Alphabet: Gemini 2.0 Launch Shows Firm’s AI Chops and Competitive Strength

Key Morningstar Metrics for Alphabet

• Fair Value Estimate: $220.00
• Morningstar Rating: 4 stars
• Morningstar Economic Moat Rating: Wide
• Morningstar Uncertainty Rating: Medium

On Dec. 11, Alphabet GOOGL/GOOG released its Gemini 2.0 LLM with a slate of performance improvements and end-user cost reductions. The firm also announced Project Mariner and Jules, two AI agents that can help users with tasks such as browser navigation and fixing errors in computer code.

Why it matters: After spending the past two years in OpenAI’s shadow and being viewed as an AI laggard, Alphabet is changing the narrative.

• We reiterate our view that while OpenAI may be a first mover in generative AI, such an advantage is difficult to maintain over a long period. This is because well-capitalized competitors like Alphabet continue pouring billions into developing their own AI models.
• Over time, we believe the competition in AI will turn from model-led advantages to those based on distribution and scale. Companies like Alphabet, which can distribute and monetize applications built on AI at scale, will likely capture greater value over time.

The bottom line: We maintain our fair value estimate of $220 per share for Alphabet and continue to view the stock as marginally undervalued, even after accounting for the roughly 4% price increase after the Gemini 2.0 launch.

• As we wrote in our Dec. 10 note on the launch of Willow, Alphabet’s quantum AI chip, we believe the market’s perception of Alphabet being an AI laggard is changing.

Coming up: The Gemini 2.0 launch included Gemini 2.0’s Flash model, which is typically smaller than the higher-tier models (think of Gemini 1.5 Flash versus Gemini 1.5 Pro). The Pro model will come next year and will likely include another slate of performance improvements versus the Gemini 1.5 Pro.

Long view: We believe a key strategy from the likes of Alphabet has been commoditizing the model layer, reducing the price per input/output tokens cost. The goal of this strategy is to drive more adoption of these models, which in turn increases spending on the public cloud.