How AI is disrupting software stocks in 2026
How AI is disrupting software stocks in 2026 AI coding tools are driving a wedge between broad tech and software-only funds. Here's what the divergence means for client portfolios. Software has a problem. It’s called AI. For all of the technology’s dazzling displays of prose, picture-generation, and problem solving, code is very much its most fluent language. As of April 2026, Google reported that human-generated code has dwindled to 25%. Tech companies with their own AI products are well-positioned in this environment. They own the tools to automate software engineering and can directly profit from others doing the same. Smaller software companies, however, face a more precarious outlook. The mere prospect of a DIY software future has turned investor sentiment against the Software as a Service (SaaS) businesses, raising predictions of a “SaaSpocalypse” in the process. Why pay for expensive enterprise software when you can build it yourself in-house? To see this trendline in action, look no further than two funds: Invesco QQQ Trust (QQQ) and iShares Expanded Tech-Software Sector ETF (IGV). QQQ is made up of the 100 biggest non-finance companies listed on the Nasdaq stock exchange. Filtering out financial firms means it’s heavily concentrated in broad-based technology companies like Alphabet (Google), Amazon, and Microsoft—all mighty players in the AI investment boom. IGV, meanwhile, primarily holds the software industry, including Salesforce, Adobe, and Intuit. While many of them are racing to integrate AI into their products themselves, they don’t own the underlying technology. These two funds have historically moved in lockstep. As goes software, so goes the broader technology sector. At least until recently. Something snapped late last year, and that correlation broke down. That something was Claude Code, an AI coding tool from Anthropic that went mainstream in late 2025. Its significance for markets lies in what it signaled: AI “agents” could soon handle complex workflows that businesses currently pay SaaS to manage. The investment research firm Citrini added fuel to the fire in February 2026, with the release of“The 2028 Global Intelligence Crisis," a report that imagined a near future where AI agents steal the market share of not just SaaS companies—but major tech and finance firms, too. For all of its alleged shortcomings in sound macroeconomic thinking, the paper went viral and moved markets. Taken all together, software stocks have experienced significant downturns over the last 12 months. With valuations in the software space having reset considerably, there may now be more cushion against further downward price pressure. Many of these businesses are also actively adapting their models—so, it would be premature to count them out. The chart below compares the price-to-earnings ratios currently to those at the beginning of 2025 for a sample of the largest software companies in the world. Stocks like Adobe and Salesforce are trading at a 50% discount now relative to early 2025, based on this valuation metric. More generally, with all of these uncertainties and headwinds out there for sectors like software, why is the market near all-time highs? Some of that resilience may reflect investor momentum, but the more fundamental explanation lies in strong corporate earnings growth. The primary source of investment returns over the long-term is growth in net income—companies' ability to become more profitable. Even as the war in Iran has been going on, analysts have revised their 2026 earnings growth estimates upwards. That's true across the board, with the U.S. as well as firms in Europe, Japan, and emerging markets forecasted to see an acceleration in profit growth. In spite of the headwinds from the conflict in Iran, this earnings backdrop remains a meaningful tailwind for clients holding diversified portfolios with a long-term investment horizon.
Anthropic's new plugin changes how advisors should think about AI
Anthropic's new plugin changes how advisors should think about AI Anthropic's new Wealth Management plugin does meeting prep, plan drafting, and TLH screens out of the box. The question for advisors has shifted. An analysis: In February 2026, Altruist launched AI tax planning inside Hazel, the feature meant to position it as an AI operating layer for independent advisors. Just 60 days later, Anthropic released 10 ready-to-run agent templates for financial services, including a Wealth Management plugin for Claude that accelerates meeting prep, generates pitches, and assists with portfolio analysis and tax-loss harvesting screens out of the box. Each template is a reference architecture built from three parts: skills (task instructions and domain knowledge), connectors (governed data access), and subagents (specialized pieces of the workflow). The skills are file-based Markdown, customizable, and free to install from GitHub. The announcement was framed as a new tool for advisors. What it actually is, is a useful lens for evaluating everything else in your stack. What Anthropic’s wealth management plugin can do for financial advisors Anthropic's Wealth Management plugin sits inside Claude and connects analytical workflows across an advisor's existing systems. It integrates with market data providers, CRMs, and internal document repositories to help advisors centralize information and automate portions of client prep and portfolio analysis. To understand what changed and what didn't, it helps to think about the advisor tech stack in three layers: Interface—dashboards, forms, alerts, reports, chat. Intelligence—interpreting client data, drafting recommendations, surfacing planning opportunities, prioritizing work. This is the layer vendors have been packaging and selling. It is also the layer foundation models do natively, with a few lines of context. Execution—opening accounts, moving money, placing trades, managing tax lots, rebalancing, harvesting losses at scale, supervising activity, maintaining the auditable record. AI is moving fastest through the first two. The third is different. Execution requires balance sheet, broker-dealer infrastructure, regulatory standing, and engineered systems that act on real client accounts under fiduciary control. No foundation-model release replicates that. It is not a software problem. “The right frame for evaluating your advisor tech stack is not AI versus non-AI. It is analysis versus execution.” A model can identify a tax-loss candidate. A platform with brokerage, custody, and tax-lot awareness can implement tax-aware management at scale. A model can draft a rebalance memo. An execution layer can place the trades. A model can summarize a tax return. It cannot become the tax professional, the custodian, the broker-dealer, or the supervisory record. The strategic question is no longer “will AI replace software?” It is: Which features are becoming AI-native utilities, and which systems still create durable value because they control data, workflow, governance, and execution? Why AI commoditization changes how advisors should evaluate technology The obvious objection is that software providers can package these AI capabilities into polished advisor workflows faster and more efficiently than most individual firms could build on their own. And that’s likely true. It doesn’t undercut the argument, it reinforces it. The intelligence layer is being commoditized whether firms build internally, buy third-party software, or combine both approaches. What’s changing is that the underlying AI capabilities are becoming increasingly portable across platforms and providers. Portability changes how advisors should think about software differentiation. Analytical workflows—summarization, planning drafts, portfolio screens, document review—can now move relatively quickly between tools, plugins, and model providers. What does not move nearly as easily is the infrastructure layer underneath. Custody, brokerage operations, compliance frameworks, supervisory systems, and automated execution capabilities are significantly harder to replicate or replace. Those systems require operational scale, regulatory infrastructure, and years of workflow development. The asymmetry is significant. As AI capabilities continue to spread across the industry, the most durable parts of an advisor tech stack are likely to be the systems responsible for execution—not just analysis. What most firms can realistically build The tools to assemble a basic advisor-AI workflow are now genuinely accessible. A motivated firm can stand up meeting prep, client review packets, document Q&A, draft emails, plan summaries, drift reviews, and tax-loss candidate screens using off-the-shelf plugins and customizable skill files. The first layer isn't that hard to build. Sustaining it is a different problem. The skills that make an AI workflow actually useful—investment philosophy encoded as text, tax-review processes, compliance language, approval rules—drift the moment regulations shift, markets move, or the firm's own process changes. Keeping them current isn't a one-time project. It requires rigorous, ongoing supervision to ensure the AI's outputs consistently align with the firm's fiduciary obligations, shifting regulations, and market dynamics. If and when a firm does not have that person, the workflow can degrade quietly until it stops reflecting how the firm actually operates. This is the same reason durable platforms invest heavily in the infrastructure underneath the AI layer. Execution capabilities—moving assets, managing tax lots, rebalancing at scale, maintaining supervisory records—require operational depth that compounds over time, not a one-time build. The firms best positioned to combine intelligent AI workflows with reliable execution likely aren't assembling it themselves. They're building on platforms that have already done the operational work. The AI layer will keep getting easier to access and cheaper to run. What doesn't get easier is the regulatory infrastructure required to act on what the AI surfaces. That gap is where durable value lives. The takeaway The advisor tech stack isn’t disappearing. It’s being reorganized. Packaged software still matters, and for many firms, buying will be smarter than building. But advisors should be increasingly skeptical of software whose primary value is packaging generic AI workflows inside a polished UI. Those capabilities will likely become cheaper, more customizable, and more portable. Durable value will accrue to systems that do what AI alone cannot: connect trusted data, support compliance, execute transactions, implement portfolio decisions, and power a reliable client experience at scale. Two questions are worth applying to every line item in the stack: Is this tool primarily generating, drafting, summarizing, or formatting information? That layer is commoditizing quickly. Is this platform deeply connected to data, governance, workflows, and execution? That layer is more durable. The pace of change matters. Within a single quarter, capabilities that once looked highly differentiated became accessible enough for motivated firms to assemble themselves. And the next wave of commoditization is likely to happen even faster. The firms best positioned for that shift are the ones building on durable operational infrastructure while treating AI as an accelerant for advisor productivity—not the foundation of the business itself. The most durable advisor technology platforms are likely to be the ones that combine intelligent workflows with the infrastructure required to actually execute on them. The key question for advisors is simple: What in your stack is truly durable, and what is ultimately just a UI layered on top of a foundation model?
