more: Upstart IR

What high dollar trading volume can (and can’t) tell you

  • Does signal: Attention from institutional players; improved liquidity; potential inflection points around news or expectations.
  • Doesn’t guarantee: Direction. High volume happens on breakouts and breakdowns alike.
  • How to use it: Pair volume trends with fundamentals (contract wins, product launches, profitability path), and with technical context (support/resistance, trend strength).

If you’re tracking AI as a theme rather than a single name, elevated sector-wide volume can also indicate a macro rotation toward (or away from) growth and innovation risk.

A practical framework for evaluating AI-exposed stocks

AI is horizontal. To compare apples to apples, standardize your checklist:

  • Problem-solution fit
  • Is the AI solving a mission-critical, high-value problem or a “nice-to-have”?
  • Are there measurable ROI metrics (cost out, revenue lift, accuracy gains)?
  • Data advantage
  • Proprietary, hard-to-replicate datasets?
  • Feedback loops that improve the model with scale?
  • Model and product differentiation
  • Is there defensible IP in models, inference optimizations, or embedded workflows?
  • How easily could a competitor replicate the offering with off-the-shelf models?
  • Go-to-market and integration
  • Direct vs. channel? OEM vs. enterprise?
  • Time-to-value and switching costs once deployed?
  • Unit economics and runway
  • Gross margins trending up?
  • Path to positive free cash flow without perpetual dilution?
  • Regulatory and concentration risk
  • Exposure to sensitive data, explainability requirements, or credit/healthcare rules?
  • Heavy reliance on one or two customers or partners?
  • Valuation sanity check
  • Growth versus cash burn; recurring revenue mix; rule-of-40 style benchmarks.
  • Scenario-test outcomes under conservative, base, and optimistic assumptions.

Key risks in AI names to keep on your radar

MarketBeat rightly notes several recurring risks across AI stocks:

  • Elevated valuations: Momentum can detach from fundamentals, amplifying drawdowns if growth slows.
  • Rapid technological change: Model architectures and platform dynamics evolve quickly. What’s cutting-edge today may be commoditized tomorrow.
  • Regulatory overhang: Data privacy, model transparency, discrimination and bias concerns, and industry-specific rules (healthcare, finance) can shape product scope and cost of compliance.
  • Concentration risk: Revenue tied to a handful of customers, government contracts, or a single platform partner can create step-function volatility.
  • Macro sensitivity: Rate regimes, credit cycles, and energy prices can significantly affect business models (particularly lenders, miners, and capital-intensive deployers).

Approach each name with a clear thesis, a checklist for disconfirming evidence, and position sizes that respect these risks.

How to build a smarter AI-stock watchlist in 2026

  • Anchor on catalysts: Identify 2–3 near-term, testable catalysts per name (e.g., OEM launch, contract award, margin inflection).
  • Track leading indicators: For software, watch net retention and backlog. For infrastructure, monitor capacity additions and utilization. For fintech/lenders, track loss curves and funding stability.
  • Separate story from numbers: Narrative drives attention; numbers drive durability. Focus on revenue mix quality, margin trends, and cash conversion.
  • Diversify across the AI stack: Blend application-layer plays (voice AI, decision intelligence) with infrastructure and enablement (storage optimization, HPC colocation) to reduce single-point risk.
  • Use disciplined entry/exit rules: Define what would upgrade or break your thesis and how you’ll react before emotions kick in.

Where to read the original screen

For the full screen and context that surfaced these seven names, see the original post on MarketBeat.

Quick links to company resources

FAQ: AI stocks and this week’s watchlist

  • What is “dollar trading volume,” and why does it matter?
  • Dollar trading volume is the total value of shares traded (price times shares). High dollar volume typically means greater liquidity and heightened institutional interest, which can precede notable price moves—but not necessarily in one direction.
  • Are these seven stocks a buy right now?
  • Not necessarily. High volume is a starting signal, not a conclusion. Evaluate each company’s catalysts, risks, and valuation versus your goals and risk tolerance.
  • How do I avoid chasing hype in AI?
  • Require measurable ROI, watch recurring revenue and margins, and prefer companies with sticky integrations or proprietary data. Size positions conservatively when narratives outrun numbers.
  • Which business models in AI are more resilient?
  • Models embedded in mission-critical workflows (finance planning, grid operations, defense analytics) and with recurring revenue and high switching costs tend to be more resilient than ad hoc tools.
  • What’s the biggest risk for AI in healthcare and lending?
  • Beyond economics, governance. Data privacy, explainability, and fairness are heavily scrutinized. Clear frameworks and regulatory compliance are essential for sustained adoption.
  • How do macro conditions affect these names?
  • Higher rates can pressure growth valuations and affect credit performance (Upstart). Energy prices and policy affect storage economics (Fluence) and mining margins (Hut 8). Government spending cycles impact defense/analytics contracts (BigBear.ai).
  • Should I diversify within AI?
  • Yes. Consider exposure across application software, infrastructure/compute, and sector-specific platforms (healthcare, energy). Diversification can reduce the impact of idiosyncratic setbacks.
  • What metrics should I monitor quarter to quarter?
  • Backlog and net retention (software), attach rates for higher-margin modules (optimization software), utilization and power costs (HPC/mining), credit loss curves and contribution margin (lending), and cash runway/free cash flow for all.

The bottom line

AI remains a horizontal force reshaping multiple industries, and the market’s recent vote—via high dollar trading volume—puts a spotlight on seven very different ways investors are trying to capture that value. Tempus AI, Hut 8, SoundHound AI, BigBear.ai, Fluence Energy, OneStream, and Upstart each monetize AI in distinct ways, with distinct playbooks and risk profiles.

If you’re tracking AI stocks in February 2026, use this week’s surge in attention as your cue to sharpen the watchlist—not to suspend discipline. Focus on tangible catalysts, durable moats, and improving unit economics. Let price and volume tell you when the market’s paying attention; let fundamentals tell you whether that attention is deserved.

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InnoVirtuoso

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