Hey Claude: Is Google Undervalued?

Assessing the practical state of AI for everyday investors

The question above – is Google undervalued? – is both a hot topic in investing forums that generates fervent, Rorschach-inspired debate, and typical of the types of investigations everyday investors take on before making a trade.

It’s also a great practical test of the state of AI for everyday investing. In particular, we want to figure out if today’s AI can help investors surface genuine insight and put reliable data behind its claims. With platforms like HOOD 0.00%↑ offering AI assistance for trades, it is timely.

First the methods, then the answers.

So, does AI unlock new insight?

Our tests start with Claude Opus 4.8, Anthropic’s most powerful publicly-available model. We asked Claude out-of-the-box about GOOG 0.00%↑ and examined what analysis it performed, where it sourced its data, and ultimately how useful the answer was.

Claude started by polling aggregator websites. These sources quoted P/E against GOOG 0.00%↑’s 10-year average and against the sector. Claude also pulled price targets that represented an average of sell-side ratings – these came pre-averaged: MarketBeat’s roll-up of a “41-analyst” consensus in one pass, a “Strong Buy” average near $429 in another. Claude cited two fair-value figures, one from Simply Wall St. and one from a Peter Lynch-style formula, representing retail-facing models returned on the first page of a web search.

Taking the price targets as an example, we noted that we didn’t have any access to the analyst list, the dates, or the averaging method behind the “consensus” targets. We also observed that Claude’s two passes over the subtask did not agree on the final number. This means that, like lots of the data pulled from the open web, Claude’s analysis rested on signpost numbers without much insight about what went into them or, critically, what was left out of the calculation. For this first analysis, we found that all Claude’s numbers were pulled from such secondary sources.

(Aside: for those following Broadcom AVGO 0.00%↑ earnings, the impact of sourcing analyst targets from the web played out in real time. Within 15 minutes of earnings, CNBC and Reuters had revenue as a miss, while WSJ and Yahoo Finance had it as a beat. Woe to the AI algo trader who isn’t careful…)

Claude’s ultimate answer was a competent summary of the web consensus. It gave an explanation with the same three bull legs and the same bear risks one would expect to find from headlines and the better forum posts. Our takeaway is that it genuinely makes the work of forum-lurking casual investors faster. But a corollary is that it does not by default deploy the type of analysis and modeling techniques professional investors use, or access the data from which they build company valuations. In these ways, out-of-the-box Claude does little to bring professional investors’ sophistication to the rest of us.

So, we asked Claude again and this time gave it access to 10 years of operational KPIs, SEC filing text, segment and competitor summaries via deepKPI, a purpose-built MCP server available to any investor.

With this information, Claude went far far deeper. First, it started by breaking down segments (Cloud, Services, Other Bets), cash-flow and capex history, share count and tax rate, and management commentary to analyze GOOG 0.00%↑‘s operating history. It dove into line items like remaining performance obligation, traffic acquisition cost, and revenue per employee to assess where the company is today and align that with its history. It also offered to pull deepKPI data into a spreadsheet to create a typical institutional-grade operational model, though we stopped short of that for the purpose of this article.

Let’s get concrete about valuation:

The distilled web opinion was that, with Alphabet near $389 and a trailing P/E around 29 against a 10-year average of 27 in a sector closer to 35, it is cheap-ish versus the group and a bit rich versus its own history. Sell-side targets ranged from $412 to $443, though two fair-value models were $112 apart on the same stock. The result was familiar bull legs and bear risks, and a wash on the under/over valued question, which is where we observe forums like r/valueinvesting ending up.

Once we added in deepkpi’s data, Claude was able to do a much deeper analysis of the health of the company against peers and historic norms. For example, the web answer leaned on EV to free cash flow being north of 70, which was categorized by a screener as expensive. But when we dug into the filings, the numbers were reframed: 2025 free cash flow was about $73B and already flat while earnings boomed, and the AI build out is causing 2026 capex to roughly double, $91B → $175 - 185B, while pulling free cash flow toward $15 - 25B. So 72x shouldn’t be taken as a verdict on how expensive the company is, but instead as a snapshot of a company in-motion while it makes a massive bet on the future. Per our recent article, this bet is both the largest among peers and the least risky relative to the core businesses. This changes the interpretation of that number entirely.

The deepKPI-powered analysis also went into the levers of GOOG 0.00%↑’s core business. It noted Cloud’s contracted backlog went $108B → $157.7B in a single quarter. 2.0x → 2.7x of segment revenue. With most of it recognized over the past 24 months, this served as evidence that Cloud has the potential to hold 30%+ growth in the upcoming years. Claude also compared segment margins near 24% to AWS and Azure, two hyperscaler peers, and noted that their margins were better: in the low 30s. This suggested attainable and meaningful room for profit optimization, another sign pointing to the go-forward health of the business.

Claude surfaced two more important levers from the operational data that were not in the web consensus. The first was what GOOG 0.00%↑ pays to get traffic to ads: a lot when they run on network sites, and a little when they run on GOOG 0.00%↑‘s own properties like Search, YouTube, and Gmail. That business is shifting more towards GOOG 0.00%↑ properties, by 2 percentage points on a $265B ad base, showing that profit is growing and has room to do more. The other lever was productivity. Revenue per employee has begun climbing after years of being flat. This matters because the data-center buildout will turn into years of depreciation expense that eat at their margins, and rising output per worker pushes margins back the other way. This is another suggestion that GOOG 0.00%↑ is taking steps to buttress its core business while it sees its AI investment through to fruition.

Taken together, the operational analysis suggested that Cloud is under-earning its peers and has a healthy backlog to power it through the process of closing that gap, the ad business is getting more profitable, and output per employee is rising. Claude + deepKPI concluded that “better than its multiple suggests” is best answered with a model that allows us to test these levers.

As retail investors, we can invest that time, or make an educated guess, but the things that drive our bet are very clear: cloud efficiency versus peers, ad channel mix, and employee efficiency. This level of insight and understanding is far more concrete and clear than our summary of web debate and does bring a testable level of insight to the original question that is not obvious simply from browsing forums.

Our conclusion is that AI models are powerful interpreters of filings and data for retail investors, but one must take steps to guide them away from secondary web data and towards primary source data such as deepKPI’s KPI timeseries and filings markdowns. But, especially as AI services drive the price of access to data and analytical tools down – from $10,000+ per seat for incumbent services like Daloopa to $20/month, or free for some uses for deepKPI – the long-established gap between retail investors and professional investors is meaningfully changing. And quickly.

All numbers above were sourced via Revelata’s deepKPI, which gives you one-click auditing on every datapoint and integration into Excel, Claude, ChatGPT, OpenClaw, or your agent via API. You should try it yourself.