The Future of AI: What Is Coming Next

Looking Ahead

Predicting the future of a fast-moving technology field is difficult, and anyone who claims certainty about where AI will be in five or ten years should be viewed sceptically. What we can do is identify the trajectories that are already under way, understand the forces driving them, and think carefully about their implications.

Several themes are shaping the near-term future of AI. Models are becoming multimodal, able to process and generate text, images, audio, and video in an integrated way. Systems are gaining the ability to take actions in the world through tools and external APIs, moving from passive responders to active agents. Reasoning capabilities are improving dramatically, with dedicated architectures trained specifically to think through problems step by step. The ecosystem is splitting along an open versus closed axis. And the field is grappling seriously with safety and alignment as capabilities advance.

This final lesson surveys each of these areas, grounding the discussion in research and systems that already exist, while being honest about what remains uncertain.

Multimodal AI

For most of deep learning's history, models were trained on a single modality: text, images, or audio. A language model understood text but was blind to images. A vision model could classify photographs but could not answer questions about them. Multimodal AI breaks down these barriers, enabling a single model to reason across different types of input.

From Specialist to Generalist

The progression has been rapid. CLIP (Contrastive Language-Image Pre-training, Radford et al., OpenAI 2021) showed that a model trained on 400 million image-text pairs from the internet could learn a shared embedding space for vision and language. This meant you could find images using natural language queries and vice versa, without any task-specific fine-tuning.

GPT-4V (OpenAI 2023) demonstrated that a large language model could accept images as input and reason about them with the same fluency it applied to text. Gemini 1.5 (Google DeepMind 2024) extended this to video, audio, and documents, with a context window of one million tokens capable of processing an entire feature film. The underlying architecture fuses different modalities through shared transformer layers and cross-attention mechanisms.

Diffusion Models

Text-to-image generation is powered by a separate class of model from transformers: diffusion models. The core idea (Sohl-Dickstein et al. 2015; Ho et al. 2020) is to train a neural network to reverse a gradual noising process. Starting from pure noise, the model iteratively denoises the image, guided at each step by a text prompt embedded through a language model. Stable Diffusion (Rombach et al., Stability AI 2022) introduced latent diffusion, which performs the denoising in a compressed latent space rather than pixel space, making generation dramatically faster.

DALL-E 3 (OpenAI 2023) further integrated language understanding by training the image generator to condition on detailed captions generated by GPT-4. This made prompt-following far more reliable and reduced the need for the elaborate prompt engineering that earlier text-to-image systems required.

AI Agents

A language model in isolation can answer questions and generate text, but it cannot take actions. It cannot search the web for current information, execute code, send an email, interact with a website, or query a database. AI agents extend language models with the ability to use tools and act in the world across a sequence of steps.

What Makes an Agent?

An agent has three components beyond a base language model: tools it can invoke (functions, APIs, browsers, code interpreters), a planning loop that decides which tool to use and when, and memory that preserves state across multiple steps. The ReAct pattern (Yao et al. 2022) interleaves reasoning and acting: at each step, the model reasons about what it knows and what it needs, selects a tool, observes the result, and reasons again. This loop continues until the task is complete.

Function Calling

Modern LLM APIs expose agents through function calling (also called tool use). You define a set of functions with JSON schemas describing their names, parameters, and descriptions. The model decides when to call a function, generates the arguments, and you execute the function in your code and return the result. The model incorporates the result and continues reasoning.

from openai import OpenAI
import json

client = OpenAI()

# Define the tools (functions) available to the model
tools = [
    {
        "type": "function",
        "function": {
            "name": "get_weather",
            "description": "Returns current weather for a given city",
            "parameters": {
                "type": "object",
                "properties": {
                    "city": {"type": "string", "description": "City name, e.g. 'London'"}
                },
                "required": ["city"]
            }
        }
    }
]

messages = [{"role": "user", "content": "What is the weather like in Accra right now?"}]

# First call: model decides to call the tool
response = client.chat.completions.create(
    model="gpt-4o-mini", messages=messages, tools=tools
)

tool_call = response.choices[0].message.tool_calls[0]
args = json.loads(tool_call.function.arguments)
print(f"Model wants to call: {tool_call.function.name}({args})")

# Execute the real function and return the result to the model
# (in a real system, this would call a weather API)
weather_result = '{"temperature": 31, "condition": "Sunny", "humidity": 72}'

messages += [response.choices[0].message,
            {"role": "tool", "tool_call_id": tool_call.id, "content": weather_result}]

# Second call: model now answers the user using the tool result
final = client.chat.completions.create(model="gpt-4o-mini", messages=messages)
print(final.choices[0].message.content)

Reasoning Models

Standard language models generate responses token by token in a single forward pass. For complex problems requiring multi-step logical deduction, mathematical proof, or careful planning, this is limiting. The model has, in effect, a fixed amount of "thinking time" proportional to the length of the answer.

Reasoning models are trained specifically to think before they answer, generating extensive internal reasoning traces (sometimes called "thinking tokens") before producing a final response. This gives the model time to explore multiple paths, backtrack when an approach fails, and verify its own conclusions.

OpenAI o1 and Beyond

OpenAI's o1 model (released September 2024) was the first widely available reasoning model. It was trained using reinforcement learning to produce internal chain-of-thought reasoning that is longer and more structured than the chain-of-thought prompting technique covered in Lesson 5.3. On the AIME 2024 mathematics competition, o1 scored 74% compared to 13% for GPT-4o. On the International Mathematics Olympiad qualification exam, it scored 83%. These results represent a qualitative jump in formal reasoning capability.

DeepSeek-R1 (DeepSeek AI, January 2025) replicated and in some benchmarks exceeded o1's reasoning performance while being open-weight and at a fraction of the training cost. This was a notable demonstration that state-of-the-art reasoning capability was not restricted to the largest, most resource-rich labs. OpenAI o3 (December 2024 preview) set a new record on ARC-AGI, a benchmark specifically designed to resist pattern matching and require genuine generalisation.

How Reasoning Models Are Trained

The key insight is to reward the quality of the final answer rather than supervising the intermediate steps. The model is trained with reinforcement learning where it receives a reward signal based on whether its final answer is correct. Over many training iterations, the model discovers that producing a long, structured thinking process before answering leads to better final answers and higher rewards. This self-discovered reasoning is in many ways richer than the hand-crafted chain-of-thought prompting strategies described in Lesson 5.3.

Open vs Closed Models

A significant structural divide has emerged in the AI landscape between closed models accessed only through proprietary APIs and open-weight models whose parameters are publicly released and can be run locally.

The Efficiency Frontier

For most of 2023, the gap between the best closed models and the best open models was substantial. That gap has narrowed significantly. Llama 3.1 405B (Meta, July 2024) matched GPT-4 on several benchmarks. Mistral Large 2 competed with GPT-4o on coding tasks. More importantly, smaller open models have improved dramatically: Phi-3-mini (Microsoft 2024, 3.8B parameters) achieved performance comparable to GPT-3.5 on many tasks while fitting on a mobile device.

Parameter-efficient fine-tuning techniques, particularly LoRA (Low-Rank Adaptation, Hu et al. 2021) and its variants, make it possible to adapt open-weight models to specific tasks with modest GPU resources. A 7B model fine-tuned on domain-specific data can outperform a 70B general model on that domain.

AI Safety and Alignment

As AI systems become more capable, the question of whether they will behave as intended across all circumstances becomes increasingly consequential. AI safety is the research field concerned with ensuring that AI systems are reliable, controllable, and aligned with human values even as they become more powerful.

The Alignment Problem

A system is aligned when it reliably pursues the goals its designers intend, including in situations not explicitly anticipated during training. Alignment is hard because it is difficult to fully specify what we want, training objectives are imperfect proxies for our actual goals, and distributional shift means models encounter situations in deployment that were not present in training.

RLHF, covered in Lesson 5.2, is currently the primary alignment technique in production systems. It is effective but has known limitations: the reward model that guides RL training can itself be gamed (reward hacking), and aligning to the preferences of a small pool of labellers may not generalise to all users and contexts.

Current Safety Approaches

Mechanistic Interpretability

A growing research direction called mechanistic interpretability attempts to reverse-engineer the internal workings of neural networks, identifying the circuits (groups of neurons and attention heads) responsible for specific capabilities. Anthropic's research in this area has identified features corresponding to concepts like "the Golden Gate Bridge" or "DNA" in Claude's internal activations, and has found evidence of deliberate planning in the residual stream of transformer models. This work is foundational for building AI systems we can genuinely inspect and verify, rather than only test empirically.

Economic and Societal Impact

The economic effects of AI are already measurable and will intensify. Understanding them is important both for making career decisions and for thinking about the policy frameworks needed to manage the transition well.

Productivity and Labour

Studies of AI tools in professional settings show consistent productivity gains for workers who adopt them. A GitHub Copilot study (Peng et al. 2023) found that developers using AI code completion completed tasks 55% faster. A study of customer service agents using AI assistance (Brynjolfsson, Li, and Raymond 2023) found a 14% increase in resolutions per hour, with the largest gains for less experienced workers. A randomised trial of consultants using GPT-4 (Noy and Zhang, 2023) found AI users completed tasks 25% faster with 40% higher quality ratings.

These productivity gains are real, but they are not evenly distributed. The tasks most easily automated tend to be the structured, routine parts of knowledge work: first drafts of reports, boilerplate code, straightforward customer queries, and data extraction. The tasks that remain require judgment, relationship-building, ethical reasoning, novel problem-solving, and contextual expertise. This shifts the premium from information access and execution speed toward these harder-to-automate qualities.

The Education and Credentialling Challenge

AI significantly changes what skill gaps matter and what skills are worth developing. The ability to use AI tools effectively is becoming as fundamental as the ability to use a search engine. At the same time, the deep domain knowledge that allows you to evaluate AI outputs, catch errors, and direct AI effectively remains valuable precisely because it is not easily automated. The people best positioned in an AI-integrated world are those who combine domain expertise with practical AI literacy.

Geopolitical Dimensions

AI capability has become a matter of national strategic interest. The United States, China, and the European Union are all pursuing distinct approaches to AI development and governance. US export controls on advanced semiconductors (Nvidia A100 and H100 chips, and subsequent generations) are an attempt to maintain an advantage in AI training capacity. China's domestic AI investment includes state-sponsored labs and significant open-weight contributions (DeepSeek, Qwen). The EU AI Act represents the first comprehensive regulatory framework for AI systems globally, likely to influence AI governance internationally.

What You Should Do Next

You have now completed the entire AI Crash Course. You have built a conceptual map of the field from the mathematics of a single neuron through to the frontier of reasoning models and AI agents. The question now is how to keep building on this foundation.

Key Takeaways

• Multimodal AI integrates text, images, audio, and video in unified models. CLIP, GPT-4V, and Gemini represent a shift from specialist to generalist systems. Diffusion models (Stable Diffusion, DALL-E 3) power text-to-image generation through iterative denoising.
• AI agents extend language models with tools, planning loops, and memory, enabling multi-step task completion. Function calling (tool use) is the API mechanism that makes agents practical. Frameworks like LangChain, LlamaIndex, and AutoGen reduce the boilerplate of building agent systems.
• Reasoning models (OpenAI o1, o3; DeepSeek-R1) are trained with reinforcement learning to generate extensive internal thinking before answering. They show qualitative improvements on formal reasoning tasks but cost more per call.
• Open-weight models (Llama 3, Mistral, Phi-3) have closed the gap with frontier closed models significantly. LoRA fine-tuning makes domain adaptation practical on consumer hardware. The right choice between open and closed models depends on privacy requirements, regulatory constraints, and resource availability.
• AI alignment remains an unsolved research problem. Current approaches (RLHF, Constitutional AI, DPO, red-teaming, interpretability) each address part of the problem. Mechanistic interpretability is a promising long-term direction for understanding model behaviour from first principles.
• Measured productivity gains from AI in professional settings are real and consistent across studies. The premium shifts toward judgment, domain expertise, and the ability to evaluate and direct AI outputs effectively.
• The best position in an AI-integrated world combines deep domain knowledge with practical AI literacy. Build things, engage with research, and specialise deliberately.