Conversation Design Institute

As Global Head of Sales at CDI, I lead the charge in expanding our reach and impact across the world. My role focuses on aligning innovative sales strategies with our mission to empower businesses at every stage of their conversational AI journey—whether they’re just starting out or scaling to new heights.

With a dual focus on training and consulting, I work to drive growth across CDI’s core offerings, including hands-on workshops, comprehensive audits, and strategic consulting. My goal is to ensure businesses are equipped with the knowledge, tools, and support they need to implement impactful, future-ready conversational AI solutions.

In this role, I’m passionate about building strong partnerships, guiding teams toward success, and contributing to CDI’s evolution as a global leader in conversational AI. Let’s connect and explore how we can shape the future of conversational AI together!

Global Head of Sales

Companies around the world are looking for conversation designers, a rare breed of people that helps them advance communication between people and AI. That's where we help!

Conversation Design Institute teaches a human-centric workflow that has proven itself to organisations around the world. We work closely with tech partners, agencies and other key players in the industry. With this support, Conversation Design Institute is well on its way to becoming the number one platform in conversation design.

Become a certified professional, join one of our online courses, or work with our experts.

Business Development Specialist

Scaled Sales 

Good business coaches are there to help you see the blind spots and serve as your compass to make sure you are heading in the right direction. A business coach serves as an important objective third party to regularly check in to make sure that you are on the most direct path to your ideal destination. Oftentimes, the biggest hurdle to success is that you don't know what you don't know. You can't Google your way out of a problem that you aren't sure how to articulate—or worse, how to anticipate.

One pro for hiring a business coach for yourself is that it allows you to get constructive criticism from someone that doesn’t necessarily know you. 

A business coach is more like a person you're accountable to. No matter what level of success you have attained, there is a tendency to stay in your own lane and not observe habits and patterns that have been successful for others. A strong business coach can help increase awareness of methods and keys to success that have worked for other people and may be analogous to your own situation. 

One of the best investments you can make is in yourself. If you find yourself struggling in your professional career, or desire to increase your sales or improve your business relationship, then a business coach is an invaluable asset. One of the major advantages of having a coach or mentor is that they help you navigate through challenges and meet personal goals with their insights.


Steve Jobs once said, "We hire smart people so they can tell us what to do." Where, if not coaching, could this apply to more? 

One of the biggest benefits of hiring a business coach is that they will help you stay on the right track. Building a business is tricky, especially if you're a startup. It's also very easy to get distracted by all the opportunities that come your way, but you can't possibly do it all. The key is setting the goals, developing the action plan on how to reach these goals and executing to make them happen.

Business Consultant

Yell 

Senior Sales Manager

Fingo - AMi

Digital Strategy Director

As a Business Development Specialist at Conversation Design Institute (CDI), I help organizations around the world create human-centric AI Assistants, such as chatbots and voice assistants. 

At CDI, I work closely with tech partners, agencies, and other key players in the industry to offer high-quality e-learning programs and live sessions for teams and individuals. I have trained hundreds of professionals in strategy, design, and development for AI Assistants, using our proven workflow and best practices.

My goal is to empower more people to design, deploy, and manage valuable conversational experiences.

Benjamin Taylor

I’m lucky enough to get to hear the episodes coming out of the academy before anyone else. 

So much of this episode struck me that I was blown away. 

Hillary Black  clearly gets it and we are unbelievably proud to have her as part of the community.

Global Head of Sales | Driving Growth in Conversational AI | Training, Auditing & Consulting Leader

View company: Conversation Design Institute (CDI)

We’re still at the beginning of conversational AI’s potential.

While prompting and new tools may make it feel like we’re starting over, this time the context is different. The widespread attention around AI has raised the baseline understanding across companies, brands, and individuals.

Many still don’t fully understand what conversational AI or conversation design truly means—but the conversation has changed. Today, people come in more informed, more curious, and more ready to explore what’s actually possible.

Join Hillary Black , conversation designer at MAV in the new episode of unprompted. by CDI. 

Watch the full video here https://hubs.la/Q03YBjZB0

That shift opens the door to deeper, more meaningful progress.
#ConversationalAI #ConversationDes

amalgamy.ai by ThisWay Global

Launch AI/HPC is software built to dramatically improve the efficiency of data centers, CSP's, Neo-Clouds and enterprise organizations.  
Sully's Sandbox is a suite of innovative products for company's using AI, agents, RAG models and automation that are seeking real ROI.

Founder/CEO of amalgamy.ai

ThisWay Global

Exciting News for 2025!
ThisWay has now added a special division for talent sourcing in these high growth markets:
AI & HPC focused organizations
Data Centers Operators
Neo-Cloud Operators
US Public Sector & SLED 
US Defense Organizations
US Government Contractors

Founder/CEO

Ai4JOBS

Our company empowers better job decisions, for people and businesses, based on real-time data.  Ai4JOBS is available via the company's online application, custom integration, customer dashboard, direct API, Salesforce App Exchange, and applicant tracking systems.

United States Marine Corps

Currently I lead the development of a jobs platform that will convert military experience into accurate civilian credentials that will be relevant to both the employer and the prior service member.  It's thiswayglobal.com  We welcome your comments.
Note - I am a USMC spouse of more than 23 years, with US government delegate experience with the National Military Family Association, but I am not prior service member.  This is just how LinkedIn categorizes this experience.  
We appreciate the trust shown by more than 50,000 service members and families who have registered for ThisWay.  You will be accepted to our public beta launch in the very near future.

NMFA Delegate

K2S Corporation

Consultant to European start-up and pre-seed entrepreneurs interested in fund raising, marketing and/or launching a product in the US market.  
Specific areas of interest are IT, international education technologies and other high-tech platforms, as well as the development and manufacturing of licensed sports products.
This work included securing licensing with NCAA, MLB, NBA and NFL products.

Consultant

Key2Sports

Key2Sports was originally founded to provide business based support for sports teams.  As a result of niche growth from 2010-2012, Key2Sports pivoted to design and manufacturing of licensed products NCAA, NFL and NBA organizations, as well as sports related companies like www.wePlay.com, Olympic training teams, etc. Manufacturing was located across Asia, Guatemala and in the United States.

Founder

Artefacts Development Corp

Led the acquisition, financing, design, development and construction of a 50 acre multi-use development project, focused on aging in place, just NW of Austin, Texas, USA.  The aesthetic of the project was new, sustainable architecture designed to reflect pre-WWII/historic architecture.  Innovative neighborhood features include land planning for separate live/work structures provided at each property, utility infrastructure plans that saved 35% in development costs, a 3+ acre central park with pool and pool house. This project received numerous awards for innovation and environmental sensitivities.

Managing Partner

Hood Consulting

Refurbished and restored numerous historic properties, both residential and commercial.  Provided client management and technical support to architects and builders of luxury homes located throughout Beaufort, South Carolina and Hilton Head Island, South Carolina.
Received the award of Best Exterior Design by the Parade of Homes.

Dataw Island Construction

Assisted the Senior Construction Manager Alcoa AlumaCorp in the daily operations and technical, construction support for the construction of more than 100 luxury, residences.

Technical Consultant

Hi everyone,

If you would like to connect with me and we don't know each other PLEASE reach out to contact@thiswayglobal.com and share what you would like to talk about so you can be connected to the right person on my team. You will have far greater success than reaching out to me here in LinkedIn.

My team will accept public speaking requests, customer invites and investor communications here.

Otherwise, feel free to "follow" me 

Hope you have a great day!
Angela

I am the Founder/CEO of ThisWay Global, an international, VC-backed HR tech company that was incubated at ideaSpace – University of Cambridge, UK, with new offices now in the US. ThisWay's team is comprised of experienced entrepreneurs, global PhD’s in the areas of machine learning and AI, as well as talent from across the globe. 

Over the last twenty years, I have founded and built three companies, in sectors that range from architecture to the licensing industry. The common core in each has been my ability to build consensus amongst stakeholders, whether they be individuals, businesses or international governments.

I am a recognized thought leader on the topics of "Diversity and ROI" and "AI in the new world of work."

Without knowing my history, it would seem that the leap from land development to tech development is a big one. However, ThisWay is the manifestation of my drive to solve big problems. As early as 1992, when I founded the Texas A&M College of Architecture Career Fair, through my award-winning land development plan in the early 2000s, I have always sought to find meaningful solutions to big challenges.  

How we got to where we are today:
IBM Silver Business Partner
Google Accelerated Founder and Startup
Business Journal Finalist - Women of the Year 2023
ideaSpace-University of Cambridge: Alumni Founder
Institute of Enterprise and Entrepreneurs: Lifetime Fellow
Cambridge Wireless: Founder Member
Enterprise Nation: Entrepreneurship Mentor for both Growth and Hiring

Get in touch with me here or via either of the channels below:

Angela Hood

I've been thinking about how to evaluate the AI investment landscape 

When everyone involved has clear incentives.

On one side, you have people saying there's no bubble.

Jensen Huang (Nvidia CEO): "From our vantage point, we see something very different."

David Sacks (White House AI czar and VC): "We're in an investment super-cycle."

Ben Horowitz (a16z): "Doesn't look like a bubble at all to me."

These are smart people. They also benefit directly from AI spending continuing.

On the other side, you have skeptics pointing to specific financial patterns.

Big Tech is spending $400 billion on AI this year. 
$3 trillion projected through 2028. 

Goldman Sachs: hyperscaler companies took on $121 billion in new debt last year (300%+ increase).

Some using special purpose vehicles to keep debt off balance sheets. 

And the circular deals: Nvidia giving OpenAI $100B to bankroll data centers that will buy Nvidia chips.

These skeptics have their own biases too. Some betting against Nvidia (Michael Burry). Some are academics who may underestimate technology adoption curves.

So how do you evaluate this when both sides have valid points AND clear incentives?

The optimists point to real AI capabilities improving. The skeptics point to concerning financial structures.

Even AI leaders seem uncertain.

Sam Altman (OpenAI): "Are we in a phase where investors are overexcited about AI? Yes. Is AI the most important thing to happen in a very long time? Also yes."

Sundar Pichai (Google): "There are elements of irrationality" but when asked if Google would survive a bubble burst: "No company is immune, including us."

So how do you think through this?

AI for B2B expert/ Forbes 50/50 List / INC Magazine Founder / Google Accelerated / IBM Think Keynote / Outstanding Alum@TAMU & Founder/Alum Uni of Cambridge: ideaSpace Founder/Alumni

Microsoft dropped 70+ announcements at Ignite 2025.

The message: Agent era begins.

First, Microsoft broke its OpenAI exclusivity. 

Anthropic's Claude (Sonnet, Haiku, Opus) now available on Azure and in Copilot.

After years of being tied to OpenAI, they're positioning Azure as the model-agnostic platform. The Model Router automatically picks the cheapest/best model for each task across OpenAI, Anthropic, Llama, or Mistral.

Second, Agent 365 attempts to solve the "Shadow AI" problem. Employees are building their own bots. Sales agents, HR agents, data analysis agents. 

Most created with tools they found online.

Nobody knows what's running, what data agents access, or which ones are security risks.

Agent 365 is the command center. A complete registry of every agent in your org, including agents employees created on their own. IT can limit agents to resources they need, set access policies based on behavior, real-time monitoring of agent connections.

Agents get digital identities (Entra Agent ID) like employee badges. They only access data they have permission to see.

Microsoft predicts 1.3 billion agents deployed by 2028. The infrastructure to manage them matters as much as building them.

Third, the autonomous agents Microsoft's selling. Sales Development Agent researches prospects, crafts outreach, follows up 24/7. When leads are qualified, hands off to human sellers. An SDR that never sleeps. Available now via Frontier program.

Excel Agent writes Python code, generates charts, analyzes risks from prompts. PowerPoint Agent builds decks with storytelling and branding. Word Agent organizes complex information into strategic plans.

SharePoint Agent creates pages and lists. Just type "@SharePoint create a tracking list for Q4" and it builds the structure.

The App Builder lets anyone build functional apps by describing them. A retail manager built a shift-scheduling app in minutes just by explaining the problem.

Background agents doing work beats chat interfaces.

Copilot then becomes the orchestrator of agents.

An IDC study Microsoft commissioned found a gap forming. 68% of companies use AI today. But the top 22% ("Frontier firms") are achieving 3x higher returns than slow adopters.

What separates them? Using AI across 7+ business functions (not just one or two). Building custom AI solutions (58% today, jumping to 77% in 24 months). Aggressively adopting agentic AI.

IDC estimates companies using agentic AI will triple in the next two years.

Agent 365 is the infrastructure play. If agents become the workforce, whoever controls the management layer has leverage regardless of which models win.

And Microsoft has the resources and distribution to pull it off.

G7 digital and technology ministers are meeting in Montreal this week.

AI expected to dominate 50% of the conversation.

EU wants AI regulation, 

US maybe less so. 

Canada is going to play middle ground host.

Mark Daley, chief AI officer at Western University, on standards:

"They're not glamorous, they're not glorious, but standards actually can change the world. And Canada as a middle power, this is one of the levers we have traditionally used in technology domains to have outsized influence."

Interoperability of AI technology is one of the key areas being discussed.

The G7 countries also agreed at the June leaders' summit to accelerate AI adoption in the public sector. Not every country thinks AI should be embedded in public services to this degree.

Canada's stated priorities: broadening trade routes and partnerships, driving investment, and strengthening "sovereign AI and sovereign quantum."

Montreal has global strength in AI research and talent.

And while you're not going to solve global AI governance at a two-day meeting, it helps to understand what your closest partners are doing, and where you want to cooperate.

The unsexy work of coordination and standards-setting doesn't make headlines. But if implemented, shapes what actually gets built.

Do these multilateral AI conversations produce anything concrete?

Nvidia sold $147.8 billion in chips and hardware from Feb through Oct.

Up from $91 billion same period last year.

But the competitive landscape has changed dramatically.  

Google's TPUs are now being bought or negotiated by Meta, Anthropic, and Apple. 

Amazon is building a data center cluster for Anthropic with over 1 million Trainium chips. AWS just launched broader sales of chips they claim are faster and more energy efficient than Nvidia's.

AMD market cap nearly quadrupled to $350B. Major deals with OpenAI and Oracle.

OpenAI struck a multibillion-dollar partnership with Broadcom to develop custom chips.

Meta acquired chip startup Rivos for in-house AI training chips.

Microsoft said they'll rely more heavily on custom accelerator chips in their data centers.

xAI is hiring chip designers for "new hardware architectures."

Some of Nvidia's biggest customers are becoming chip designers themselves.

And yes, Jensen says they've sold 6 million Blackwell chips so far this year with orders for 14 million more. Half a trillion dollars in total.

Nvidia's boasts flexibility. Their systems have broader uses than custom chips optimized for specific tasks.

But money is flowing in other directions. When you're spending billions on compute, even a 20% efficiency gain from custom silicon can justify the R&D investment.

Nvidia isn't going anywhere. But the total domination run might be coming to a close. 

I think diversification is healthy for the AI ecosystem, and it benefits us all as end consumers.

Just in time for Thanksgiving. 

A robot that can do dishes!

Meet Memo from Sunday Robotics. 

Who emerged from stealth Tuesday with $35M from Benchmark and Conviction. 

The mission? 

Home robots trained on real family routines.

Memo is a two-armed rolling robot. Clears tables, loads dishwashers, folds laundry, organizes shoes, pulls espresso shots.

Sunday built Skill Capture Gloves that cost $200. They record how real people move, clean, and organize in their actual homes. The gloves share exact geometry and sensors with Memo's hands.

If you can do it wearing the glove, Memo can learn it.

Sunday shipped 2,000 gloves to "Memory Developers" across 500+ homes. Collected 10 million episodes of real family routines. Messy, chaotic, real-world data.

"Constantly surprised by what we find—from cats in dishwashers to bucketloads of plums on the table."

The dishes task is what they call "the classic nightmare scenario for roboticists." Long-horizon, highly dexterous, precise manipulation with delicate, transparent, reflective, deformable objects.

One dinner cleanup can have 33 unique and 68 total dexterous interactions with 21 different objects while navigating 130+ feet.

(Say that 10 times fast)

Memo handles wine glasses without breaking them. Zero breakages over 20+ live demo sessions.

The team is comprised of Stanford PhD roboticists known for ALOHA, Diffusion Policy, UMI. Ex-Tesla Autopilot engineers including former FSD autonomy and ML systems leads.

Home robotics has been "5 years away" for decades. 

Hopefully this time it's right. 

(And I can get a Turkey time assistant soon!)

🦃

Google just released Nano Banana Pro. 

AI-generated images that are almost impossible to distinguish from real photos.

Same day, they released SynthID. 

A tool to detect if images were created by Google AI.

Nano Banana Pro (technically Gemini 3 Pro Image, but everyone's calling it Nano Banana) has three major upgrades.

First, it actually renders text correctly in images. Previous models butchered spelling and made fonts look like alphabet soup. Nano Banana Pro generates clean, legible text in multiple languages. Posters, mockups, designs that don't embarrass you.

Second, it blends up to 14 images while maintaining consistency of up to 5 people. Turn sketches into photorealistic products. Combine multiple reference images into cohesive compositions. Keep brand consistency across touchpoints.

Third, it taps into Google Search's knowledge base for factually accurate visuals. Need an infographic about solar energy or a recipe visualization? It pulls real-world info to create context-rich images.

SynthID embeds imperceptible digital watermarks into all AI-generated images from Google. Invisible to humans, detectable by technology.

Designed to survive modifications. Cropping, filters, compression. Watermark persists.

Google's keeping visible watermarks (Gemini sparkle) on free and Pro tier images. But removing them for Ultra subscribers and AI Studio users who need clean visuals for professional work.

Gemini can check the embedded SynthID watermark and let you know if the image was AI generated.

Starting with images, expanding to audio and video soon.

Unfortunately, this only works for Google AI-generated content. If someone uses Midjourney, DALL-E, Flux, or any other tool, SynthID can't detect it. But I think it's a step in the right direction.

With more and more realistic gen AI outputs hitting the market, are we going to be able to solve this growing verification problem? 

Photo credential: Google

Instead of asking one LLM your question, ask four at once.

Andrej Karpathy just released LLM Council.

A weekend open source project. 

And in his words - 99% vibe coded.

Here's how it works:

Stage 1: Your query goes to GPT-5.1, Gemini 3 Pro, Claude Sonnet 4.5, and Grok 4 simultaneously. You see all four responses side by side.

Stage 2: Each model reviews the other models' answers. Their identities are anonymized so they can't play favorites. They rank each response for accuracy and insight.

Stage 3: A "Chairman" model (you pick which one) compiles all responses and cross-reviews into a single final answer.

It's a simple local web app. Looks like ChatGPT. Uses OpenRouter to route queries to multiple providers.

Karpathy's use case: reading books with LLMs and wanting to evaluate responses side by side.

But you can imagine this trend picking up steam. 

As model count grows (GPT, Claude, Gemini, Grok, Llama, Mistral, DeepSeek, etc.), single-model answers become less defensible.

Why trust one model's interpretation when you can get consensus from four?

While this won't turn into a product / be supported, the concept might stick around.

Multi-model querying becomes easier as OpenRouter and similar aggregators normalize API access across providers.

And the cost math changes when models get cheaper and faster. Asking four models instead of one becomes reasonable.

Does asking multiple models add more noise to queries, or do you think this has a chance to be useful?

Link to Github Repo: https://lnkd.in/gr9meQFg

Photo credit: same link above (LLM Council)

Salesforce estimates AI will influence $73 billion in global sales this holiday season.

Up from $60 billion last year.

Amazon's Rufus now remembers your family. Tell it you have four kids who like board games, and it retains that context for future recommendations.

OpenAI launched instant checkout in ChatGPT. You can buy from Etsy, Glossier, Skims, Spanx without leaving the app. Walmart deal is live too, but limited to one item at a time.

Google introduced an AI agent that calls local stores to ask if products are in stock. It discloses it's an AI caller. Stores can opt out.

Amazon added an "auto buy" button. Set a price alert, and Rufus automatically purchases when the price drops. You get notified after the order is placed with a window to cancel.

Google's "buy for me" does the same through Google Pay. Works with Wayfair, Chewy, Quince, some Shopify merchants.

We're seeing a potential usage shift from "AI helps you find things" to "AI buys things for you".

Some other takes are more measured. 

Impact will be "relatively limited" this year. 
Not every site has useful tools, not every shopper is willing to try them.

"Customer behavior takes a long time to change."

One thing is for sure, we will be beta testers for AI shopping assistants this holiday season. 

Can you think of useful ecommerce functionality that would cause these agents to gain more traction? Increase conversion rates?

Need help advancing your AI or HPC projects in a meaningful way?

Maybe you just need to know how to get started?  We will help you.

Go to https://lnkd.in/gpEYm5iQ

Click Get Started

And be sure to apply today!

Don't worry about if you think your project is right for this. Just apply.
We are here to help, not judge. :)

ThisWay Global is your AI safe space.

https://lnkd.in/gvM9ExHN

A user posted a screenshot showing Google's Nano Banana Pro solving

A math problem correctly in their actual handwriting.

"Students are going to love this"

The comment section: "Students are going to learn nothing and just have AI do their homework."

Nano Banana Pro (Gemini 3 Pro Image) can now take a picture of a handwritten question, solve it correctly, and replicate the student's handwriting style.

It's part of Nano Banana Pro's improved text rendering and multimodal understanding. But the application is obvious.

Homework is on borrowed time. 

If the goal is building understanding through practice, does it matter how students get feedback? If AI can show the work in your handwriting, is that different from using a calculator or Wolfram Alpha?

The instinct is to say this is obviously bad. Students will cheat, learn nothing, graduate unprepared.

But we've been through versions of this before. Calculators would ruin math skills. Google would kill memorization. Spell check would destroy writing.

Each time, education adapted. Sometimes slowly. Sometimes poorly. But it adapted.

So either we rebuild verification (proctored assessments, oral exams, project-based learning), or we rethink what we're measuring.

"Can you solve this math problem" becomes less relevant if AI can always solve it faster.

"Can you explain when to apply this mathematical concept and why" becomes more important.

The handwriting replication specifically bothers people because it crosses a psychological line. Typing AI-generated answers feels like using a tool. Handwriting in your style feels almost like identity theft.

Some will argue this removes the struggle necessary for learning. No pain, no gain. Students need to work through problems to build skills.

Others will argue we should focus on higher-order thinking that AI can't replicate. Problem-solving, creativity, judgment under uncertainty.

Both are probably right. And schools will lag behind the technology because institutions move slowly.

My main concern at the moment is teachers. 

Do they have any chance of navigating through this in the short term? 

photo/post credit, twitter - @immasiddx

AWS re:Invent wrapped with a singular message: 

AI agents for the enterprise.

"Kiro autonomous agent" 

Writes code, learns how your team works, and can operate independently. Pretty standard marketing terms these days. 

CTO Werner Vogels gave his final re:Invent keynote after 14 years. Addressed the "will AI take my job" question directly.  

"Maybe. Some tasks will be automated, some skills will become obsolete. Will AI make me obsolete? Absolutely not, if you evolve."

Lyft shared numbers on their Bedrock-powered AI agent handling driver and rider issues. 

87% reduction in average resolution time
70% increase in driver usage this year

On the hardware side, Trainium3 promises 4x performance gains for training and inference with 40% lower energy use. They're already working on Trainium4, which will be compatible with Nvidia chips.

AWS also announced "AI Factories" for private data centers. Companies and governments can run AWS AI systems on-premises. Designed for data sovereignty requirements.

AWS VP Swami Sivasubramanian on where we are: 

"For the first time in history, we can describe what we want to accomplish in natural language, and agents generate the plan. They write the code, call the necessary tools, and execute the complete solution."

The enterprise pitch is clear. AI assistants give way to AI agents. Agents that don't just answer questions but perform tasks, automate workflows, and operate autonomously.

CEO Matt Garman: "This is where we're starting to see material business returns from your AI investments."

Good to see Lyft sharing numbers, I hope 2026 is fueled with additional examples and case studies across the enterprise suite. 

Any examples you're waiting to see?

ChatGPT turned three yesterday.

Before GPT, big US labs released a major model every six months. 

Now? 

One every two weeks.

Big tech capex went from $120-150B/year to ~$400B this year. 

About $220-230B of that is incremental AI spend.

Generative AI revenue hit roughly $60B in 2025. ~230% annual growth. 

API revenue alone grew nearly 300%.
(Go developers go!)

Cursor reportedly hit $1B ARR by late 2025. Fastest SaaS scale-up ever.

~90% of enterprise organizations say they use AI in at least one function. 

Plus employees are already using AI informally on personal devices, outside official workflows. There's a layer of usage inside organizations that doesn't show up in adoption surveys.

Three years in. The spending is real. The infrastructure buildout is real. The productivity gains are concentrated.

Is 2026 the year we see broad productivity wins across the board?

OpenAI is testing ads in ChatGPT.

Spotted in the Android beta with references to

"ads feature", "search ad", and "search ads carousel."

800 million weekly users. Up from 100 million in November 2023 and 300 million in late 2024.

2.5 billion prompts per day. 

18 billion messages per week.

Google knows what you search for. ChatGPT knows what you're thinking about, struggling with, planning, curious about. 

People tell ChatGPT things they'd never type into a search bar.

Which could make the ad experience much more targeted than before. 

After all AI + Context is everything.  So will AI based ads. 

We're already wrestling with verification and whether or not AI sources are accurate. 

What happens when we throw paid ads into the mix? 

Hard to know how the LLMs will respond with weighted responses based on who has the biggest wallet. 

And it remains to be seen if users will tolerate it. ChatGPT's current experience feels like a tool, not a platform selling your attention. 

Ads change that perception.

Would you trust LLM generated ads for products? 

How will this change the entire application experience as a user?

Google released Gemini 3 this week.

Available now in Gemini app, AI Mode in Search, AI Studio, Vertex AI, 

and their new agentic platform Google Antigravity.

Also in Cursor, GitHub, JetBrains, Manus, Replit. 

Gemini 3 tops LMArena at 1501 Elo. Beats everything currently available.

37.5% on Humanity's Last Exam (PhD-level reasoning). 91.9% on GPQA Diamond. 23.4% on MathArena Apex (new state-of-the-art for frontier models).

Multimodal: 81% on MMMU-Pro, 87.6% on Video-MMMU. Can analyze pickleball match videos, decipher handwritten recipes in different languages, generate interactive flashcards from academic papers.

Google Antigravity is their version of Cursor. New agentic development platform where agents have direct access to editor, terminal, and browser. Can autonomously plan and execute complex software tasks while validating their own code.

Powered by Gemini 3's reasoning and tool use. Coupled with Gemini 2.5 Computer Use model for browser control.

The "vibe coding" emphasis: Gemini 3 tops WebDev Arena at 1487 Elo. Exceptional at zero-shot generation, handles complex prompts to render richer, interactive web UI.

54.2% on Terminal-Bench 2.0 (tests ability to operate computer via terminal). 76.2% on SWE-bench Verified (measures coding agents).

The scale they're already at: AI Overviews have 2 billion users monthly. Gemini app surpasses 650 million users per month. 70%+ of Cloud customers use their AI. 13 million developers building with their models.

Sundar Pichai: "In just two years, AI has evolved from simply reading text and images to reading the room."

Google's been behind in the AI race narrative despite having DeepMind, pioneering transformers, and massive distribution. Gemini 1 and 2 didn't capture mindshare like GPT-4 or Claude.

Google is heating up with their recent AI releases. 

The only question is, can they maintain velocity while OpenAI/Anthropic/Microsoft keep pushing?

DeepSeek just dropped V3.2.

Open weights. Matching GPT-5 on reasoning benchmarks. 

The high-compute variant (V3.2-Speciale) surpasses it.

Gold medals at IMO 2025, IOI 2025, and ICPC World Finals 2025. 

From an open-source model.

Some technical details worth noting:

They introduced DeepSeek Sparse Attention (DSA), which reduces attention complexity from quadratic to roughly linear. A "lightning indexer" scores all past tokens, keeps only the top 2,048 most relevant ones per query. The expensive attention computation only runs on that subset.

Their post-training RL compute now exceeds 10% of pre-training cost. That's a significant resource allocation shift toward reasoning capabilities after the base model is trained.

For agentic tasks, they synthesized 1,800+ environments and 85,000+ complex prompts. Code agents, search agents, general agents, code interpreters. The synthetic training data transfers to real-world benchmarks they didn't train on.

The inference cost curve is interesting. At 128K context, V3.2 costs significantly less per token than their previous model. The sparse attention might actually deliver on efficiency claims.

DeepSeek-V3.2-Speciale achieved:
- 96% on AIME 2025
- 99.2% on HMMT Feb 2025
- 2701 Codeforces rating

Still some gaps. Token efficiency lags Gemini-3.0-Pro. They need more tokens to reach the same output quality. World knowledge breadth is narrower due to less total training FLOPs.

But the open-source vs closed-source gap is an interesting topic heading into 2026. 

What does competitive open-source reasoning mean for how companies build AI products?

I feel every part of Grant Lee's post.

It is incredibly difficult to make a product intuitive for every user.

It usually means that an enormous amount of 'editing' is required. 

Most engineers embrace complexity but most users do not.  This is where the role of 'Product' comes in. 

Craig Peters and his team brought all ThisWay Global into one platform Amalgamy.ai.

This was the beginning of important simplification and adoption for data center and cloud operators that our software serves.

Every time I use a product that is intuitive to use it inspires me.

Three buttons to one. That was the entire product strategy.

In 1980, Apple hired a designer named David Kelley to reinvent the mouse. (He later founded IDEO. At Stanford, he was my mentor.)

Xerox's version cost hundreds of dollars and was basically a lab instrument.

Great for researchers. Terrible for everyone else.

Apple's brief was simple: make it cheap, make it tough, make it easy enough that you could explain it without scaring people.

David's team pushed for a single button.

Not because they didn't understand the power of three.

Because they understood the anxiety of zero.

"One button was all most people could emotionally handle the first time they touched a computer."

Not an engineering statement. An empathy statement.

We're in the one-button era of AI right now.

Most people are still at the edge of the pool, wondering if it's safe to get in.

I learned this the hard way at Gamma.

We launched. Got the spike you're supposed to get. Product Hunt trophies. Good press. Traffic.

Then the curve flattened. People tried it, but they weren't telling their friends.

So we did something that felt almost irresponsible:

We stopped chasing users and spent months fixing only the first 30 seconds.

The goal: any normal person lands, types one thing, clicks one button, and sees something undeniably useful.

Only after we nailed that did growth become self-propelling.

That was our one-button moment.

Here's what David's lesson taught me about building AI:

1. Founders underestimate how demoralizing it is for someone to open your product and feel stupid in the first 30 seconds. They blame themselves, not you, then quietly churn.

2. The people who compound your growth aren't debating model benchmarks. They're the PM with eight meetings who needs a deck for the fifth one.

3. Your enemy isn't lack of features. It's intimidation.

Power users will always ask for more knobs, more scripting, more ways to break your guardrails.

That's fine. Just be honest about what’s currently holding you back..

Here's the question I'd ask any builder:

What would David Kelley fight to turn into one button in your product?

Answer that first. The three-button roadmap can come later.

Trump approved Nvidia H200 exports to China on Monday.

Beijing is already discussing how to limit access to those same chips, per the Financial Times.

The U.S. spent years restricting advanced AI chip sales to China. 

Trump reversed course, allowing H200 exports with a 25% fee. Nvidia's stock popped 2% in premarket.

Then China signaled they might not want the chips anyway. Stock gains pared to 0.6%.

Beijing has been pushing domestic firms away from U.S. technology as retaliation for American restrictions. Now they're potentially extending that to chips the U.S. just agreed to sell.

The approval may have limited impact unless Nvidia can also export Blackwell or Rubin chips. The H200 is last-generation hardware.

So we have: U.S. lifts restrictions on chips China may not accept, for technology that's already a generation behind what matters.

The chip trade war has gotten strange. Both sides are now restricting access to things the other side was already restricted from having.

Nvidia is up ~40% this year regardless. The China market question keeps getting asked but hasn't actually mattered to the stock.

Does China reverse course and start buying Nvidia chips?  What will that do to Nvidia's trajectory?

OpenAI released their State of Enterprise AI report. 

The aggregate numbers are interesting. 

The case studies are more interesting.

Intercom: 53% of calls now resolved by AI. Hundreds of millions in annual savings.

Lowe's: Customers who use their AI assistant convert at more than double the rate. 1 million questions a month.

Indeed: Job seekers using AI are 38% more likely to get hired. Applications happen 7x faster.

BBVA's legal team: 9,000 queries automated. 26% of their annual savings target hit from one deployment.

Oscar Health: 58% of benefits questions answered instantly without human escalation.

Moderna: Target Product Profile development went from weeks to hours.

Seem to be a lot of "AI changes what's possible to measure and optimize."

Intercom got data on every customer interaction that humans couldn't capture at scale.

Indeed surfaced which job seekers were actually more hirable.

Lowe's created a conversion signal that didn't exist before.

Maybe we start adding to the ROI conversation capabilities that weren't previously feasible.

Nvidia released Nemotron 3 Nano. 

"The most efficient open models for agentic AI."

Some specifics worth noting.

The Nano model is 3.2B active parameters, 31.6B total. 

It outperforms GPT-OSS-20B and Qwen3-30B on popular benchmarks while activating less than half the parameters per forward pass.

On a single H200: 3.3x higher inference throughput than Qwen3-30B, 2.2x higher than GPT-OSS-20B.

Context length up to 1M tokens.

The architecture is a hybrid Mamba-Transformer mixture-of-experts. 

Alongside the model release, Nvidia also included: training recipe, 2.5 trillion tokens of Common Crawl data, 428 billion code tokens, synthetic datasets, SFT data, RL data.

Not just weights. The whole pipeline.

Super and Ultra versions are coming. Those will have multi-token prediction and NVFP4 training. Nano is the smallest; Ultra is positioned as state-of-the-art reasoning.

Nvidia's strategy leans into selling both the chips and now optimized models to run most efficiently on those chips. 

Open weights help adoption. Adoption drives chip sales.

When your chip business prints money, giving away the software makes sense. 

Nvidia is building the default stack.

This is an outstanding opportunity for startups. 
We were part of the program in 2021 and it forever improved our company.

Iran Karimian is incredible to work with!

Head of Accelerator & Startup Ecosystem, Canada at Google

The holidays came early! 🎁

I'm thrilled to announce that applications are NOW OPEN for the 2026 Google for Startups Accelerator: Canada program!

📍 Based in Canada?
💻 Tech Startup?
🚀 Ready to take your startup to the next level with exclusive Google, Google DeepMind and Google Cloud resources?

This is your chance to join a global network of tech innovators and take your startup to the next level. 🔗 Apply here → g.co/acceleratorcanada

🔎To learn more, check out the article below or join us for the Google for Startups Accelerator: Canada Info Session on December 17th! Save your spot today → https://lnkd.in/gU8X43P6

#AcceleratedWithGoogle

French version below “Google for Startups Accelerator: Canada is by far one of the best programs we’ve participated in. We’re grateful to Google mento

Open article: The 2026 Google for Startups Accelerator: Canada program is now accepting applications!  by Iran Karimian • 4 min read

The 2026 Google for Startups Accelerator: Canada program is now accepting applications!

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