Gemini 3.1 Flash TTS vs Gemini 2.5 Flash Image

Playing around with Gemini 2.5 Flash Image (sorry, not calling it that other name) just now, I felt like Oppenheimer staring at the fireball. Such an enormity of new power, so suddenly.

The masturbators of tomorrow will marvel that people were once limited to non-customized pornography.

Seriously, I think this changes everything.

Fellow Regards and Degenerates,

I'm here to tell you that $GOOGL / $GOOG is the most criminally undervalued stock in mega-cap tech because it’s the undisputed leader in the technologies that define the next century. Forget the short-term noise. This is a deep dive into the strategic moat that others can't even dream of crossing.

**1. Future of Tech**

**Waymo**

Google's Waymo is WAY MORE than a competitor. It's the only fully scaled, commercialized Level 4 self-driving service available to the public. It operates 24/7 robotaxi services in multiple major US cities like Phoenix, San Francisco, Los Angeles, Austin and testing in other cities

In San Francisco, its massive surge in volume has already resulted in its market share surpassing Lyft's, making it the city's second-most popular ride-hailing service. It’s the result of a decade-plus of calm, deep-pocketed investment, allowing it to log over 100 million fully autonomous miles and complete over 10 million paid trips.

The sheer mileage, the complexity of the scaled deployments—which have demonstrated an 80% reduction in injury-causing crashes compared to human drivers—and the fact that they are now expanding internationally to places like Tokyo and London is a moat that no other company has even come close to building. The heck, there is no second competition in autonomous self-driving.

**Quantum Leap for Humanity**

The recent quantum discovery by Google, featuring its Quantum Echoes algorithm, is a major step toward making quantum computers a practical, powerful tool. This breakthrough, which demonstrated verifiable quantum advantage on the Willow quantum chip, is set to accelerate scientific discovery across key industries.

Specifically, the ability to perform verifiable quantum advantage means we can now trust a quantum computer to reliably solve real-world physics problems that are computationally infeasible for classical machines.

What Quantum Echoes Will Do

This breakthrough directly accelerates the original promise of quantum computing:

* Design Better Drugs and Cures: The Quantum Echoes algorithm ran 13,000 times faster on Willow than the best classical algorithm on one of the world's fastest supercomputers. This technique—which is already being used in a quantum-enhanced version of Nuclear Magnetic Resonance (NMR) to study molecular structure—will dramatically cut the time it takes to discover and develop new, more effective medicines by providing unprecedented insights into how potential drug compounds interact with disease targets.
* Create Advanced New Materials: The algorithm's power to reveal previously undetectable details about atomic interactions will unlock the discovery and design of novel materials. This is vital for creating the next generation of:
* High-Performance Batteries (for electric vehicles and energy storage).
* More Efficient Solar Cells.
* Lighter, Stronger Polymers for manufacturing and aerospace.

In short, Google's Quantum Echoes is an engineering milestone that moves quantum computing from a theoretical concept to a practical, verifiable machine for solving humanity's hardest scientific problems.

Think of it this way - The average age of a few generations from now will be approximately 100 years. This is truly remarkable.

**AI: The Medical Revolution**

AI, particularly from Google DeepMind, is already achieving breakthroughs that save time, money, and lives. This is AI's immediate, profitable impact.

* AlphaFold & Isomorphic Labs: AlphaFold, an AI model from DeepMind, solved the 50-year-old problem of protein folding. This monumental achievement earned Google DeepMind's Demis Hassabis and John Jumper a share of the 2024 Nobel Prize in Chemistry (along with David Baker). In simple terms, proteins are the body's tiny machines. Knowing their 3D shape is the blueprint for creating drugs. AlphaFold can find that blueprint in minutes, a process that used to take years. Isomorphic Labs is now using this and other advanced AI to design new small-molecule drugs from scratch at "digital speed," accelerating drug discovery from years to months.
* AI and Quantum Synergy: This is where the magic happens. AI (the brain) helps guide the ultra-powerful quantum computer (the brawn) by identifying which molecules to focus on and then analyzing the quantum simulation results. This hybrid approach makes breakthroughs possible that would be computationally impossible otherwise. Google is the only company with a dominant lead in *both* technologies.

**2. AI Supremacy: The Foundational Architect**

The current AI boom exists because of Google, and its competitive position is strong due to decades of strategic investment focused on making powerful technology affordable enough to scale effectively. By now, it is widely known that the foundational technology for modern AI—the Transformer architecture—was created by Google.

* Models: Leading Across the Modalities Google has established market-leading or top-tier models across text, image, and video.
* Text & Multimodal: The Gemini family of models sets the pace in multimodal reasoning, handling text, code, audio, and video inputs.
* Image (Nano Banana/Imagen): The technology powering Nano Banana (Gemini 2.5 Flash Image) excels at enterprise-critical tasks like advanced editing that preserves character/product consistency across iterations—a crucial capability for marketing and design.
* Video (Veo): Google's cutting-edge video generation models, like Veo, are rapidly advancing the state-of-the-art in creating high-quality, long-form video content.
* Infrastructure: The TPU Efficiency Moat Google designs its own custom AI chips, the Tensor Processing Units (TPUs), which are engineered for peak AI efficiency and low-cost operation. They have spent years perfecting this hardware because a tech needs to be affordable for it to scale and work. This commitment to efficiency is so superior that competitors, including major AI labs, must increasingly rely on the latest generations of Google's custom hardware by coming to Google Cloud Platform (GCP) to train and run their own cutting-edge models. This external validation proves that Google's approach is about making large-scale AI economically sensible.

The Vertical Advantage:

Google is the only major company that is competing fiercely and winning or coming close to the top in every critical layer of the AI stack:

1. Infrastructure (TPUs): Competing directly with NVIDIA on highly efficient, specialized AI silicon.
2. Foundation Models (Gemini, Imagen, Veo): Competing with OpenAI/Microsoft and Anthropic on core intelligence.
3. Applications (Nano Banana, AI Overviews): Integrating AI features into products that serve billions of users globally.

This end-to-end control, from the silicon chip to the final consumer application, provides a powerful strategic and economic advantage that is unmatched in the industry.

**3. The ChatGPT Myth and Search Dominance**

The idea that chatgpt will kill Google Search is a false narrative. Facebook, Instagram, TikTok, Reddit all were supposed to reduce google search queries. They have only grown. This new technology has made it much easier to ask any type or questions in any language. We were previously limited to what we would or could google. Now there are no limits. The more we know, the more questions we have and the more we search. Google search will be just fine.

I think ChatGPT will become another app on the phone where users will go to. I envision it as a personal assistant and less of search. But only time will tell.

Google was and will remain the gateway to the internet. The new AI business will be a net positive for Google by creating a new revenue stream through Google Cloud (GCP) and gemini features and subscriptions to its user base.

**4. The Financial Powerhouse and PE Hypothesis**

The fundamentals confirm this giant is firing on all cylinders.

* Net Income King: Alphabet's Trailing Twelve Months net income ending June 30, 2025, was $115.573 Billion, making it one of the most profitable companies in the world. This was more than MSFT $101.832 billion and APPL $99.280 billion
* Accelerating Triple-Threat Growth: All core segments - Google Cloud, Youtube and Google Search are growing at double-digit rates.

The core reason Google's Price-to-Earnings (PE) ratio is generally lower than many other tech companies is its revenue mix being heavily dominated by consumer advertising.

Simply put, investors are willing to pay a higher multiple (PE) for the more predictable, higher-margin, and rapidly growing recurring revenue streams typical of enterprise software and cloud platforms.

My hypothesis is with AI increasingly driving revenue through Google Cloud Platform (GCP), the enterprise segment will become a bigger component of Google's business mix, and hence, the company will earn a higher blended Price-to-Earnings (PE) ratio. This is because Enterprise and Cloud businesses are valued more highly, providing predictable, high-margin, recurring subscription revenue (SaaS), a financial profile superior to advertising. As this higher-multiple segment captures a greater share of Google's overall profit, the market will be forced to re-rate $GOOGL with a higher blended multiple, making the current valuation—which is depressed by the ad-centric multiple look like a significant undervaluation and a compelling investment opportunity.

TLDR : GOOGL is a generational buy. You're buying the best-in-class *present* (Search/Maps/YouTube), the scaled *near-future* (Waymo/GCP), and the *long-term future* (Quantum/AI Core Tech) at a discount.

https://preview.redd.it/h9doi0xnuywf1.png?width=1179&format=png&auto=webp&s=741748eadb6976d2ebf32a72f601343e6abc7d5c

We've been building [Scenema Audio](https://scenema.ai/audio) as part of our video production platform at scenema.ai, and we're releasing the model weights and inference code.

The core idea: emotional performance and voice identity are independent. You describe how the speech should be performed (rage, grief, excitement, a child's wonder), and optionally provide reference audio for voice identity. The reference provides the "who." The prompt provides the "how." Any voice can perform any emotion, even if that voice has never been recorded in that emotional state.

# Limitations (and why we still use it)

This is a diffusion model, not a traditional TTS pipeline. Common issues include repetition and gibberish on some seeds. Different seeds give different results, and you will not get a perfect output with 0% error rate. This model is meant for a post-editing workflow: generate, pick the best take, trim if needed. Same way you'd work with any generative model.

That said, we keep coming back to Scenema Audio over even Gemini 3.1 Flash TTS, which is already more controllable than most TTS systems out there. The reason is simple: the output just sounds more natural and less robotic. There's a quality to diffusion-generated speech that autoregressive TTS doesn't quite match, especially for emotional delivery.

# Audio-first video generation

As [this video](https://www.youtube.com/watch?v=ZZO3XAy3KTo) points out, generating audio first and then using it to drive video generation is a powerful workflow. That's actually how we've used Scenema Audio in some cases. Generate the voice performance, then feed it into an A2V pipeline (LTX 2.3, Wan 2.6, Seedance 2.0, etc.) to generate video that matches the speech. [Here's an example of that workflow in action.](https://youtu.be/dcAjQhPKNLk?si=4iOwtpsLR-WzwDmF)

# On distillation and speed

A few people have asked this. Our bottleneck is not denoising steps. The diffusion pass is a small fraction of total generation time. The real costs are elsewhere in the pipeline. We're already at 8 steps (down from 50 in the base model), and that's the sweet spot where quality holds.

# Prompting matters

This model is sensitive to prompting, the same way LTX 2.3 is for video. A generic voice description gives you generic output. A specific, theatrical description with action tags gives you a performance. There's also a `pace` parameter that controls how much time the model gets per word. Takes some experimentation to find what works for your use case, but once you do, you can generate hours of audio with minimal quality loss.

Complex words and proper nouns benefit from phonetic spelling. Unlike traditional TTS, it doesn't have a phoneme-to-audio pipeline or a pronunciation dictionary. If it garbles "Tchaikovsky," you would spell it "Chai-koff-skee" or whatever makes sense to you.

# Docker REST API with automatic VRAM management

We ship this as a Docker container with a REST API. Same setup we use in production on scenema.ai. The service auto-detects your GPU and picks the right configuration:

|VRAM|Audio Model|Gemma|Notes|
|:-|:-|:-|:-|
|16 GB|INT8 (4.9 GB)|CPU streaming|Needs 32 GB system RAM|
|24 GB|INT8 (4.9 GB)|NF4 on GPU|Default config|
|48 GB|bf16 (9.8 GB)|bf16 on GPU|Best quality|

We went with Docker because that's how we serve it. No dependency hell, no conda environments. Pull, set your HF token for Gemma access, then `docker compose up`.

# ComfyUI

Native ComfyUI node support is planned. We're hoping to release it in the coming weeks, unless someone from the community beats us to it. In the meantime, the REST API is straightforward to call from a custom node since it's just a local HTTP service.

# Links

* **All demos + article:** [scenema.ai/audio](https://scenema.ai/audio)
* **Model weights:** [huggingface.co/ScenemaAI/scenema-audio](https://huggingface.co/ScenemaAI/scenema-audio)
* **Code + setup:** [github.com/ScenemaAI/scenema-audio](https://github.com/ScenemaAI/scenema-audio)
* **YouTube demo:** [youtu.be/VnEQ\_ImOaAc](https://youtu.be/VnEQ_ImOaAc)

This is fully open source. The model weights derive from the LTX-2 Community License but all inference and pipeline code is MIT.