Artificially Intelligent Marketing

What will AI actually change for marketers this year? Paul and Martin share their predictions for 2026 — from agentic Chrome to buying products without ever seeing them.

Welcome to the first episode of 2026. Paul and Martin deliver their annual predictions, covering everything from Google's inevitable agentic browser launch to the rise of in-chat purchasing, advertising inside AI conversations, and whether agency headcount will survive the year. This episode cuts through the hype to focus on what marketers should genuinely prepare for — and what they can safely ignore.

Key Takeaways

• Agentic browsers are coming: Google will likely launch a Gemini-powered agentic Chrome experience in 2026, following Perplexity's Comet and Claude for Chrome. Marketers who learn to brief browser agents now will unlock significant productivity gains.
• GEO interest will surge: Generative engine optimisation will become a mainstream concern as AI chatbot referral traffic grows. Expect platforms to develop better measurement tools, though the fundamentals of quality content remain unchanged.
• No major model number jumps: Neither OpenAI, Google, nor Anthropic will release GPT-6, Gemini 4, or Opus 5 this year. Instead, expect incremental improvements focused on computer use and agentic capabilities.
• In-chat purchasing arrives: Google's Agent Payments Protocol and Microsoft's Copilot Checkout signal that buying products directly within AI conversations will become standard by year-end.
• Ads enter AI chatbots: OpenAI is already testing ad formats internally. Expect sponsored content within conversational AI, though platforms must tread carefully to preserve user trust.
• Anthropic launches agent pricing: A dedicated agent subscription plan for teams — potentially $2,000+ per month — will emerge as the major AI labs seek higher-value enterprise revenue.
• Meta productises Manus: Following Meta's acquisition, expect agentic customer service bots via WhatsApp and Messenger, capable of handling orders, returns, and bookings.
• UK agency headcount may fall: The IPA's agency census showed 26,787 employees in 2024. Increased efficiency through AI could reduce that figure, though demand for AI-literate talent will grow.

What to Do Now

• Practice with browser agents: Try Claude for Chrome or Perplexity Comet now. Document your repetitive tasks step-by-step to prepare for more capable agentic tools.
• Track AI referral traffic: Monitor traffic from ChatGPT, Gemini, and Perplexity in your analytics. Even 1% today will grow significantly.
• Prepare for in-chat commerce: Ensure your product information is machine-readable with clear names and structured data that AI can parse and recommend.
• Budget for AI advertising: Reserve experimental spend for AI chatbot ad formats as they launch — early movers may find arbitrage opportunities.
• Focus on first-party data: As AI assistants build deeper user profiles, your own customer data becomes more valuable for personalised campaigns.

Mentioned in This Episode

• Platforms/Features: Claude for Chrome, Perplexity Comet, Claude Cowork, Google Agent Payments Protocol, Microsoft Copilot Checkout, Manus, Do Anything
• Companies: Google, Anthropic, OpenAI, Meta, Microsoft, PayPal, Shopify, Stripe, DeepSeek
• References: IPA Agency Census 2024

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The AI model race intensifies as OpenAI rushes GPT 5.2 to market, whilst marketers discover a surprisingly effective shortcut to AI search visibility.

This week, we analyse OpenAI's scramble to compete after Google's Gemini 3 Pro and Anthropic's Claude Opus 4.5 threatened their dominance. We examine GPT 5.2's contested benchmark scores and whether the rushed release has created more problems than it solved. We also explore the resurgence of 2009-era SEO tactics for AI visibility, revealing how "best of" listicles are gaming generative search results—and how long that window might stay open. Plus: OpenAI's enterprise adoption report, Gemini's new native audio translation, and Martin's bricked Limitless pendant.

Key Takeaways

• OpenAI's defensive launch: GPT 5.2 achieved impressive benchmark scores (53% on ARC-AGI vs 37.6% for Claude Opus), but user reports suggest degraded performance on real-world tasks, with concerns about benchmark optimisation over practical utility.
• AI search visibility follows old playbooks: Publishing "best of" listicles with your company ranked first is proving remarkably effective, with results appearing within 1–2 weeks rather than the 3–12 months typical for SEO.
• Enterprise adoption accelerates: OpenAI reports 8× growth in ChatGPT Enterprise usage, with workers reporting 40–60 minutes of daily time savings. 87% of IT workers report faster issue resolution, 85% of marketers report faster campaign execution.
• Model providers face different futures: OpenAI pursues consumer markets through a Disney partnership for Sora-generated content, whilst Anthropic and Mistral focus explicitly on enterprise solutions, such as on-prem deployments.
• Translation goes universal: Gemini's native audio model now supports real-time translation through any Bluetooth earphones, removing hardware restrictions that previously limited adoption.
• Projects over prompts: OpenAI reports 19× increase in custom GPT and project usage, indicating a shift from casual querying to repeatable workflow automation.

What to Do Now

• Test GPT 5.2 cautiously: If you have access, compare outputs against 5.1 for your specific use cases before switching workflows. Early reports suggest mixed results.
• Deploy listicle strategy immediately: Create "best of" articles in your category with your company ranked first. Include detailed comparison tables. Speed matters—this window may close as AI providers refine their models.
• Monitor your AI visibility: Check how ChatGPT Search, Gemini, and Claude answer queries about your product category. Track changes weekly to understand which content formats they prioritise.
• Audit your project setup: If you're using ChatGPT Enterprise, review whether your projects contain too much general context. More focused, use-case-specific projects typically perform better.
• Invest in genuine reviews: As AI providers wise up to self-published listicles, review platforms like Clutch, G2, or Google Reviews will likely become more important for AI search visibility.

Mentioned in This Episode

• Platforms/Features: GPT 5.2, GPT 5.1 Pro, Gemini 3 Pro, Claude Opus 4.5, Nano Banana, ChatGPT Search, Notebook LM, Sora, Microsoft Copilot Pro, Manus
• Companies: OpenAI, Google, Anthropic, Disney, Mistral AI, Limitless, Meta, HSBC, Boston Dynamics, Blend B2B
• Tools: Ahrefs, Clutch, G2
• References: ARC-AGI benchmark, OpenAI Academy, Ethan Mollick's prompt research, Moonshots podcast

Three major model releases in a single week, ads arriving in AI search results, and a landmark acquisition that signals where SEO is heading. Paul and Martin cut through the noise.

This week saw an unprecedented flurry of model releases: Google's Gemini 3 Pro stormed the benchmarks, only for Anthropic's Opus 4.5 to arrive days later with superior coding performance. We unpack what these advances mean for marketers, from Nano Banana Pro's near-production-ready image generation to Opus's orchestration capabilities for multi-agent workflows. Beyond the model wars, we examine Gartner's 2026 predictions—including a warning about critical thinking atrophy—and debate the implications of ads appearing in AI overviews. Adobe's $1.9 billion acquisition of SEMrush closes the episode, raising questions about the future of generative engine optimisation.

Key Takeaways

• Gemini 3 Pro benchmarks: Scored 45.8% on Humanity's Last Exam versus the previous high of 26.5%, and doubled ScreenSpot Pro accuracy to 72%—critical for agent-based computer use.
• Opus 4.5 efficiency: Achieved 81% accuracy on software engineering tasks using 12,000 tokens, while Sonnet 4.5 needed 22,000 tokens for 77% accuracy.
• Nano Banana Pro: Now handles up to 5 consistent characters and 14 objects in a scene, making product imagery workflows increasingly viable.
• Gartner's lazy thinking warning: Predicts 50% of organisations will require AI-free skills assessments by 2026 as critical thinking atrophies.
• B2B procurement shift: Gartner forecasts 90% of B2B buying will be AI-agent intermediated by 2028, pushing $15 trillion through agent exchanges.
• Ads in AI search: Google has begun placing sponsored links in AI overviews; reports suggest ChatGPT may follow.
• Adobe acquires SEMrush: The $1.9 billion deal validates generative engine optimisation as a strategic priority.

What to Do Now

• Test your own use cases: Benchmarks are directional; run Gemini 3 Pro and Opus 4.5 against your specific workflows before switching daily drivers.
• Experiment with Nano Banana Pro: If you need consistent product imagery, trial multi-character and multi-object prompts to assess production readiness.
• Protect critical thinking: Build review processes where humans form independent judgments before consulting AI—especially for strategic decisions.
• Prepare for agent-readable content: Convert key product documentation from PDFs to markdown to ensure AI procurement agents can parse your information.
• Monitor AI ad placements: Track emerging ad formats in Google AI overviews and prepare to test early for potential first-mover advantages.

Mentioned in This Episode

• Models: Gemini 3 Pro, Nano Banana Pro, Opus 4.5, Sonnet 4.5, Haiku 4.5, GPT 5.1, DeepSeek 3.2
• Platforms/Features: AI Overviews, AI Mode, Veo 3.1, Claude Code
• Companies: Google, Anthropic, OpenAI, Adobe, SEMrush, Microsoft, HSBC, McKinsey, Gartner
• Benchmarks: Humanity's Last Exam, ScreenSpot Pro, VendingBench, SWE-bench, GPQA Diamond

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What's driving Google and Meta's record earnings? Hint: it's AI, not traditional ads.

In this episode, Paul and Martin unpack the latest earnings calls, HubSpot's strategic acquisition of X Funnel, and then venture into scenario planning for marketing's future. From business-as-usual to AI agent swarms running entire organisations, we map out what you should watch for and what you should do right now.

Key Takeaways

• Meta's AI-powered advertising suite is now handling $60 billion in annualised ad spend. That's real money, real results: 5% more time on Facebook, 10% on Threads, 30% growth on Instagram video.
• Google posted its first $100 billion quarter, driven by AI features in Search and YouTube. YouTube Shorts now earn more per watch hour than traditional in-stream ads.
• HubSpot acquired X Funnel to double down on Generative Engine Optimisation (GEO) — ensuring your brand shows up in AI chatbot answers. But don't chase FOMO. Focus on brand-building and content quality; those work across all search channels.
• Five plausible futures for marketing:
  1. Business as usual
  2. Niche AI agents handling specific tasks
  3. Hyper-personalisation at scale
  4. AI agents driving strategy
  5. Fully autonomous agent swarms Which scenario emerges depends on technology progress, regulation, and how quickly organisations can rethink workflows.
• The real bottleneck isn't technology — it's human change. AI-first startups will outcompete legacy organisations unless incumbents commit to wholesale rethinking. Disruption ahead.
• Customer preference trumps efficiency. If your audience values human interaction and human-crafted outputs, that changes everything. Personalisation won't win if customers prefer the human touch.

What to Do Now

• Invest in AI skills training for yourself and your team. The saying holds: marketers aren't replaced by AI; they're replaced by marketers who use AI.
• Get your customer data in order. Clean, structured, first-party data is essential — especially if privacy regulations tighten. If an AI agent needs to draw insights, it needs good data to work with.
• Establish an AI ethics and usage charter. Give customers confidence. Guide employees on responsible AI use.
• Build human-in-the-loop QA processes. Identify where you can safely automate and where humans must review. As niche agents emerge, you'll know exactly where to deploy them.
• Run scenario planning for your business. Use ChatGPT or Claude with your business context to model which scenario matters most to you. Don't just accept our five; build your own.

Mentioned in This Episode

• Meta: AI recommendations; Vibes (image generation); Advantage+.
• Google: AI Overviews; Performance Max; YouTube Shorts.
• HubSpot: X Funnel acquisition; Loop Marketing; GEO.
• Tools: Zapier; Claude; ChatGPT; Canva; HubSpot; Salesforce.
• Reference: The Last Economy by Emad Mauck.

New episodes every week on Artificially Intelligent Marketing. For questions, use cases, or scenario disagreements, find us on LinkedIn or reply to our latest episode post.

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What actually matters now in AI for marketers—beyond the hype. Paul and Martin unpack the week’s launches from Anthropic and Google, new Microsoft–Claude tie-ins, and the UK government’s civil-service AI literacy push. They also tackle the practical question: should you chase “AI SEO” today, or double-down on timeless content and distribution?

Top takeaways

• AI visibility ≠ old SEO. Generative search is highly personalised; “rank tracking” for LLM answers is shaky. Invest in high-quality, well-structured content and robust distribution over gimmicky “get featured in ChatGPT” checklists.

• Anthropic’s direction: Haiku 4.5 offers fast, low-cost coding and general tasks; Claude Skills hint at SOP-driven, tool-aware assistants and a future marketplace.

• Enterprise shift: Claude is deepening its Microsoft 365 integration (SharePoint, Outlook, OneDrive, Teams), making business context more accessible to assistants.

• Video creation reality: Google’s Veo 3.1 and Flow expand control (reference frames, storyboard timelines). Quality still depends on human editing judgement—AI is an ingredient, not the whole recipe.

• AI Studio cleanup: Google has unified its playground and simplified keys/settings, making it easier for newcomers to explore models and tune temperature/controls.

• Public sector skills: The UK’s “One Big Thing: AI for All” aims to boost civil-service confidence via short modules and department case studies—baseline literacy first, then role-specific adoption.

• Training that sticks: Start with common foundations (what LLMs are/aren’t), then move to function-level workflows. Create “champion” groups and a light governance rhythm so updates translate into process change.

Chapters

00:00 Harrison recap; AI in sales needs thoughtful application, not blind scale.

03:00 Brand presence in LLM answers: content + distribution over hacks.

10:00 Why LLM personalisation breaks classic rank tracking.

15:00 Anthropic: Haiku 4.5 and Claude Skills; SOPs for agentic work.

21:00 Claude ↔ Microsoft 365: the enterprise context advantage.

25:00 Google Veo 3.1 & Flow: creative control vs. “one-click” video.

34:00 Google AI Studio: unified playground; why marketers should tune temperature.

38:00 “Zero-to-magic” tease; what a no-code app path might mean.

40:00 UK “One Big Thing—AI for All”: literacy at scale; early gov use cases.

42:00 Good AI training design: baseline, then function-specific change.

55:00 Reality check: useful today, but still needs steering and review.

Practical guidance

• If you’re a marketer: Treat AI outputs as drafts. Edit for voice, accuracy, and distinctiveness. Measure distribution as much as production speed.

• If you’re a leader: Provide one sanctioned assistant (Copilot/Gemini/Claude), set guardrails, and fund a small “AI council” to turn releases into workflow changes.

• If you’re experimenting with video: Use Flow (or your editor) to storyboard, trim, and mix assets. Don’t ship raw generations.

Mentioned in this episode

• Anthropic: Haiku 4.5; Claude Skills; Microsoft 365 integrations

• Google: Veo 3.1; Flow storyboard editor; AI Studio updates

• UK Government: “One Big Thing 2025—AI for All” initiative

• Discussion: training that works vs. one-and-done workshops

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🎙️ Artificially Intelligent Marketing: Episode 47 – Harrison Wade on “Why AI is NOT the Easy Button”

Welcome back to Artificially Intelligent Marketing! In this episode, Paul Avery is joined by special guest Harrison Waid, co-founder of Succession. Harrison brings hands-on experience from the life sciences sector, revealing how his team is redefining sales and marketing through AI innovation.

From Business to Science Harrison shares his journey from software sales to leading go-to-market strategies for life science firms—where tools common in SaaS are still a rarity. He explains how this gap creates both challenge and opportunity for innovation.

AI in Sales Enablement & Marketing Discover why sales teams often lag behind marketing in tech adoption, and why tech-fluent, AI-first team members are essential for orchestrating automated workflows that actually deliver results.

What Works—and What Doesn’t Harrison details how his team uses robust context, training, and tools like Clay to supercharge outreach—highlighting the danger of treating AI like an “easy button.” A 27-page context document? Essential, not overkill.

Real AI Workflows, Not Magic Forget AGI hype—this episode is packed with practical advice on using AI for prospect research, workflow automation, and creative outreach. Think personalised videos, Suno-generated songs, and campaigns that cut through the noise.

Building the Future: Micro-Apps & Vibe Coding Paul and Harrison explore how tools like Replit and Lovable let marketers build lead magnets and custom web apps without coding skills—ushering in a new era of “vibe coding.”

Human Creativity Still Wins AI can scale output, but human creativity drives connection. Harrison argues that empathy, emotion, and storytelling remain the ultimate differentiators in AI-driven marketing.

Arms Race or Open Water? Will personalised AI content saturate the market? Harrison says no—most teams lack the expertise or drive to execute at scale. There’s still huge opportunity for those willing to experiment.

Actionable Advice

• If you love tinkering, start small and iterate.
• If you want results, partner with builders who do.
• For life sciences growth, Succession Bio can help scale your top-of-funnel efforts.

Key Takeaways

• AI isn’t the easy button—human creativity and context win.
• Automation is messy—curiosity and patience pay off.
• Personalisation breaks through—even songs can sell.
• The emerging “conductor” role is key: orchestrating tools to outcomes.

Connect & Challenge Want to stand out? Combine creativity and AI—send us a Suno-generated song about this episode using the YouTube transcript!

Contact For top-of-funnel support or AI workflow advice, reach out to Harrison at Succession Bio.

Subscribe to Artificially Intelligent Marketing for stories, workflows, and insights on AI in marketing—new episodes every week.

In this episode of Artificially Intelligent Marketing, Martin Broadhurst and Paul Avery reunite to explore how AI has transformed marketing over the past 18 months. They cover reasoning models, agentic automation, Microsoft Copilot’s evolution, open vs closed-source AI, and the rise of AI-powered hardware—sharing real-world insights and examples from their work.

Major Evolutions in AI for Marketers

• Reflection on the rapid progress of AI tools and models
• Overview of major shifts since the last episode
• How marketers are adapting to new AI capabilities

AI Reasoning Models

• Difference between chain-of-thought prompting and modern reasoning models
• Improvements in accuracy and reduced hallucinations
• Trade-offs between speed and reasoning depth
• Groq CEO’s insights on the value of ultra-fast inference

AI Tools Adoption and Platform Maturity

• Microsoft Copilot’s leap from basic to highly capable
• Key tools: Researcher agent, Analyst tool, and Copilot Studio
• Integration across Microsoft 365 (SharePoint, OneDrive, Teams)
• Comparisons with Google and OpenAI’s platforms
• Ongoing confusion over pricing and value

Model Selection: The “Model Roundabout”

• Recent advances in GPT, Claude, Gemini, and open-source models
• Balancing reasoning and instant modes
• Common use cases: coding, summarisation, planning, and copywriting
• Quirks such as GPT-5’s writing tone and output style
• Tips for reducing hallucinations and improving reliability

Open vs Closed Source AI Debate

• Rise and stall of open models like DeepSeek and Llama 4
• Meta’s shift from open development to proprietary AGI
• Open source’s future in experimentation rather than frontier innovation
• Market consolidation, privacy, and trust concerns

AI-Integrated Hardware and the Attention Economy

• Growth of wearable AI, e.g. Meta’s Ray-Ban smart glasses
• Privacy and social implications of constant recording
• Adoption driven by convenience and content habits
• Meta’s competing aims: productivity vs attention monetisation

Agentic Progress: AI Agents and Automation

• “Agentic AI” explained: systems acting autonomously to complete goals
• From document retrieval to full workflow automation
• Tools like Make.com, Zapier, and N8N enabling marketers
• Claude Code as an advanced example of self-directed agents
• Use cases: automated slide decks, proposals, and scheduled reporting

MCP (Model Context Protocol) Connectors

• Overview of MCP for connecting LLMs to CRMs and cloud tools
• Martin’s experience linking Claude to HubSpot and Google Workspace
• Examples of AI updating pipelines and deal notes automatically
• Benefits balanced against setup complexity

Current State of AI for Marketers

• Honest look at AI-generated content and “work slop”
• AI as a speed and productivity enhancer, not a replacement for experts
• Advances in visual and video generation:
  • Faster, more consistent imagery (Midjourney, DALL·E 3, Nano Banana)
  • Real-world use in proposals, events, and social media
• Emerging video models (Veo 3, Sora 2, Kling) offering realism and sound
• Reflection on low-quality AI output and the lasting importance of trusted brands

Intro In this week's episode, Paul and Martin discuss the current limitations of AI for data analysis, Google's AI overview feature, and predictions about AI's impact on jobs. They also cover the latest news and updates from Anthropic, OpenAI, ElevenLabs, and a promising new AI-powered TV show creation tool called Showrunner.

Data Analysis with AI Falls Short

Martin shares his experience using ChatGPT for data analysis, highlighting flaws in extracting accurate insights.

Issues with Google's AI Overview Feature

While widely rolled out, Google's AI overview feature has produced bizarre and inaccurate search summaries.

Anthropic Introduces Function Calling for Claude

Anthropic has made function calling available for its AI assistant Claude, allowing integration with external data sources.

Anthropic Enables Exploration of Claude's Inner Workings

The team at Anthropic mapped out the inner workings of the Claude AI model to better understand its behaviour.

GPT-4 Now Available to All ChatGPT Users

OpenAI has made GPT-4 available to all users of its ChatGPT platform, along with other new features.

ElevenLabs Launches AI-Powered Sound Effects Tool

ElevenLabs introduced a new tool that generates realistic sound effects from text descriptions.

The Simulation Unveils AI-Generated TV Shows

A startup called The Simulation showcased Showrunner, a tool for creating AI-generated TV shows and videos.

Survey Reveals Limited Use of Generative AI Tools

A Reuters Institute survey found that while awareness of AI tools is high, frequent use remains rare.

Apple Rumored to Unveil Siri 2.0 with AI Capabilities

Apple is expected to announce a new version of Siri with enhanced AI capabilities at its upcoming WWDC event.

Anthropic Chief of Staff Predicts Job Obsolescence

An Anthropic executive wrote an article predicting that AI will make most jobs obsolete within the next 5 years.

After an 8-week break, "Artificially Intelligent Marketing with Paul and Martin is back.

Here's a quick recap of recent news: OpenAI's Figure 1 robot now integrates GPT-4, and Devin, a new AI software engineer, was previewed. Claude 3 Opus briefly topped the LLM performance chart, Heygen updated its AI-created user-generated content, and Meta AI released Llama 3. Amazon launched Q for enterprises, and Microsoft is working on the Frontier model LLM named MAI-1. OpenAI's Sora video model produced its first commissioned music video, DeepMind released AlphaFold 3, and AI-enabled hardware like the Humane Pin and Rabbit R1 received poor reviews.

GPT-4o: A Unified Model for Text, Audio, and Video Inputs and Outputs

OpenAI's GPT-4o (Omni) is a versatile new AI model that processes text, audio, and video inputs and generates outputs in multiple formats. This unified approach streamlines workflows and ensures consistency in data processing. Currently, the API supports text and image inputs, with audio capabilities coming soon, making it a comprehensive tool for marketers. Learn more about GPT-4o.

Enhancements to Data Analysis in ChatGPT

ChatGPT has introduced new data analysis improvements, allowing users to upload files directly from Google Drive and Microsoft OneDrive, interact with tables and charts in an expandable view, and customise and download charts. These features, available in the GPT-4o model for ChatGPT Plus, Team, and Enterprise users, enhance efficiency and streamline workflows, making data handling simpler and more engaging.

Google Announces Cutting-Edge AI-Powered Features at I/O 2024

At I/O 2024, Google unveiled new AI features and integrations, focusing on enhanced information organisation and long context management with Gemini. Integrations in Gmail, Google Photos, Google Workspace, and Google Search improve user experiences. Gemini Pro's long context management supports up to 1 million tokens, benefiting research and content analysis. Experimental apps like NotebookLM and AI Studio offer researchers, students, and analysts innovative tools.

ElevenLabs Launches AI Sound Effect Generator

ElevenLabs expands its AI technology to generate realistic sound effects from text prompts. The tool excels at natural and atmospheric sounds but has limitations with electronic noises. While not perfect, it represents a significant advancement in AI-generated audio for immersive digital content.

Midjourney Introduces Consistent Characters Feature

Midjourney's new feature allows users to maintain character consistency across multiple AI-generated images. By referencing an original character image, the AI can generate variations with adjustable resemblance. This advancement opens new possibilities for visual storytelling and marketing campaigns, but raises ethical and copyright concerns.

EU Passes Groundbreaking AI Act

The European Parliament has passed the AI Act, the world's first comprehensive AI regulation. It categorizes AI products by risk level, promoting responsible AI development and use. The act emphasizes compliance, ethics, and transparency, reshaping the regulatory landscape for marketing professionals.

Anthropic Launches Fast and Affordable Claude 3 Haiku API

Anthropic's Claude 3 Haiku model offers high-speed processing and cost-effectiveness for enterprise AI workloads. It can analyze large volumes of text and images at a fraction of the cost of competitors. The model's affordability and performance make it a significant advancement for AI capabilities.

Sam Altman Predicts AI Will Take Over 95% of Marketing Agency Tasks

OpenAI CEO Sam Altman forecasts that AGI will handle most tasks currently done by marketing agencies and creative professionals within five years. While AI integration in marketing is already underway, this prediction raises concerns about job security, data privacy, and intellectual property.

IBM Announces Layoffs in Communications and Marketing Divisions

IBM initiates layoffs in its communications and marketing staff as part of a workforce rebalancing effort. The company focuses on high-demand areas like AI and hybrid cloud technologies. These layoffs highlight the growing impact of AI on marketing and the need for professionals to adapt to emerging technologies.

Meta's Significant Investment in GenAI Infrastructure

Meta takes a leap in AI with its GenAI infrastructure, powered by cutting-edge NVIDIA GPUs. The company plans a substantial expansion, aiming for 600,000 GPU units by 2024. This investment signals a transformative shift in AI capabilities and highlights the importance of leveraging advanced technology for market differentiation.

Devin: The First Autonomous AI Software Engineer

Devin, developed by Cognition, is an AI designed to handle complex software engineering tasks autonomously. While not perfect, Devin's performance on coding benchmarks marks a significant improvement over previous models.

Figure and OpenAI Collaborate on AI-Powered Humanoid Robots

Figure partners with OpenAI to enhance its humanoid robots with advanced AI models. The robots demonstrate autonomous task completion in controlled environments.