Fund Manager Names Two ASX 200 Tech Stocks Poised to Survive and Thrive Amid AI

Is AI Really the End of All Software Companies?

The answer is no. AI is indeed a powerful disruptive force, but it is more akin to an elimination contest than a massacre. It eliminates “tool-type” software whose value is built solely on a single function, lacking data accumulation and ecosystem protection. Conversely, platform-based enterprises that have established complex workflow integrations, possess proprietary data networks, and can internalize AI as a deep part of their service will see their moats actually deepen because of AI. The essence of this transformation is elevating competition from “feature comparison” to the level of “ecosystem intelligence.”

Market panic stems from an oversimplified understanding of AI capabilities. The belief that an AI capable of writing code and analyzing data can easily replicate Xero’s accounting platform or Altium’s design environment overlooks the true core value of enterprise software: reducing overall complexity. A small or medium-sized enterprise using Xero is not just for automated bookkeeping but also for connecting bank feeds, managing invoices, handling tax filings, and collaborating with advisors. This is a dynamic system involving multiple parties and governed by strict regulations. Similarly, engineers using Altium Designer are not just drawing circuit diagrams but also managing component libraries, conducting team design reviews, generating production files, and ensuring designs meet manufacturing standards.

AI’s role here is not to replace the platform but to become a more powerful “automation engine” within it. It enables the platform to handle more trivial, repetitive tasks, thereby allowing platform resources and user attention to focus more on high-value strategic decisions and innovation. Therefore, the key metrics for evaluating whether a tech company can survive in the AI era have changed:

According to a Goldman Sachs research report from 2025, in the enterprise software sector, it is projected that by 2027, over 35% of new software features will be driven or assisted by generative AI, but nearly 80% of these features will be provided by existing platform leaders, not entirely new startups. This illustrates the power of integration advantage. Another data point from IDC shows that in the Asia-Pacific region (excluding Japan), over 60% of enterprises prioritize embedding AI solutions into their existing, already-purchased enterprise application platforms when choosing AI solutions, to reduce integration risks and learning costs.

mindmap
  root(Key to Tech Stock Survival in the AI Era)
    (Platform Ecosystem Strength)
      Deep Workflow Integration
      High Switching Costs
      Cross-Role Collaboration Network
    (Data Asset Quality)
      Proprietary Domain Data
      Real-Time Data Closed Loop
      Compliance Data Labels
    (AI Internalization Capability)
      Modular Feature Embedding
      Enhancing Existing Service Efficiency
      Creating New Revenue Models
    (Market Positioning)
      Solving Highly Complex Problems
      Operating Within Regulatory Frameworks
      Serving Professional Domain Users

Xero: How to Turn AI into the Ultimate Co-pilot for Accountants and SMEs?

Xero’s strategy is to make AI an omnipresent “compliance and insight engine.” It does not compete with ChatGPT on generating reports but deeply weaves AI into every step from transaction categorization and cash flow forecasting to tax preparation, upgrading itself from a System of Record to a System of Decision.

Over the past six years, Xero has continuously invested in machine learning and data analytics. Its platform has processed over hundreds of billions of transaction data points. This gives its AI models unparalleled domain advantages in understanding the financial patterns of SMEs. For example, its “Cash Flow Forecast” feature is not just a simple extrapolation of historical trends but can incorporate seasonal factors, industry dynamics, and even the enterprise’s past payment behavior patterns to provide highly contextualized warnings and suggestions. For users, this means less manual adjustment, earlier detection of financial risks, and more ample decision-making time.

More crucially, Xero uses AI to strengthen the stickiness of its ecosystem. It connects banks, payment gateways, payroll service providers, and thousands of third-party applications. AI plays the role of an intelligent router within this ecosystem, capable of automatically suggesting optimal payment methods, identifying potential cost savings, and even predicting when to seek professional accountant consultation. This creates a virtuous cycle: more users bring more data, better AI models lead to superior user experience and higher retention rates, which in turn attract more ecosystem partners to join.

According to Xero’s latest data from the 2025 fiscal year, its active users utilizing AI-driven features (such as auto-coding, smart reconciliation) have a renewal rate over 15 percentage points higher than the industry average. Furthermore, these users on average use 1.7 additional add-on services on the Xero platform. This directly proves the effectiveness of AI as a growth lever.

timeline
    title Xero's AI Evolution and Platform Deepening Path
    section 2018-2020 : Data Infrastructure
        Machine Learning for Transaction Auto-Categorization
        Launch of Basic Cash Flow Preview
    section 2021-2023 : Ecosystem Intelligence
        Deep Integration of Bank Real-Time Data Feeds
        AI-Driven Business Insight Reports Go Live
        Launch of Accountant Collaboration AI Tools
    section 2024-2026 : Decision as a Service
        Contextualized Cash Flow Forecasting and Alerts
        Automated Tax Compliance Checks and Preparation
        AI-Assisted Business Credit Assessment Interface

Altium: Building an AI-Insurmountable Wall at the Source of Hardware Design?

Altium’s moat lies in locking the design process, component data, and team collaboration entirely within a cloud-native platform—Altium 365. AI can help optimize the routing of a signal trace or simulate the thermal distribution of a circuit, but it cannot create out of thin air a smart component library containing millions of verified parts with supply chain and compliance data, nor can it replicate a cross-departmental collaborative workflow spanning design, review, procurement, and manufacturing.

Electronic Design Automation (EDA) is an extremely specialized field with near-zero tolerance for error. A minor design flaw can lead to multi-million-dollar chip fabrication failures. Therefore, engineers’ trust in tools is built on decades of technical accumulation, stability, and complete design chain support. Altium Designer and its cloud platform Altium 365 precisely occupy the critical position from Concept to Manufacturing. Its value lies not in the power of a single function but in the smoothness of the process and the consistency of data.

For Altium, AI is an “accelerator” that enhances its platform’s power. For example, its AI-assisted routing feature under development can free engineers from tedious manual labor, allowing them to focus on architectural design and performance optimization. More importantly, all design data on the Altium 365 platform is structured and analyzable, providing excellent fuel for training domain-specific AI models. These models can learn the design patterns of top engineers, suggest optimizations, and even pre-warn of potential electromagnetic interference (EMI) or signal integrity issues.

This “platform + AI” model creates extremely high switching costs. All of a design team’s intellectual property (IP), design history, supplier lists, and team collaboration habits are embedded within Altium 365. Migrating to another tool is not just a software change but a collapse and rebuild of the entire workflow and knowledge management. According to industry surveys, the average cost for a medium-sized electronic design team to migrate EDA tools exceeds $500,000 and requires at least six months of adaptation.

Looking at the Survival Rules for the Tech Industry in the Next Five Years Through Xero and Altium

The cases of Xero and Altium reveal a universal principle that transcends the software industry: in the AI era, the survivors are not the smartest individuals but the most adaptable ecosystems. They demonstrate how to co-opt a potential disruptor (AI) as an enhancing component within the system. The implications for investors and business operators are profound.

First, the valuation of “data assets” must be reassessed. Not all data is valuable. Scattered, unstructured data disconnected from core business processes will see diminishing value. Conversely, standardized transaction data continuously flowing into Xero, or component data with physical and electrical properties constantly growing on Altium’s platform, are “strategic assets” capable of generating compound interest. In the future, financial reports may require a new footnote to explain the scale, quality, and potential for generating AI advantage of a company’s proprietary dataset.

Second, business models must evolve from “license/perpetual sale” or “simple subscription” towards “outcome-oriented value sharing.” When AI can significantly improve a customer’s operational efficiency or revenue, companies can more confidently adopt pricing models tied to customer success. For example, Xero could tier pricing based on the accountant hours saved or cash flow optimized by its AI for the customer. This would more tightly couple revenue growth with customer value creation, forming a healthier business cycle.

Finally, the competitive landscape will shift from “single product line duels” to “ecosystem alliance competitions.” The era of going it alone is over. Xero’s ecosystem includes banks, payment providers, and advisors; Altium’s ecosystem connects semiconductor manufacturers, PCB fabricators, and component distributors. The future winners will be the “platform conductors” that can build and dominate the most prosperous, mutually beneficial industry ecosystems. They set data exchange standards, provide core AI capabilities, and attract countless partners to innovate on top of them.

According to McKinsey projections, by 2030, generative AI could add $2.6 to $4.4 trillion annually to the global economy, with about 75% of that value realized in four areas: customer operations, marketing and sales, software engineering, and R&D. The vast majority of value capture will belong to existing platform enterprises like Xero and Altium, which have already established beachheads in these areas and can leverage AI to push service depth to the extreme.

Further Reading

1. Goldman Sachs Research Report: The Impact of Generative AI on the Enterprise Software Market - In-depth analysis of how AI is reshaping the software value chain and competitive landscape. https://www.goldmansachs.com/intelligence/pages/generative-ai-could-drive-significant-productivity-boosts.html
2. IDC: Current Status and Future Outlook of Enterprise AI Adoption in Asia-Pacific - Provides regional market data on enterprise priorities and trends when purchasing AI solutions. https://www.idc.com/getdoc.jsp?containerId=prAP50615524
3. McKinsey: The Economic Potential of Generative AI - Comprehensive assessment of the value and impact scope generative AI could create across industries. https://www.mckinsey.com/capabilities/mckinsey-digital