Matt Schmid

Marketing technology. AI in practice. Product marketing. More coffee please. #B1GCats

[Matt Schmid @MattSchmid]

@kyleshannon Good on ya Kyle! Memorial Day edition.

[Todd Spence @Todd_Spence]

Someone edited THE THREE AMIGOS vs. TOMBSTONE and it fits so perfectly 😂🔥

[Logan Kilpatrick @OfficialLoganK]

Gemini

[Matt Schmid @MattSchmid]

@nlw which is more important for best results? My context stack (.md files) or model memory?

[Sam Altman @sama]

i appreciate how seriously the team always takes these reports (even when the answer turns out to be 'i got used to the current level of magic and now i'd like more please')

[Matt Schmid @MattSchmid]

@interact_ai This is a post even a bot could safely comment on!

[Kyle Shannon (AI Learning Lab/AI Salon) @kyleshannon]

HOLY CRAP!

New Image 2 model from ChatGPT…

Prompt: Create a detailed infographic on the history of muscle cars

[Matt Schmid @MattSchmid]

@OfficialLoganK My favorite vibe coding platform. I’m no ninja, but GAIS is very good.

[Alex Kantrowitz @Kantrowitz]

New Data: OpenAI’s Lead Is Contracting as AI Competition Intensifies

AI market share over the past year:

- ChatGPT from 69.1% to 45.3%
- Gemini from 14.7% to 25.1%
- Grok from 1.6% to 15.2% https://t.co/3OMNAiQ6AB

[Matt Schmid @MattSchmid]

@MbarkCherguia Up. Unless it’s a sharp tip knife.

[Matt Schmid @MattSchmid]

Google will eclipse ChatGPT.

[The Information @theinformation]

With nearly $100 billion in annual capex planned, Google is pointing to rapid Gemini adoption as evidence its AI investments are paying off. https://t.co/cti7N9cOoo

[Similarweb @Similarweb]

First Global AI Tracker of 2026

Gen AI Website Worldwide Traffic Share, Key Takeaways:

→ Gemini surpassed the 20% share benchmark.
→ Grok surpasses 3% and is approaching DeepSeek.
→ ChatGPT drops below the 65% mark.

🗓️ 12 Months Ago:
ChatGPT: 86.7%
Gemini: 5.7%
Perplexity: 1.9%
Claude: 1.5%
Copilot: 1.5%

🗓️ 6 Months Ago:
ChatGPT: 78.6%
Gemini: 8.6%
DeepSeek: 4.8%
Grok: 2.1%
Perplexity: 1.6%
Claude: 1.5%
Copilot: 1.1%

🗓️ 3 Months Ago:
ChatGPT: 74.1%
Gemini: 12.9%
DeepSeek: 3.7%
Perplexity: 2.4%
Grok: 2.0%
Claude: 2.0%
Copilot: 1.2%

🗓️ 1 Month Ago:
ChatGPT: 68.0%
Gemini: 18.2%
DeepSeek: 3.9%
Grok: 2.9%
Perplexity: 2.1%
Claude: 2.0%
Copilot: 1.2%

🗓️ Today (January 2):
ChatGPT: 64.5%
Gemini: 21.5%
DeepSeek: 3.7%
Grok: 3.4%
Perplexity: 2.0%
Claude: 2.0%
Copilot: 1.1%

>>

[xAI @xai]

https://t.co/RzNuclNfsR

[Ivan Zhao @ivanhzhao]

https://t.co/fxHYR6S29D

[TechCrunch @TechCrunch]

VCs deploy ‘kingmaking’ strategy to crown AI winners in their infancy https://t.co/FeIYWepEAo

[Lenny Rachitsky @lennysan]

My biggest learnings from Jeanne DeWitt Grosser (ex-Chief Business Officer at @Stripe, now @Vercel COO):

1. What failed seven years ago now works with AI. In 2017, Jeanne tried to build a system at Stripe that would automatically personalize outbound emails based on company data. Despite working with world-class data scientists, it failed due to too many errors. Today, that exact same approach works. This shows how AI has made previously impossible ideas suddenly viable.

2. A single GTM engineer at Vercel reduced a 10-person sales team to 1 (in just 6 weeks). Jeanne’s team at Vercel had an engineer build an AI agent that handles inbound lead qualification, outbound prospecting, and deal loss evaluation. The agent costs $1,000 per year to run versus over $1 million in salaries for the sales team. The nine displaced team members moved to higher-value work rather than being laid off, and the remaining salesperson is 10 times more efficient.

3. Their AI deal-loss bot has become better at understanding what went wrong than humans. When Jeanne analyzed her biggest loss of the quarter, the salesperson blamed pricing. But an AI agent reviewed every email, call transcript, and Slack message and discovered the real reason: they never spoke to the person who controls the budget, and when ROI came up, the customer clearly didn’t believe the value claims. They are now using AI to analyze sales calls in real time and send alerts like “You’re halfway through the sales process and haven’t talked to a budget decision-maker yet.”

4. Wait until $1 million in revenue before hiring your first salesperson. Founders should continue selling themselves until they reach around $1 million in annual revenue with a repeatable process. The key is having a defined ideal customer profile—customers who look alike.

5. Segment customers on what drives their buying decisions, not just company size. OpenAI has roughly 3,000 employees, which would typically put them in the “mid-market” category. But they’re a top-25 website globally by traffic, so Vercel treats them as enterprise customers requiring complex sales. Effective segmentation combines company size with growth rate, web traffic, workload type, and industry—because selling to e-commerce companies requires completely different language than selling to crypto companies.

6. Most customers buy to avoid risk, not to gain opportunity. About 80% of customers purchase to reduce pain or avoid problems, while only 20% buy to increase upside. This means you should focus your sales messaging on what could go wrong without your product—like falling behind competitors or damaging their reputation—rather than just talking about exciting features. This is especially true when selling to larger companies, where individual careers are on the line.

7. Sales teams should be indistinguishable from product managers—for a bit. Jeanne hires salespeople who have such deep product knowledge that if you put one in front of a group of engineers, it should take 10 minutes to realize they’re not a product manager. This credibility allows sales teams to serve as an extension of research and development—a 20-person sales team talks to hundreds of customers weekly and can translate those conversations into product insights at scale.

8. Building your own AI sales tools may beat buying off-the-shelf software. Because AI is so new and every company’s sales process is unique, Jeanne finds that building custom internal agents often delivers more value than buying vendor solutions. A single go-to-market engineer built their deal analysis bot in just two days, perfectly tailored to their specific workflow. These engineers shadow top salespeople to understand their workflows, then build automation that would have taken months or been impossible just a few years ago.

9. Make every sales interaction great, whether customers buy or not. Jeanne replaced boring discovery calls at Stripe with collaborative whiteboarding sessions where customers drew their payment architecture. Many customers had never visualized their own systems before. They left with a useful asset and a feeling of collaboration, regardless of whether they bought. Many returned years later to purchase. Think about your go-to-market process like a product, not just a sales function.

10. Product-led growth has a ceiling—no $100 billion company runs on it alone. While product-led growth (where users can sign up and start using a product without talking to sales) works well for early growth, customers generally won’t spend a million dollars through a self-service flow. Every major technology company eventually builds a sales team for larger deals. The mistake is waiting too long, since building a predictable sales process takes time.

[DeepSeek @deepseek_ai]

🚀 Launching DeepSeek-V3.2 & DeepSeek-V3.2-Speciale — Reasoning-first models built for agents!

🔹 DeepSeek-V3.2: Official successor to V3.2-Exp. Now live on App, Web & API.
🔹 DeepSeek-V3.2-Speciale: Pushing the boundaries of reasoning capabilities. API-only for now.

📄 Tech report: https://t.co/7EyydyNuG0

1/n

[Luke Barnett @LukeBarnett]

Coca-Cola spent millions of dollars on 70,000 prompts for their AI ad.

We made whatever this is for $3 in 45 minutes.

You’re welcome, Pepsi.

[Logan Kilpatrick @OfficialLoganK]

Good news: rate limits have doubled for Deep Think in the @GeminiApp

Happy prompting : )

[Igor Babuschkin @ibab]

Today was my last day at xAI, the company that I helped start with Elon Musk in 2023. I still remember the day I first met Elon, we talked for hours about AI and what the future might hold. We both felt that a new AI company with a different kind of mission was needed.

Building AI that advances humanity has been my lifelong dream. My parents left the Russian Federation after the collapse of the USSR in search of a better life for their kids. Life wasn’t always easy as immigrants. Despite the hardships, my parents believed that human values were priceless: values like courage, compassion, curiosity for understanding the world. As a child, I admired scientists like Richard Feynman and Max Planck, who relentlessly pushed the frontiers of physics in order to understand the universe. As a particle physics PhD student at CERN I was excited to contribute to that mission. But the search for new physics was getting harder and harder, requiring bigger and bigger colliders, while new discoveries kept getting fewer. So I began to wonder if superintelligence, not larger colliders, could be the key to unlocking the mysteries of the universe. Could AI develop a consistent theory of quantum gravity? Could AI prove the Riemann hypothesis? In early 2023 I became convinced that we were getting close to a recipe for superintelligence. I saw the writing on the wall: very soon AI could reason beyond the level of humans. How could we ensure that this technology is used for good? Elon had warned of the dangers of powerful AI for years. Elon and I realized that we had a shared vision of AI used to benefit humanity, thus we recruited more like minded engineers and set off to build xAI.

The early days of xAI were not easy. Naysayers told us that we arrived too late to the game, so starting a top AI company from scratch would be impossible. But we believed we could do the impossible. Starting a company from zero required lots of hands-on work. In the beginning I built many of the foundational tools used at the company to launch and manage training jobs. I later oversaw much of the engineering at the company, including Infrastructure, Product and Applied AI projects. xAI’s people are deeply dedicated. Through blood sweat and tears, our team’s blistering velocity built the Memphis supercluster, and shipped frontier models faster than any company in history. I learned 2 priceless lessons from Elon: #1 be fearless in rolling up your sleeves to personally dig into technical problems, #2 have a maniacal sense of urgency.

xAI executes at ludicrous speed. Industry veterans told us that building the Memphis supercluster in 120 days would be impossible. But we believed we could do the impossible. Our goal was to get our training setup running at scale on the Memphis cluster ASAP. Towards the end of our 120 day deadline, we were riddled with mysterious issues with communicating over RDMA between the machines. Elon decided to fly to the datacenter, and we followed. Our infra team landed in Memphis in the middle of the night and got straight to work. After pouring through tens of thousands of lines of lspci output we finally identified a wrong BIOS setting, the root of the problem. Elon was there with us until late into the night. When the training run finally worked, Elon posted our triumph at “4:20am” causing us to laugh out loud. I will never forget the rush of adrenaline that night, and the emotional bonds that we were all in this together. We went to bed feeling like we were living through the most exhilarating time of our lives.

I have enormous love for the whole family at xAI. Our team is truly special - you’re the most dedicated people I’ve ever worked with. Catching up to the frontier this quickly hasn’t been easy. It was made possible by everyone’s diehard grit and team spirit. Thank you to every single person who joined me on this adventure. I want to honor your contributions, your time, your sacrifices, which are never easy. I will always remember working together far into the nights and burning the midnight oil. I will never forget the sacrifices and contributions you’ve made. As I drive away today, I feel like a proud parent, driving away after sending their kid away to college. My heart is brimming with tears of joy, rooting for the company as it grows and matures.

As I'm heading towards my next chapter, I’m inspired by how my parents immigrated to seek a better world for their children. Recently I had dinner with Max Tegmark, founder of the Future of Life Institute. He showed me a photo of his young sons, and asked me “how can we build AI safely to ensure that our children can flourish?” I was deeply moved by his question. Earlier in my career, I was a technical lead for DeepMind's Alphastar StarCraft agent, and I got to see how powerful reinforcement learning is when scaled up. As frontier models become more agentic over longer horizons and a wider range of tasks, they will take on more and more powerful capabilities, which will make it critical to study and advance AI safety. I want to continue on my mission to bring about AI that’s safe and beneficial to humanity. I’m announcing the launch of Babuschkin Ventures, which supports AI safety research and backs startups in AI and agentic systems that advance humanity and unlock the mysteries of our universe. Please reach out at [email protected] if you want to chat. The singularity is near, but humanity’s future is bright!