AI's 2026 Roadmap Just Leaked

The New AI Power Panel: Why These Voices Matter Now

Forward Future has quietly turned into one of the internet’s most influential AI war rooms. Host Matthew Berman now speaks daily to an audience of more than 500,000 builders and decision-makers across YouTube, X, and his Forward Future newsletter, stitching together model releases, chip shortages, and policy fights into a single, very actionable feed. His Forward Future Live streams function less like YouTube shows and more like real-time strategy briefings for people betting their careers on AI.

The 12/12/2025 lineup looks like a snapshot of the AI stack in 2026. OpenAI researcher Tejal Patwardhan represents the foundational model layer that still sets the pace for everyone else. Fabian Stelzer, Co-Co-Founder of Glif, brings the agent layer that turns those models into autonomous workflows. On top of that, Christina Cacioppo and Jeremy Epling from Vanta anchor security and compliance, while Spenser Skates, CEO & Co-Co-Founder of Amplitude, closes the loop with product analytics and user behavior data.

Taken together, these guests cover the full route from GPU to business KPI. OpenAI defines capabilities and constraints. Glif experiments with how far you can push agents before they break—or break things. Vanta answers whether any of this can pass a SOC 2 audit or a bank’s risk committee, and Amplitude tracks whether customers actually use the AI features that teams keep shipping.

All of this lands in a volatile, post-GPT-5.2 moment. Model performance jumped again, but the market reaction shifted from pure awe to hard questions about cost, reliability, and control. VCs now close “AI-only” deals in under 15 minutes, but those checks increasingly demand real deployment stories, not just benchmark charts.

Enterprises, meanwhile, stopped treating AI as an experiment. Fortune 500 teams now wire GPT-class models into customer support, internal search, and analytics, while regulators race to keep up and state-level AI laws stall in court. Berman’s panel sits right at that fault line: the people who build the models, the agents that act on your behalf, the guardrails that keep regulators calm, and the dashboards that prove whether any of this was worth doing.

Glif's Vision: Your AI Agent Workforce Is Here

Glif co-Co-Founder Fabian Stelzer talks about agents the way early web pioneers talked about browsers: as the new runtime, not just another app. Instead of a single chatbot window, he imagines swarms of autonomous agents coordinating across APIs, data warehouses, and SaaS tools, handing tasks off like a production line. One agent drafts strategy, another pulls analytics, a third negotiates with vendors or cloud services, all without a human stitching the workflow together.

That shift moves AI from “smart feature” to “orchestrator of work.” Stelzer’s Glif treats agents as composable building blocks—small, specialized processes that can watch events, trigger actions, and call other agents. You don’t just prompt a model; you design a system where agents maintain context over days, remember business rules, and escalate only when confidence drops below a threshold.

For developers, this maps directly onto Matthew Berman’s Vibe Coding Playbook: describe intent, constraints, and desired “vibe,” then let agents handle the wiring. Instead of manually integrating 10 SDKs, a dev defines goals like “monitor churn risk and launch retention experiments,” and an agent graph figures out which APIs to call, which dashboards to update, and when to ping a human in Slack. Coding becomes more about specifying guardrails and less about writing glue code.

Businesses see that as leverage, not novelty. A 10-person startup can spin up an “AI growth team” of agents that: - Scrape competitor changes daily - Auto-generate A/B tests - Push updates into tools like Amplitude and ad platforms

Each agent runs 24/7, costs cents per hour, and logs every decision for compliance and postmortems.

Major platforms are racing to own this layer. AWS executives now talk about agentic workflows in the same breath as custom silicon and vector databases, positioning Bedrock and Step Functions as the backbone for multi-agent systems. Microsoft, Google, and OpenAI are converging on similar stacks: hosted models, memory stores, event buses, and policy engines tuned for agents that act, not just chat.

An agent-first world feels different for creators and companies. Products launch with “assign an agent to this” baked in—research, outreach, QA, finance. Roadmaps start with, “Which agents own this process?” rather than “Which team can spare a sprint?”

OpenAI's Next Frontier: Beyond Foundational Models

Enterprise AI demand inside OpenAI’s dashboards looks less like a curve and more like a wall. Tejal Patwardhan described usage from Fortune 500 clients multiplying quarter over quarter as teams wire GPT-style models into sales ops, customer support, and internal knowledge systems, often via quiet “shadow pilots” before CIOs ever sign off. For OpenAI, those patterns turn abstract research into hard product requirements: reliability, latency, compliance, and deep integration hooks.

Post-GPT-5.2, OpenAI’s research agenda appears to tilt toward self-improving systems rather than just bigger monoliths. Think model fleets that continuously retrain on user-approved data, auto-generate tools, and learn to orchestrate specialized agents. That shift aligns with what Fabian Stelzer is building at Glif, where agent networks already chain models, APIs, and memory; for a taste of that future, see the Glif Official Website.

Patwardhan’s comments hint at a stack where models act as meta-optimizers: they write code, design experiments, evaluate outputs, and fold the best results back into new versions. Self-play transformed game AI; OpenAI now wants self-iteration across research, enterprise workflows, and even product UX. Evaluation becomes as strategic as training data, with synthetic benchmarks and agent-based stress tests running nonstop.

Timelines for AI generating “most new knowledge” by 2028 split the panel. Optimists argue that once models can autonomously propose hypotheses, run simulations, and mine multimodal data, scientific output could spike by 10x in narrow fields like materials science or drug discovery. Skeptics counter that bottlenecks—lab validation, regulation, and institutional conservatism—will cap impact long after models surpass human literature review.

Forward Future guests flagged three constraints that make 90% AI-generated knowledge in 2–3 years look aggressive: - Hardware and energy ceilings - Legal and safety guardrails - Human trust and adoption rates

For businesses planning 2026–2028 roadmaps, OpenAI’s trajectory forces hard choices. Betting only on static foundational models risks obsolescence as competitors embrace agentic, self-improving stacks that can rewire workflows weekly. Savvier teams are budgeting for continuous integration with OpenAI’s APIs, data governance that anticipates automated retraining, and org charts where “AI operations” sits alongside DevOps and security.

Vanta's Warning: AI's Massive Security Blind Spot

Trust breaks faster than any model can autocomplete, and Christina Cacioppo and Jeremy Epling are treating that as AI’s real rate limit. From their vantage point at Vanta, companies aren’t just adopting GPT-class systems; they’re quietly wiring them into customer data, production code, and decision-making without anything resembling a security review.

Rushing to plug models into CRMs, source repos, and payment systems creates a new blast radius. An “innocent” AI assistant that can read Jira tickets, Stripe logs, and Slack DMs becomes a perfect lateral-movement tool once a single API key leaks or an OAuth token gets phished.

Traditional AppSec checklists don’t map cleanly to AI behavior. Models can exfiltrate data through outputs, leak training examples, and infer sensitive attributes from “anonymized” logs. Prompt injection, data poisoning, and jailbreaks aren’t hypothetical anymore; they’re the new SQL injection, and most security teams don’t have playbooks or monitoring for them.

Cacioppo and Epling argue that compliance teams face a double bind: regulators demand control over opaque systems that vendors can’t fully explain. When a model hallucinates a financial recommendation or misroutes PHI, who owns the incident report—the enterprise, the model provider, or the integrator gluing everything together?

Regulation only amplifies the chaos. Companies building nationwide AI products must navigate a patchwork of: - State-level AI bills - Sectoral rules for health, finance, and education - Emerging federal and EU AI frameworks

Blocked or stalled state laws don’t remove friction; they create uncertainty. Teams delay features or quietly geo-fence capabilities because no one wants to ship an AI underwriting tool or hiring screener that might violate a revived statute or a fresh FTC interpretation six months later.

Security, in Vanta’s framing, can’t stay a late-stage checkbox. Treating trust and compliance as first-class product requirements—data minimization, auditable logs for every AI decision, clear model provenance—becomes the only way to keep shipping once regulators, auditors, and customers catch up.

The companies that win the 2026 AI race won’t just have faster agents or bigger GPUs. They’ll have an evidence trail: SOC 2 reports that include AI systems, real-time risk scoring for prompts and outputs, and a story about safety that can survive both a breach and a subpoena.

Data Is King: Amplitude on AI's Real-World Impact

Data, not vibes, decides whether AI is actually working. That’s Spenser Skates’ core argument: if you’re not instrumenting every click, prompt, and completion with a product analytics stack like Amplitude, you’re just guessing. DAUs, retention curves, and funnel drop-offs expose which AI features users adopt, ignore, or actively route around.

Skates pushes companies to track AI usage at the feature level, not as a single blob of “AI engagement.” Teams need to know if an AI writing assistant increases document completion by 20%, or if a support copilot cuts average handle time from 7 minutes to 3. Without that behavioral telemetry, executives only see a line item in the cloud bill, not whether it changed user behavior.

ROI for AI, he argues, lives in a simple chain: model call → user action → business outcome. You measure that by tying AI events to metrics like: - Task completion rate - Time-to-value or time-to-resolution - Conversion, expansion, and churn

If an AI search feature boosts successful queries from 55% to 80% and lifts conversion 5%, that’s bankable ROI, not a demo win.

Data also cuts through the marketing war between ChatGPT, Gemini, Claude, and the rest. Skates’ world cares less about which model scores higher on synthetic benchmarks and more about which one drives more documents shipped, tickets closed, or dashboards created per active user. When you A/B test models behind the same UI, analytics reveal a clear winner in task success and user stickiness.

That telemetry becomes a live scoreboard for the AI platform race. If swapping from one LLM to another quietly drops weekly active usage by 10% or increases error-triggered rage clicks, you know the “cheaper” model just cost you real money. The competitive landscape stops being a hype contest and becomes a dataset.

Continuous iteration sits on top of this measurement stack. Teams that ship AI features weekly can watch cohort charts and pathing reports to see how users adapt, then refine prompts, guardrails, and UX. Skates’ message: the companies that win AI won’t just fine-tune models; they’ll fine-tune behavior, using data as their feedback loop.

The 15-Minute VC Deal: Is AI in a Bubble?

Fifteen-minute term sheets have quietly become the new normal in AI, with seed and Series A rounds closing over a single Zoom call and a shared Notion page. Investors chasing “once-in-a-generation” upside preempt each other with soft diligence, betting that missing the next OpenAI hurts more than backing 10 future write-offs. Co-Founders brag about calendar screenshots where a cold intro turns into a signed SAFE before lunch.

Hyper-accelerated capital has obvious upside. Teams like Glif can staff up engineers, buy GPU time, and ship agentic features months faster than traditional SaaS startups ever did. OpenAI’s Tejal Patwardhan described enterprises that move from pilot to multi-million-dollar contracts in a quarter, which makes speed a rational—if dangerous—default.

Costs stack up just as fast. Christina Cacioppo and Jeremy Epling warned that compliance, SOC 2, and data governance rarely keep pace with 15-minute deals, leaving security as a retrofit rather than a design constraint. Vanta’s growth itself reflects how many AI companies need a trust layer bolted on after the funding sugar high; more details sit on the Vanta Official Website.

Behind the frenzy lies a brutal infrastructure race. Startups burn fresh capital on NVIDIA H100 clusters, scarce A100 leases, or Groq-style accelerators, often committing to multi-year cloud minimums with AWS, Azure, or Google Cloud. Miss a GPU allocation window, and your entire roadmap slips a quarter.

That race extends to people, not just chips. Co-Co-Founders court ex-DeepMind and OpenAI researchers with $500,000+ total comp and equity that assumes decacorn outcomes by 2027. Amplitude’s Spenser Skates argued that data-rich incumbents can outspend early-stage teams on talent and infra, forcing smaller players into narrow verticals or aggressive M&A.

Compressed timelines warp product strategy. Instead of validating use cases with careful analytics, many AI startups launch half-baked copilots, then scramble when retention and daily active usage crater. Boards expect “GPT-4-level” step changes every 6–9 months, even though model training, evals, and safety reviews do not compress on the same curve.

Bubble or not, the market already prices in sci-fi outcomes. If those breakthroughs slip—because chips stay scarce, regulators tighten, or users simply plateau—today’s 15-minute deals could become tomorrow’s long, ugly down-rounds.

The Battle for AI's Soul: Open vs. Closed

Open vs. closed AI no longer looks like a GitHub culture war; it is a fight over who controls cognition at scale. On one side sit centralized stacks like OpenAI, Anthropic, and Google, bundling frontier models, hosting, and safety layers behind APIs. On the other, Meta’s Llama family and a swarm of smaller labs push permissive licenses, local inference, and model weights you can actually touch.

Closed advocates argue that only tightly controlled systems can handle trillion-parameter models, multi-billion-dollar training runs, and safety regimes demanded by regulators. Open-source proponents counter that reproducibility and forkability are the only real checks on concentrated AI power. They point to Llama 3, Mistral, and open DeepSeek derivatives as proof that quality no longer belongs exclusively to sealed labs.

Geopolitics now runs straight through model cards. US startups increasingly talk about “Americanizing” powerful foreign models like DeepSeek: stripping or retraining away CCP-aligned data, adding US legal guardrails, and routing everything through American cloud providers. Washington, Brussels, and Beijing all want frontier AI that aligns with their own laws, languages, and values.

That creates a strange dynamic: open weights trained in China or Europe, lightly adapted by US companies, then re-exported as “safe” enterprise products. Policy hawks worry this still leaks capabilities across borders; open-source advocates argue that trying to put national borders around math will fail, just as it did with strong cryptography in the 1990s.

Underneath the licensing fights sits a deeper philosophical split. Some labs quietly still chase a single, massively capable superintelligence that acts like a global utility: one model, many tenants. Others, including agent-focused platforms like Glif, imagine billions of small, personalized agents tuned to each person’s preferences, data, and risk tolerance.

Developers sit at the fault line. Closed APIs give instant scale, uptime SLAs, and compliance checkboxes that Vanta can audit, but they lock teams into pricing, content policies, and opaque model updates. Open models let engineers pin versions, fine-tune on proprietary data, and run locally or on cheaper GPUs, at the cost of more ops work and security responsibility.

Power distribution in the AI era will likely track this choice. If closed platforms win, a handful of US and Chinese companies effectively become cognitive utilities, renting out reasoning. If open ecosystems keep compounding, AI looks more like Linux or Android: messy, fragmented, but ultimately controlled by the many, not the few.

Humanity's Last Prompt? The Future of Work Redefined

Humanity’s Last Prompt Engineering Guide, which Berman plugs like a survival manual, reads less like a cheat sheet and more like a job description for the next decade: you won’t be the person doing the work, you’ll be the person telling an army of AI agents what to do. Prompting stops being a parlor trick and becomes management science, closer to writing a product spec or legal brief than chatting with a bot.

Workflows that used to be linear already fragment into agent swarms. A marketer doesn’t “make a campaign” anymore; they orchestrate a stack of agents that: - Scrape competitors - Generate copy variants - Auto-run A/B tests in Amplitude - Ship assets into ad platforms

In that world, job titles quietly mutate. A “senior analyst” starts to look like an AI team lead, supervising 10–50 agents, checking edge cases, and setting guardrails rather than building dashboards line by line. Developers shift from hand-writing boilerplate to designing agentic systems that call APIs, monitor logs, and self-heal.

Interfaces also move past the browser tab. Conversational UIs already sit in Slack, Notion, and Figma; by 2026, Berman’s audience expects AI-native surfaces like Gemini-style smart glasses, Meta’s Limitless-inspired wearables, and context-aware earbuds that listen to meetings and whisper suggested actions. Your “computer” becomes a mesh of sensors, cameras, and mics feeding a persistent agent that knows your calendar, codebase, and contracts.

That changes what “productivity software” even means. Instead of apps, people subscribe to vertical AI crews: a finance pod that closes the books, a sales pod that prioritizes leads, a compliance pod that preps Vanta audits. Human work shifts to adjudicating tradeoffs: cost vs. accuracy, risk vs. speed, privacy vs. personalization.

The doom narrative—AI as a pure job destroyer—misses this reallocation. Berman’s guests repeatedly frame AI as a force multiplier that creates new productivity categories: one person running what used to be a 20-person back office, or a solo Co-Founder spinning up an entire go-to-market machine in a weekend. The hard part won’t be finding tasks for AI; it will be upskilling humans fast enough to manage what they’ve suddenly become capable of.

The Infrastructure War You Don't See

Power, not prompts, decides who actually ships AI. Underneath every agent demo and GPT upgrade sits a brutal infrastructure race that Forward Future regulars talk about constantly but consumers rarely see. GPUs, datacenters, and fiber links now function as the real API limits.

US policymakers finally treat semiconductor capacity as national security. The CHIPS and Science Act earmarks over $52 billion to pull advanced fabs back onshore, with TSMC, Intel, and Samsung building or expanding plants in Arizona, Ohio, and Texas. Whoever controls sub-3 nm production controls how fast the next generation of models can train.

That shift turns US-based fabs into geopolitical choke points. Export controls already restrict high-end NVIDIA H100 and B200 shipments to China, fragmenting the global AI stack. Rival blocs now race to secure their own design talent, lithography tools, and packaging capacity to avoid sitting on the wrong side of a supply cutoff.

Silicon alone does not win this war; people do. Top AI researchers and infrastructure engineers command compensation packages that routinely cross $1–3 million per year in cash and equity. OpenAI, Google DeepMind, Meta, and a swarm of well-funded startups quietly poach from each other every quarter, and a single defection can tilt an entire product roadmap.

That talent gap shows up in how quickly teams can exploit new hardware. Training a frontier model now requires hundreds of thousands of accelerators, orchestration software that does not implode at 10,000+ nodes, and engineers who can shave percentage points off utilization losses. Companies that cannot hire those specialists end up renting capacity from those that can.

Groq illustrates the pressure on infrastructure natives. Its LPU-based inference clusters deliver jaw-dropping token throughput, but demand from agent platforms and enterprise copilots already pushes waitlists and regional capacity ceilings. When a single viral app can spike usage by 10–20x overnight, even well-capitalized providers scramble to add racks, power, and cooling.

Forward Future guests frame this as a new kind of platform lock-in. Builders who want to understand where the real constraints sit—chips, power, bandwidth, or brains—end up tracking infra news as closely as model releases, often starting with hubs like **Forward Future by Matthew Berman**.

Your 2026 Action Plan: What Builders Do Next

Momentum now favors people who can ship AI products, not just talk about them. Over the next 12–18 months, builders need to treat agents, data, and security as first-class product features, not afterthoughts. That means moving from “we added an LLM” to “our entire workflow runs on autonomous agents that are observable, testable, and accountable.**

Start with agentic workflows. Tools like Glif point to a near-term default where products orchestrate fleets of specialized agents: one for research, one for execution, one for QA. Developers should prototype narrow, high-ROI flows—sales outreach, support triage, internal ops—then wire agents together with clear handoffs and human override.

Enterprise demand is exploding around platforms like OpenAI, but enterprises buy reliability, not demos. Builders need to design for SLAs, auditability, and vendor redundancy across OpenAI, Anthropic, and open models. That includes explicit model-switching strategies, latency budgets, and cost controls baked into architecture, not tacked on in procurement.

Security now decides who gets deployed at scale. Vanta’s message is blunt: AI without governance becomes an unmonitored data exfiltration engine. Teams should: - Classify data before it ever hits a model - Log every prompt and response touching sensitive systems - Map AI use to SOC 2, ISO 27001, and sector rules from day one

Data strategy separates toys from durable companies. Spenser Skates’ world at Amplitude revolves around instrumenting every AI interaction: prompts, responses, user edits, downstream actions. Builders should treat AI features like any growth experiment—A/B test prompts, measure retention impact, and kill what doesn’t move core metrics.

No one needs to do this in isolation. Matthew Berman’s Forward Future ecosystem—newsletter, tools directory, and community—functions as a real-time map of what’s working across 500K+ builders. Use it to benchmark stacks, discover agents-first tools, and track shifting best practices.

Next year and a half sets the permanent hierarchy of AI winners. Hardware buildouts, model capabilities, and enterprise contracts all harden by 2026. Anyone who wants a foothold in the AI economy needs to be shipping agents, securing data, and measuring real impact—now, not in the next funding cycle.

Frequently Asked Questions

What is Glif and what did its co-founder discuss?

Glif is a platform for creating and using AI agents. Co-founder Fabian Stelzer discussed the shift from single-prompt AI tools to complex, interconnected agentic workflows that will automate entire processes for developers and businesses.

How is Vanta addressing the security risks of AI?

Vanta, represented by its CEO and CPO, focuses on trust and compliance for AI systems. They highlighted the urgent need for robust security frameworks to manage risks as enterprises rapidly adopt AI, especially with evolving regulations.

What were the key takeaways from the OpenAI researcher?

OpenAI researcher Tejal Patwardhan likely provided insights into the massive surge in enterprise AI adoption. The discussion pointed towards future model developments beyond GPT-5, including self-improving AI and aggressive timelines for AI-generated knowledge.

Why is data analytics important for the future of AI?

Spenser Skates, CEO of Amplitude, emphasized that data analytics is crucial for understanding real-world AI user reception and ROI. Data helps companies measure the actual impact of AI features and guides future product development.