Navigating Language Learning: The Intersection of Knowledge Graphs and Curriculum Structuring

Speak (https://speak.com) may not be very well known to native English speakers, but they have come from a slow start in 2016 to emerge as one of the favorite partners of OpenAI, with their Startup Fund leading and joining their Series B and C as one of the new AI-native unicorns, noting that “Speak has the potential to revolutionize not just language learning, but education broadly”.

Today we speak with Speak’s CTO, Andrew Hsu, on the journey of building the “3rd generation” of language learning software (with Rosetta Stone being Gen 1, and Duolingo being Gen 2). Speak’s premise is that speech and language models can now do what was previously only possible with human tutors—provide fluent, responsive, and adaptive instruction—and this belief has shaped its product and company strategy since its early days.

https://www.linkedin.com/in/adhsu/

https://speak.com

One of the most interesting strategic decisions discussed in the episode is Speak’s early focus on South Korea. While counterintuitive for a San Francisco-based startup, the decision was influenced by a combination of market opportunity and founder proximity via a Korean first employee. South Korea’s intense demand for English fluency and a highly competitive education market made it a proving ground for a deeply AI-native product. By succeeding in a market saturated with human-based education solutions, Speak validated its model and built strong product-market fit before expanding to other Asian markets and eventually, globally.

The arrival of Whisper and GPT-based LLMs in 2022 marked a turning point for Speak. Suddenly, capabilities that were once theoretical—real-time feedback, semantic understanding, conversational memory—became technically feasible. Speak didn’t pivot, but rather evolved into its second phase: from a supplemental practice tool to a full-featured language tutor. This transition required significant engineering work, including building custom ASR models, managing latency, and integrating real-time APIs for interactive lessons. It also unlocked the possibility of developing voice-first, immersive roleplay experiences and a roadmap to real-time conversational fluency.

To scale globally and support many languages, Speak is investing heavily in AI-generated curriculum and content. Instead of manually scripting all lessons, they are building agents and pipelines that can scaffold curriculum, generate lesson content, and adapt pedagogically to the learner. This ties into one of Speak’s most ambitious goals: creating a knowledge graph that captures what a learner knows and can do in a target language, and then adapting the course path accordingly. This level-adjusting tutor model aims to personalize learning at scale and could eventually be applied beyond language learning to any educational domain.

Finally, the conversation touches on the broader implications of AI-powered education and the slow real-world adoption of transformative AI technologies. Despite the capabilities of GPT-4 and others, most people’s daily lives haven’t changed dramatically. Speak sees itself as part of the generation of startups that will translate AI’s raw power into tangible consumer value. The company is also a testament to long-term conviction—founded in 2016, it weathered years of slow growth before AI caught up to its vision. Now, with over $50M ARR, a growing B2B arm, and plans to expand across languages and learning domains, Speak represents what AI-native education could look like in the next decade.

Chapters

• 00:00:00 Introductions & Thiel Fellowship Origins
• 00:02:13 Genesis of Speak: Early Vision & Market Focus
• 00:03:44 Building the Product: Iterations and Lessons Learned
• 00:10:59 AI’s Role in Language Learning
• 00:13:49 Scaling Globally & B2B Expansion
• 00:16:30 Why Korea? Localizing for Success
• 00:19:08 Content Creation, The Speak Method, and Engineering Culture
• 00:23:31 The Impact of Whisper and LLM Advances
• 00:29:08 AI-Generated Content & Measuring Fluency
• 00:35:30 Personalization, Dialects, and Pronunciation
• 00:39:38 Immersive Learning, Multimodality, and Real-Time Voice
• 00:50:02 Engineering Challenges & Company Culture
• 00:53:20 Beyond Languages: B2B, Knowledge Graphs, and Broader Learning
• 00:57:32 Fun Stories, Lessons, and Reflections
• 01:02:03 Final Thoughts: The Future of AI Learning & Slow Takeoff