As companies pour billions into artificial intelligence, attention is shifting from experimentation to execution. By 2026, analysts expect clearer winners to emerge — not just among consumer-facing brands, but across the infrastructure, data, and security systems that quietly power the AI economy.
Wedbush Securities analyst Dan Ives has outlined several technology and AI stock themes that could shape market performance over the next two years. His outlook focuses less on short-term market swings and more on how large firms are positioning themselves for sustained growth as AI moves deeper into everyday operations.
Below is a closer look at five predictions shaping expectations for 2026, and why they matter beyond stock prices.
Why 2026 Is Becoming a Key Benchmark for the Tech Sector
The next two years are expected to mark a shift from heavy AI investment toward measurable returns. Many companies are now building the operational backbone needed to support large-scale deployment — including data pipelines, monitoring tools, and reporting systems that help translate innovation into usable products.
For investors, researchers, and business leaders, 2026 serves as a checkpoint: a moment to assess which strategies have moved beyond prototypes and into repeatable, scalable models.
1. Tesla’s Robotaxi Expansion Could Reach Dozens of Cities
Summary: Tesla is expected to broaden its autonomous ride-hailing rollout, potentially operating Robotaxis in more than 30 cities by 2026.
Dan Ives continues to rank Tesla among his strongest long-term convictions. His forecast centers on the company’s ambition to turn self-driving technology into a large-scale commercial service rather than a limited pilot.
If executed, a wider Robotaxi rollout would rely on continuous data collection, real-time system monitoring, and constant software refinement. Each vehicle effectively acts as a moving sensor, feeding driving data back into Tesla’s research and validation pipelines.
Why this matters:
Autonomous transport depends as much on data oversight as on engineering. The ability to track performance, detect edge cases, and refine decision models at scale will determine whether such fleets can operate reliably across different cities and conditions.
2. A Potential Apple–Google AI Partnership Could Redefine Mobile Strategy
Summary: Apple may partner with Google to integrate Gemini-powered features into iPhones, accelerating its AI roadmap.
According to Ives, a formal agreement between the two companies could significantly strengthen Apple’s AI capabilities without forcing it to rebuild foundational models from scratch. Such a move would allow Apple to focus on product integration, privacy controls, and user experience.
The analyst suggests this strategy could help push Apple’s market value toward the $5 trillion mark by 2026.
From a structural standpoint, the partnership would reflect a growing trend in the industry: separating model development from deployment. Large platforms increasingly rely on shared AI infrastructure while maintaining their own analytics, monitoring, and reporting layers.
3. Oracle’s Revival Tied to Data Centers and AI Workloads
Summary: Oracle is gaining renewed attention as demand for AI-ready infrastructure grows.
After a quieter period, Oracle is benefiting from rising demand for high-capacity data centers and long-term enterprise contracts tied to AI workloads. Ives has set a bullish price target of $250, pointing to a growing backlog linked to cloud and data services.
Oracle’s strength lies in its ability to support structured analytics environments, where performance tracking, reporting accuracy, and system reliability are critical. These features make the company a key supplier for organizations running complex research or operational pipelines.
In practical terms, AI adoption is pulling infrastructure providers back into the spotlight — especially those equipped to handle scale, security, and long-term visibility.
4. Nebius Emerges as a Likely Acquisition Target
Summary: Infrastructure firm Nebius is drawing interest from major technology players looking to expand AI capacity quickly.
Ives notes that companies such as Microsoft, Alphabet, or Amazon could view Nebius as a strategic acquisition. The appeal lies in its role as a specialized infrastructure provider supporting compute-intensive workloads.
Rather than building every capability internally, large firms often look to acquire platforms that already manage data routing, monitoring, and system optimization efficiently. For acquirers, this shortens development timelines and strengthens their AI supply chain.
Such deals also highlight how competition in AI is increasingly fought at the infrastructure level, not just through consumer-facing applications.
5. Cybersecurity Poised to Remain a Standout Sector
Summary: Cybersecurity is expected to be one of the strongest-performing areas of tech through 2026.
As AI systems expand, so does the need to protect them. Wedbush continues to favor companies such as CrowdStrike and Palo Alto Networks, citing their leadership in threat detection and enterprise security.
Modern cybersecurity platforms depend on continuous data ingestion, behavioral analytics, and real-time reporting. These systems monitor vast activity streams to identify anomalies and respond before damage spreads.
For organizations adopting AI at scale, security is no longer a separate layer — it is embedded into monitoring, compliance, and operational oversight.
A Common Thread: Data, Monitoring, and Accountability
Across all five predictions, one pattern stands out. Whether the focus is autonomous vehicles, cloud infrastructure, mobile platforms, or cybersecurity, progress depends on strong systems for:
- Tracking research and development workflows
- Monitoring real-time performance
- Managing analytics pipelines
- Producing transparent, auditable reports
These foundations determine which companies can move from experimentation to reliable execution.
Frequently Asked Questions
What does tracking a research pipeline involve?
It refers to following how data, experiments, and models move from development through testing and deployment, including performance review and updates.
Why are monitoring systems essential for AI-driven companies?
They help detect errors, measure outcomes, and ensure systems behave as expected once deployed.
How do analytics workflows support decision-making?
They organize data collection, processing, and evaluation so insights can be reviewed and acted upon consistently.
What role do reporting frameworks play in transparency?
They document system performance, risks, and outcomes, helping stakeholders understand how decisions are made.
Why is cybersecurity closely tied to AI growth?
AI expands digital infrastructure, increasing exposure to threats. Security platforms use analytics and monitoring to manage that risk.
Why are infrastructure providers gaining attention in 2026 forecasts?
Because scalable compute, storage, and monitoring systems are essential for running advanced AI reliably at scale.
Conclusion: What These 2026 Forecasts Signal
The outlook for 2026 suggests a more disciplined phase of AI expansion. Instead of bold promises alone, markets are beginning to reward companies that can demonstrate execution — supported by data visibility, monitoring tools, and dependable infrastructure.
From Tesla’s autonomous ambitions to Apple’s potential AI partnerships, from Oracle’s infrastructure rebound to the steady rise of cybersecurity leaders, the message is consistent: long-term value will come from systems that can be measured, managed, and trusted.
For investors, researchers, and business leaders alike, understanding how these foundations work is becoming just as important as tracking the technologies themselves.